Meteorology doi: 10.3390/meteorology5030027

Authors: Gunavathi Sundaram Selvakumar Radhakrishnan

Climate change has significantly influenced regional precipitation patterns, affecting agricultural productivity and water-resource sustainability in monsoon-dependent regions. Reliable climate projections are therefore essential for climate impact assessment and adaptation planning. However, the performance of Regional Climate Models (RCMs) varies considerably across regions and climatic conditions, necessitating rigorous evaluation before their application in local-scale studies. In this study, eight CORDEX-CORE South Asia (0.22°) RCMs were evaluated for their ability to reproduce historical precipitation over Thanjavur district, Tamil Nadu, India, during 1976–2005 using observed rainfall data from 13 rain gauge stations. Model performance was assessed using seven statistical metrics integrated through the Compromise Programming Index (CPI), together with evaluations of precipitation occurrence, rainfall intensity occurrence, and cumulative precipitation distribution. The results revealed considerable variability among the RCMs. RegCM-based models generally exhibited wet biases, whereas REMO and CCLIM tended to underestimate seasonal rainfall. Based on the selected metrics and CPI framework, NorESM–RegCM, NorESM-CCLIM, and NorESM-REMO demonstrated comparatively better overall performance. However, analyses of precipitation occurrence and distribution showed that model performance depended on the precipitation characteristic considered, with no single RCM consistently outperforming the others across all evaluation criteria. These findings demonstrate that a multi-criteria evaluation framework provides a more comprehensive assessment of RCM performance than conventional statistical metrics alone and offers a robust basis for selecting suitable RCMs for future climate projections and agro-hydrological applications in the Cauvery Delta region.

Meteorology doi: 10.3390/meteorology5030026

Authors: Wei Li Linlin Cui Jun Wang Fanglin Yang Jongil Han

Accurate forecasting of Convective Available Potential Energy (CAPE) is critical for severe weather prediction. However, the operational GFS model exhibits a persistent low-CAPE bias. In this work, we apply a two-step regression–diffusion model (NVIDIA CorrDiff) to address this issue. Our results indicate that while a standard U-Net can successfully reduce the bulk systematic bias, the generated output remains overly smoothed. This occurs because, for data with long-tailed statistical distributions such as CAPE, standard models trained on mean squared error fail to capture rare, high-magnitude events. In contrast, generative diffusion models can reproduce realistic, small-scale features similar to the ground truth by learning to reverse a noise-corruption process through a series of iterative denoising steps. Our study begins with bias correction for the 24 h forecast. We then extend this by applying the model—trained solely on 24 h data—to correct forecasts of up to 120 h. This strategy leverages our finding that the GFS forecast bias is highly persistent over time. Furthermore, our examination of CAPE’s joint Probability Density Functions emphasizes the necessity of matching machine learning models to the target variable’s statistical properties. Ultimately, the effectiveness of CorrDiff highlights its potential for other challenging applications involving small-scale phenomena with long-tailed distributions.

Meteorology doi: 10.3390/meteorology5030025

Authors: Francesca Becherini Claudio Stefanini Antonio della Valle Dario Camuffo

The recovery of measurements from the early instrumental period is crucial for improving the quality of historical climate reconstructions, enhancing model calibration, and increasing the reliability of long-term predictions. The study aims to recover, correct, and analyze the daily pressure observations made in Bologna by Jacopo Bartolomeo Beccari and his pupils from 1715 to 1774. The empirical quantile mapping at monthly level using the Padua series as a reference resulted in the most reasonable correction method. The quality of the reconstruction was assessed through a posteriori data analysis at monthly, seasonal, and annual time scales. The reconstructed Bologna series shows general agreement with the contemporary Padua series, in particular in the day-by-day variability and percentile distribution. Moreover, its main features reflect those of the 1961–1990 reference period. The annual cycle is also consistent with the Milan and Venice series for the overlapping periods. The notable difference between the 18th-century pressure observations in Northern Italy and the paleo-reanalysis dataset ModE-RA (Modern Era Reanalysis) is likely due to the fact that instrumental measurements are only assimilated from the 18th century onward. The paper provides a new 18th-century daily dataset and is part of a broader effort aimed at reconstructing the Bologna pressure series up to the present day.

Meteorology doi: 10.3390/meteorology5030024

Authors: James E. Overland

Arctic temperatures are rising faster than global temperatures. They show regionality that impacts specific extreme events. The previous five years favor three large societal im-pacts: Canadian wildfires with extensive air pollution, disastrous landfalling storms in Western Alaska with high waves and storm surge, and Arctic/midlatitude weather connections impacting regions with large populations and expanding infrastructure expo-sure. Taken together, such rare extremes form a collective indicator of Arctic change. Rather than a focus on trends, extremes are single occurrences that amplify typical weather events. Temperature maximums and dryness related to stationary geopotential heights in Eurasia and northwestern Canada add persistent forces to co-located wildfires. Recent warm sea temperatures in the western Bering Sea helped fuel two re-energized remnant typhoons on a northeast trajectory toward Alaska. Cold-air outbreaks continue to penetrate south into midlatitude regions ill-prepared to deal with frigid temperatures—such as Florida, Texas, and east Asia. It is important to connect extreme Arctic weather hazards to the magnitude of their societal impact. Impacts are an overlap of weather hazards, societal vulnerability, and exposure.

Meteorology doi: 10.3390/meteorology5030023

Authors: Juan A. Rivera Georgina Marianetti

Southern South America encompasses temperate forests, steppe ecosystems, peatlands, fjords, and marine environments that support unique biodiversity and human communities, containing one of the world’s largest freshwater reserves in the Patagonian Icefields. Its relevance makes climate change a major threat to both natural ecosystems and human populations. Nevertheless, future changes in climate extremes, considering the complex topographic patterns that shape regional climatic patterns, remain uncertain. This study used an ensemble of regional climate models to assess future changes in a set of 9 temperature and precipitation extreme indices under two emissions scenarios (RCP4.5 and RCP8.5) and three future time horizons (2026–2045; 2051–2070 and 2081–2100). Our results reveal a robust intensification of temperature extremes, with minimum temperature extremes projected to increase by more than 7 °C under the high-emissions scenario by the end of the century, accompanied by increases exceeding 30 summer days in the northeastern portion of the region. In contrast, precipitation extremes exhibit larger spatial variability and uncertainty, although robust reductions in the frequency of days with precipitation exceeding 20 mm partially explain the projected drying trend, while increases in 1- and 5-day maximum precipitation indicate more intense heavy rainfall events across the Patagonian steppe. These results have clear implications for the cryosphere and the biodiversity of the region.

Meteorology doi: 10.3390/meteorology5030022

Authors: Callum Cording Harry West

The combined influence of the NAO and EA on monthly precipitation across mainland Spain remains insufficiently understood despite their recognized importance for European climate variability. This study investigates how individual and combined NAO and EA phases influence monthly precipitation patterns across mainland Spain using the MOPREDAScentury dataset between January 1950 and December 2020. Spatial correlation analyses and precipitation deviation mapping were used to identify regional and seasonal precipitation responses associated with each teleconnection phase. The results demonstrate that combined NAO/EA phases produce distinct precipitation signatures that vary considerably throughout the year, with the strongest and most spatially coherent relationships occurring during winter. Concurrent NAO−/EA −phases were associated with significantly wetter conditions, whereas NAO+/EA− phases generally produced drier conditions across much of mainland Spain. The influence of the NAO weakened through summer, while the EA became a more important control on precipitation variability. These findings demonstrate that considering the NAO and EA together provides a more comprehensive understanding of precipitation variability than analyzing either teleconnection independently, with potential applications for seasonal forecasting, drought preparedness and water resource management.

Meteorology doi: 10.3390/meteorology5030021

Authors: Helga Tóth Balázs Szintai Hajnalka Breuer

This study investigates the impact of assimilating Advanced Scatterometer (ASCAT) Level-2 surface soil moisture retrievals into the Application of Research to Operations at Mesoscale (AROME) model, the operational numerical weather prediction system of the Hungarian Meteorological Service. The Level-2 retrievals are geophysical soil moisture estimates derived from satellite radar backscatter observations and represent the uppermost soil layer (approximately 0–5 cm). Data assimilation is performed using a Simplified Extended Kalman Filter (SEKF) within the SURFEX surface modeling platform. In the reference configuration (REF), the same SEKF framework is applied, as used operationally for the assimilation of 2 m temperature and relative humidity observations. A second experiment (ASCAT) extends this configuration by additionally assimilating ASCAT surface soil moisture retrievals. The experimental period covers May–October 2023. The objective of the study is to quantify the added value of ASCAT soil moisture assimilation relative to the REF experiment, which does not assimilate ASCAT retrievals. Results indicate a systematic improvement in root-zone soil moisture and soil temperature, suggesting that the assimilation of surface soil moisture observations propagates beneficially to deeper soil layers. Verification against in situ and model-derived diagnostics shows a positive impact on near-surface atmospheric variables, particularly for 2 m temperature and humidity during nighttime conditions. Furthermore, precipitation verification reveals a measurable improvement, suggesting a beneficial influence of improved land–atmosphere coupling on short-range forecasts.

Meteorology doi: 10.3390/meteorology5030020

Authors: Meriem Ouattab Hicham Charifi Rachid Moustabchir Albin Ullmann Pascal Roucou Fouad Gadouali

Morocco, located at the southern margin of the Mediterranean climate-change hotspot, is exposed to a rapidly evolving precipitation regime whose national-scale characterization remains incomplete. This study delivers an integrated assessment of the spatio-temporal variability and trends of precipitation and its extremes over the country during the most recent World Meteorological Organization (WMO) climate-normal period (1991–2020), based on daily observations from 31 synoptic stations operated by the Direction Générale de la Météorologie (DGM). Trends in annual, seasonal and monthly precipitation were quantified using the non-parametric Mann–Kendall test combined with Sen’s slope estimator, while the structural transformation of the rainfall regime was characterized through three indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI): the Consecutive Dry Days (CDDs), the Simple Daily Intensity Index (SDII) and the amount of precipitation from very wet days (R95pTOT). The results reveal an apparent tendency toward a negative trend, with a predominance of negative precipitation trends in winter and early spring, most pronounced in February, that reach statistical significance at only a limited number of stations, partly offset by a spatially coherent wetting in November over central and eastern Morocco. The joint analysis of the three ETCCDI indices indicates a north–south contrasted reorganization: northern stations exhibit longer dry spells coexisting with intensified extreme rainfall, whereas southern stations show a generalized weakening of both intensity and extremes. These findings point to a structural shift toward more episodic and contrasted precipitation regimes, with the wet season starting later, ending earlier and concentrating rainfall into fewer but more intense events. The analysis provides an updated observational baseline for the validation of CMIP6 based regional projections and for the design of climate-resilient water and agricultural strategies in Morocco.

Meteorology doi: 10.3390/meteorology5030019

Authors: Andreas Matzarakis

Heat and ultraviolet (UV) radiation are among the most consequential environmental stressors intensified by anthropogenic climate change [...]

Meteorology doi: 10.3390/meteorology5030018

Authors: José Luis Argain

This study introduces a novel empirical model to estimate the total pressure drag generated by trapped lee waves (TLW) and upward-propagating internal waves in moderate-to-strong non-hydrostatic, stratified flow over a mountain ridge, as a function of flow non-linearity. The core framework is based on a two-layer atmosphere characterized by a piecewise-constant Scorer parameter, l, where a lower layer of constant l1 underlies an upper layer with l2<l1. This framework incorporates key features to extend beyond idealized assumptions, providing a reliable tool for predicting non-linear flow regimes over mountainous terrain, particularly those featuring realistic vertical profiles of the Scorer parameter. To develop the empirical formulation, a micro- to mesoscale numerical model is employed to simulate realistic, non-linear flows over steep topography. The proposed empirical model yields results that compare favorably with numerical simulations across a range of moderate-to-strong non-hydrostatic regimes, including complex cases derived from observational data and realistic vertical profiles of the Scorer parameter. The model demonstrates robust performance ranging from strongly to moderately non-hydrostatic regimes (the latter corresponding to dimensionless half-widths of approximately 5), and provides accurate drag estimates for non-linearities up to a dimensionless mountain height of approximately unity. Therefore, this empirical approach serves as a valuable foundation for improving drag parameterizations in weather prediction models, offering a computationally efficient alternative to high-resolution numerical downscaling over steep terrain.

Meteorology doi: 10.3390/meteorology5030017

Authors: Md Rafiqul Islam Scott C. Sheridan

This study applies a synoptic seasonal climatological framework, extended vertically through the troposphere, to investigate the South Asian monsoon using daily mean data (1948–2024) from the NCEP–NCAR Reanalysis. A seasonal synoptic circulation framework was developed using self-organizing maps (SOMs) to classify four distinct seasons—winter, pre-monsoon, monsoon, and post-monsoon—and their transitional phases. Diagnostics including temperature and moisture advection and vertically integrated moisture transport (VIMT) were incorporated to examine circulation–environment interactions. The results highlight the pre-monsoon-to-monsoon transition as the most critical seasonal shift, marked by rapid land heating, steep pressure gradients, and northward ITCZ migration that initiates southwesterly monsoon winds. Classical land–sea thermal contrasts initiate the low-level monsoon wind reversal, while vertical circulation assessment suggests that mid- to upper-tropospheric thermal gradients, supported by latent heating and Hadley-type overturning, help organize and sustain monsoon circulation strength. Additionally, South Asian monsoon circulation is shifting from well-defined seasonal regimes toward more transitional states. The results reveal widespread warming, weakened VIMT during major monsoon-related phases, and uneven moisture redistribution, suggesting that climate change is reshaping the monsoon seasonal cycle through both thermodynamic and circulation-driven processes. Taken together, the findings demonstrate that monsoon dynamics arise not from a single mechanism but from interconnected processes operating across atmospheric layers. This vertically integrated synoptic circulation approach thus provides a more comprehensive framework for understanding monsoon processes.

Meteorology doi: 10.3390/meteorology5020016

Authors: Julius Olaniyan Deborah Olaniyan Ibidun C. Obagbuwa Madison N. Ngafeeson

Floods remain among the most devastating natural disasters globally, disproportionately impacting low-resource regions where real-time flood forecasting is constrained by limited computational infrastructure and the scarcity of fine-resolution predictive models. Although state-of-the-art global climate models achieve high predictive accuracy, their scale and computational complexity restrict their applicability in localized and resource-constrained settings. This study proposes a deep climate model distillation framework that transfers knowledge from a high-capacity Fourier Neural Operator (FNO)-based global climate model inspired by FourCastNet into lightweight, regionally adaptive student networks suitable for edge deployment. The framework combines climate variables, satellite observations, and hydrological measurements to improve localized flood prediction. Knowledge transfer is achieved through a multi-objective distillation strategy that combines supervised learning, soft-target alignment, and intermediate feature matching. Experimental evaluation across multiple flood-prone regions in Sub-Saharan Africa and South Asia shows that the distilled student model achieves an average classification accuracy of 0.89, an AUC of 0.91, and an F1-score of 0.88, retaining approximately 96.7% of the teacher model’s predictive performance. In continuous discharge estimation, the model attains a mean absolute error of 0.17, RMSE of 0.24, and an R2 score of 0.85. The proposed distillation approach yields an 8× reduction in inference latency and over a 20× reduction in model size, enabling real-time execution on low-power edge devices such as the Raspberry Pi 4 and NVIDIA Jetson Nano. The student model further demonstrates robust regional and temporal generalization, with limited performance degradation in unseen geographic areas and during extreme flood years.

Meteorology doi: 10.3390/meteorology5020015

Authors: Shailendra Kumar

The present study investigates the statistical distribution of radar reflectivity slopes [S-Ze] in the lower troposphere along the west coast of India using a C-band radar during the pre-monsoon and monsoon seasons in 2024. The study period spans a range of meteorological conditions, from a drier atmosphere during pre-monsoon months to a moist atmosphere during the monsoon months, with varying updraughts and downdraughts. To investigate the S-Ze, we calculated the difference in Ze between 4 km and 2 km altitudes in the lower troposphere. The S-Ze could be either positive or negative, where, in a positive [negative] S-Ze, the Ze decreases [increases] towards the surface. The monthly variations in S-Ze from the pre-monsoon to monsoon months are observed in the lower troposphere and are higher in monsoon months compared to pre-monsoon months, which are too near the coast. The land–ocean contrasts of the vertical profiles contributing to +ve and −ve S-Ze are lower compared to north–south gradients and higher in monsoon months. The average S-Ze shows the highest +ve and −ve S-Ze magnitude near the coast among all the months. The highest magnitude in S-Ze is observed in March and April and is associated with the lower and higher numbers of vertical Ze profiles. The increase or decrease in hydrometeor size is less during the monsoon months (June, July, August, and September) compared to pre-monsoon months, where the March–April months have the highest increase or decrease in the hydrometeor’s size in the lower troposphere. The variations in the S-Ze are the combined effect of the atmospheric, thermodynamic (relative humidity (RH) and moisture flux), and dynamic conditions (zonal, meridional, and vertical velocity). Strong updraughts that carry RH to higher altitudes make the lower atmosphere drier and contribute to a +ve S-Ze; Ze tends to decrease in the lower troposphere. However, a weaker updraught or a moderate downdraught with sufficient RH provides sufficient time for hydrometeors to grow and contributes to −ve S-Ze, and Ze tends to increase in the lower troposphere. For example, in March and April, the atmosphere is dry, and we observe the largest decrease in hydrometeors near the coastal boundary. However, we also see significantly higher negative radar reflectivity slopes, and weak downdraughts provide enough time for hydrometeors to grow. In June and July, there are strong updraughts (downdraughts) with high (low) RH, making the atmosphere more conducive to a decreasing tendency in Ze and contributing to a higher fraction of +ve S-Ze. The results presented here would be an extension of the study from the satellite-based observations, revealing the extension of climatology for the inclusion of stratiform precipitation.

Meteorology doi: 10.3390/meteorology5020014

Authors: Letícia Stachelski Ronald Buss de Souza Gilberto Fisch Regiane Moura Breno Tramontini Steffen Luciano Ponzi Pezzi

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m−2) than for Hs (±5 W∙m−2). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m−2), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m−2) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m−2 for Hs and <7 W∙m−2 for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m−2 at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction.

Meteorology doi: 10.3390/meteorology5020013

Authors: Vladimir S. Kostsov Dmitry V. Ionov Maria V. Makarova

Continuous ground-based microwave (MW) measurements with the RPG-HATPRO radiometer at the observational site of St. Petersburg State University located near the coastline of the Gulf of Finland have provided a large amount of data on the cloud liquid water path (LWP) of non-raining clouds. The 12-year (2013–2024) time series of the LWP values has been analysed and the diurnal evolution of the LWP has been assessed for each month of the year. The calculations have been made for the LWP in the range 0–0.4 kg m−2 using different sampling subsets that include the so-called true and virtual LWP values. True LWP values correspond to measurements with clouds in the field of view of the radiometer, whereas virtual LWP values correspond to measurements with clouds or with clear sky in the field of view of the instrument and, therefore, virtual values can be zero (in clear sky cases). Based on the correlation analysis, time periods characterised by similar meteorological conditions and suitable for assessing the daily dynamics of LWP were identified. The LWP diurnal cycles in December, January, and February demonstrated a similar pattern with a maximum around local astronomical noon and with a minimum around midnight. For the remaining months except March and June, the maximum LWP is observed in the early morning and the minimum is observed in the afternoon. This cycle is characteristic of marine stratocumulus clouds. The diurnal cycles of the LWP in March and June, peaking in the afternoon and morning, respectively, are typical of convective continental clouds. Thus, the LWP diurnal cycle in the coastal zone of the Gulf of Finland may have characteristics of both marine and continental clouds. Parameters of the two-mode sinusoidal approximation of the diurnal cycle of the LWP in different seasons are presented.

Meteorology doi: 10.3390/meteorology5020012

Authors: Matheus José Gomes Juliana Aparecida Anochi Marília Harumi Shimizu

Seasonal precipitation forecasting remains challenging in regions with complex topography and high climatic variability, such as the state of Minas Gerais, Brazil. This study evaluates the performance of an Artificial Intelligence (AI)-based ensemble approach for seasonal precipitation prediction. The AI-based predictions are compared against outputs from multiple dynamical models, including those from the North American Multi-Model Ensemble (NMME) and the Copernicus Climate Data Store (CDS). The AI model was trained using high-resolution precipitation data from the Center for Weather Forecast and Climate Studies (CPTEC) dataset – MERGE-CPTEC – and subsequently applied to generate regional-scale seasonal forecasts. Model performance was assessed using Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Pearson Correlation (r). The results indicate that the AI-based forecasts achieve competitive performance relative to dynamical models across all seasons, exhibiting lower error metrics and improved representation of spatial precipitation patterns. The highest forecast skill was observed during winter (June-July-August, JJA), when atmospheric conditions are more stable, and precipitation variability is low. During the wet seasons (December-January-February, DJF and September-October-November, SON), despite increased convective activity and spatial heterogeneity, the AI model maintained greater spatial coherence and closer agreement with observations than the dynamical forecasts. Overall, the findings demonstrate that AI-based approaches represent a promising and computationally efficient complementary tool for regional-scale seasonal precipitation forecasting, particularly in climatically heterogeneous regions.

Meteorology doi: 10.3390/meteorology5020011

Authors: Aikaterini Lampraki Nikolaos A. Bakas

The response of temperature extremes to recent warming at the local scale remains uncertain because changes in mean temperature may be accompanied by changes in the shape of the temperature distribution. While higher mean temperatures generally lead to more frequent heat waves and fewer cold events, variations in higher-order statistical moments can either amplify or moderate these effects. This study examines how the probability distribution of 2 m temperature has evolved during the last 80 years in Greece using the ERA-5 reanalysis dataset. The evolution of the first four statistical moments (mean, standard deviation, skewness and kurtosis) and of the 5th and 95th percentiles of daily mean temperature is calculated by splitting the time series into eight decades, with each decade representing a separate climatology. A clear increase in mean temperature is observed across Greece. However, trends in the higher-order moments are more complex: the standard deviation and skewness exhibit positive and negative trends that depend on the region and the season, while kurtosis trends are weaker with a few regional exceptions. These changes alter the response of temperature extremes to warming, resulting in non-uniform shifts of the 5th and 95th percentiles. In mountainous regions, extreme cold events during winter and autumn have decreased more strongly than expected from mean warming alone, while in marine regions extreme warm events during summer and autumn have increased beyond what would be expected by a shift in the mean. In other areas, changes in the distribution shape lead to weaker extremes than those predicted by mean warming alone. These results highlight the role that changes in temperature variability have in modulating the evolution of temperature extremes under climate warming.

Meteorology doi: 10.3390/meteorology5020010

Authors: José J. Hernández Ayala Rafael Méndez-Tejeda Kyara V. Virella Carrión Jesús A. Hernández Londoño

Puerto Rico has experienced increasingly frequent and intense extreme heat conditions in recent years, with the 2023–2024 warm seasons standing out for prolonged periods of dangerously high heat index values and widespread spatial exposure. These conditions are particularly concerning in tropical island environments, where high humidity limits physiological cooling and amplifies heat-related health risks. The main objective of this study is to identify and characterize extreme heat zones and events across Puerto Rico using NOAA-modeled heat index (apparent temperature) data, as well as to examine their spatial and temporal variability during the 2021–2024 period. Hourly modeled apparent temperature data between 2 and 4 pm, representing the warmest time of day, were analyzed for each day from June through October. Mean maximum and maximum heat index surfaces were generated for each month and warm season, and extreme heat zones were identified using the 103 °F (39.4 °C) danger threshold. Results show a persistent concentration of extreme heat in low-elevation coastal regions, particularly across the northern coastal plains from San Juan to Hatillo, with floodplain areas in Arecibo and Manatí exhibiting the highest and most consistent exposure. August was identified as the month with the highest mean maximum heat index across all study years, followed by September. The warm seasons of 2023 and 2024 exhibited the highest magnitudes and spatial extents of extreme heat, with some regions experiencing apparent temperatures exceeding 110 °F and up to 141 extreme heat days during peak afternoon hours. The findings indicate a transition from localized heat hotspots to widespread and sustained extreme heat exposure across Puerto Rico’s coastal regions. This study provides an island-scale assessment of extreme heat patterns with direct implications for public health, infrastructure planning, and heat-risk management in a warming tropical climate.

Meteorology doi: 10.3390/meteorology5020009

Authors: Timothy W. Juliano Sue Ellen Haupt Eric A. Hendricks Branko Kosović Raghavendra Krishnamurthy

Coastally Trapped disturbances (CTDs) are shifts in wind direction from the pre-dominant direction to equatorward to poleward for a period of time. These CTDs occur during the warm season off the California coast and impact coastal weather conditions and planned offshore wind plants. This study assesses the characteristics of CTD events as observed by lidar and other offshore buoys, then evaluates the ability of modeling systems to capture the correct characteristics, leveraging model output from the High-Resolution Rapid Refresh (HRRR) operational modeling system and the NOW-23 (National Offshore Wind) model dataset. CTDs were analyzed for October 2020 and May through to October of 2021, identifying 18 unique CTD events, confirmed by a nearby National Data Buoy Center (NDBC) buoy. The HRRR model captured most of these events, but the NOW-23 model output contained only 12 events. Composites of the wind, temperature, and pressure perturbations pre-, during, and post-event demonstrated the diminishment in wind speed, particularly for the alongshore component. Although the NOW-23 model captured the alongshore wind component and pressure perturbations well, the cross-shore wind component and temperature perturbations varied substantially. When the turbulent kinetic energy deviation and wind shear was positive across all levels pre-event, the NOW-23 modeling system was less likely to capture the CTD event. In contrast, the events that were captured by the model tended to have negative wind shear aloft pre-event.

Meteorology doi: 10.3390/meteorology5020008

Authors: Mohima Sultana Mimi Md Jahangir Alam

The Atlantic Meridional Overturning Circulation (AMOC) is a key regulator of the global climate system, yet its influence on future precipitation remains uncertain because climate models project widely varying degrees of weakening. Here, we examine the relationship between AMOC decline and global precipitation using historical and RCP8.5 simulations from ten CMIP5 models. Models are grouped by the magnitude of projected AMOC weakening, and an intermodel regression framework is used to quantify the sensitivity of precipitation to changes in overturning strength. The CMIP5 multi-model mean reproduces observed large-scale precipitation patterns. While early-century responses are modest, stronger AMOC weakening by the late century is associated with pronounced drying across the tropical North Atlantic and enhanced rainfall over the Indo-Pacific. Regression analysis indicates that precipitation within the Intertropical Convergence Zone decreases by ~2.3% per 1 Sv reduction in AMOC strength. Sensitivity experiments further show that reduced Atlantic heat transport cools the North Atlantic and shifts tropical rainfall southward. These results identify AMOC variability as an important source of uncertainty in projections of future global hydroclimate.

Meteorology doi: 10.3390/meteorology5010007

Authors: Jessica Cagnoni Patrizio Ripesi Stefano Amendola Edoardo Bucchignani Myriam Montesarchio

Climate change (CC) is widely recognized as a major human concern, affecting society across all aspects and activities. Among various economic sectors, aviation is one of the most affected due to its exposure to adverse weather events. Consequently, adaptation and mitigation actions are becoming increasingly important to reduce the negative effects of CC-driven extreme weather events on aviation operations. In this study, we analyzed 30 years of historical aerodrome meteorological routine reports (METARs) from several major Italian airports to assess multi-decadal changes in aviation weather-related hazards, based on observational evidence such as convection, visibility, and snow and freezing precipitation. Furthermore, we examined the ERA5 reanalysis dataset to assess potential anomalies in the synoptic circulation over the Euro-Mediterranean region that may drive fluctuations in local airport climatology. Our results reveal relevant trends for the considered aviation-related weather hazards, while also indicating meaningful links to variations in local and synoptic patterns. The observed increases in 500 hPa geopotential height, 850 hPa temperature, and convective available potential energy (CAPE) lead to changes in the climatology of the airports considered, including a general enhancement of thermoconvective phenomena, a reduction in events associated with synoptic-scale disturbances, an overall decrease in snowfall, and contrasting trends in fog occurrence depending on local factors.

Meteorology doi: 10.3390/meteorology5010006

Authors: Jorge A. Amador Dayanna Arce-Fernández Tito Maldonado Erick R. Rivera

Propagating easterly waves (EW) are analyzed here, within the dynamical environment of the Caribbean Low-Level Jet (CLLJ) using radiosondes from the Organization of Tropical East Pacific Convection (OTREC) field campaign, ERA5 reanalysis, and lightning from the World Wide Lightning Location Network (WWLLN) over 5∘–20∘ N, 60∘–100∘ W during 21 August–30 September 2019. Radiosondes resolve the vertical structure of the waves at San Andrés (Colombia), Limón and Santa Cruz–Guanacaste (Costa Rica), while ERA5 provides spatial–temporal continuity and vertically integrated diagnostics—namely, the vertically integrated moisture flux divergence (VIMFD) and the vertically integrated geopotential flux divergence (VIGFD). Lightning from WWLLN and precipitation from ERA5 and the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement mission (GPM IMERG) offer independent convective proxies to track disturbances. Mean profiles from radiosondes and ERA5 show strong agreement at Limón and Guanacaste and some differences at San Andrés, yet all datasets capture coherent, phase-locked anomalies in zonal wind, meridional wind, temperature, humidity, vertical velocity and vorticity used to diagnose EW–CLLJ interactions. VIMFD, VIGFD, lightning and precipitation exhibit westward-propagating cores that align with the above anomalies, indicating that organized convection is coupled to the disturbances, whereas the mean state preconditions the environment to enable wave-induced upward motion. A robust vertical adjustment of the CLLJ is documented: the core shifts from near 925 hPa over the Caribbean Sea to about 700 hPa over the Eastern Tropical Pacific (Δp∼150 hPa). This feature is reproduced by a 30-year ERA5 climatology, consistent with jet-exit forcing and enhanced boundary-layer coupling over land. Conditions favorable for barotropic instability using the Rayleigh–Kuo criterion, were present over most of the period. A qualitative barotropic conversion proxy, computed from the eddy momentum covariance ⟨u′v′⟩, shows positive values in the lower troposphere at Guanacaste and in the layer 850–700 hPa at San Andrés, suggesting mean-to-eddy momentum transfer, whereas the signal at Limón is weaker. Together, these results provide a physically consistent view of EW–CLLJ interactions across the IAS; therefore, a schematic of those mechanisms is proposed here. The results highlight the need for high-resolution modeling and full energy-budget analyses.

Meteorology doi: 10.3390/meteorology5010005

Authors: Robert A. Weller Roger Lukas Sebastien P. Bigorre Albert J. Plueddemann James Potemra

An eighteen-year record of in situ surface meteorology and computed bulk air–sea fluxes of heat, freshwater, and momentum from an ocean site windward of the Hawaiian Islands is presented. Observations were logged every minute. The one-minute, one-hour, and one-day time series statistics are presented. The daily-averaged time series provide an overview of this trade wind site, with mean wind of 6.8 m s−1 toward the west–southwest, mean ocean heat gain of 23.2 W m−2, and freshwater loss of 1.2 m yr−1. Energetic variability was found at the higher sampling rates, evidenced by spectral peaks in solar insolation and sea-level pressure and by striking transient signals including short-lived insolation values higher than clear-sky values, short periods with air warmer than the sea surface, and by series of downdrafts of dry air. At longer periods, the presence of moist air accompanying low winds and sunny skies enhanced ocean heating. Winter events with dry air and wind, resulting in large latent and net heat loss, led to ocean cooling. Signals of two hurricanes, Darby and Douglas, were recorded. Normalized by their duration, short-lived events have the potential to make significant contributions to the heat, freshwater, and mechanical energy exchanges.

Meteorology doi: 10.3390/meteorology5010004

Authors: Ognjen Bonacci Ana Žaknić-Ćatović Tamara Brleković Tanja Roje-Bonacci Anita Filipčić

This study investigates the intensification of drought in the continental part of the Istrian peninsula using two standardized drought indices: the Standardized Precipitation Evapotranspiration Index (SPEI) and the New Drought Index (NDI). Monthly precipitation and temperature data from the main meteorological station in Pazin, covering the period 1961–2024, were analyzed. Statistical methods, including linear regression, Mann–Kendall test, and Rescaled Adjusted Partial Sums (RAPS) analysis, were applied to detect trends and fluctuations in the time series. Results indicate a significant increase in mean annual air temperatures since the late 1990s, with particularly strong warming in summer months. Precipitation trends, although highly variable, did not show a statistically significant long-term decline. Both drought indices reveal an intensification of drought conditions after 1985, with NDI showing stronger sensitivity to temperature rise than SPEI. Seasonal analyses demonstrate that drought occurrence is most pronounced during the warm part of the year, while cumulative series indicate a shift from predominantly wet to predominantly dry conditions after the mid-1980s. The comparison of the two indices shows a high degree of agreement but also highlights the added value of NDI in detecting temperature-driven drought processes. The findings emphasize the growing risk of more frequent and severe droughts in humid regions of Istria, including the potential for flash drought events. These results may support the development of improved drought early-warning systems and adaptation strategies in the Mediterranean context.

Meteorology doi: 10.3390/meteorology5010003

Authors: P. C. M. de Menezes D. C. de Souza M. G. Tavares R. A. G. Marques

Accurate air temperature and precipitation data are fundamental for environmental and socioeconomic applications in Brazil. However, the observational network managed by the National Institute of Meteorology, suffers from spatial gaps, necessitating the use of gridded datasets. This study provides a rigorous comparative assessment of three prominent gridded products—the station-interpolated dataset of Brazilian Daily Weather Gridded Data (BR-DWGD), the satellite-gauge blended product MERGE, and the ERA5-Land Reanalysis dataset—against station data. We evaluate the performance of the institutionally supported MERGE and ERA5-Land products as viable alternatives to the interpolated dataset. Daily data for maximum temperature (Tmax), minimum temperature (Tmin), and total precipitation were selected from 1994 to 2024 and analyzed using statistical metrics. The interpolated product showed the highest fidelity to observations, especially for temperature. For precipitation, the MERGE product demonstrated the best performance, achieving higher correlation and lower error than both the interpolated dataset and the poorly performing ERA5-Land. For temperature, ERA5-Land proved to be an excellent alternative for minimum temperature, but exhibited significant regional biases for maximum temperature and a tendency to underestimate heat extremes. We conclude that MERGE is the most robust alternative for precipitation studies in Brazil. ERA5-Land is a highly reliable source for minimum temperature, but its direct use for maximum temperature requires caution.

Meteorology doi: 10.3390/meteorology5010002

Authors: Juddy N. Okpara Kehinde O. Ogunjobi Elijah A. Adefisan

Drought remains a phenomenal disaster of critical concerns in West Africa, particularly within the Niger River Basin, due to its insidious, multifaceted, and long-lasting nature. Its continuous severe impacts on communities, combined with the limitations of existing univariate index-based monitoring methods, worsen the challenge. This paper introduces and evaluates a Hybrid Drought Resilience Empirical Model (DREM) that integrates meteorological, agricultural, and hydrological indicators to improve their concurrent monitoring and early warning for effective decision-making in the region. Using reanalysis hydrometeorological data (1980–2016) and community vulnerability records, results show that the DREM-based composite index detects drought earlier than the Standardized Precipitation Index (SPI), with stronger alignment to soil moisture and streamflow variations. The model identifies drought onset when thresholds range from −0.26 to −1.19 over three consecutive months, depending on location, and signals drought termination when thresholds rise between −0.08 and −0.82. The study concludes that the DREM-based composite index provides a more reliable and integrated framework for early drought detection and decision-making across the Niger River Basin, and hence, has proven to be a suitable drought monitor for stakeholders in the Niger Basin which can be relied upon and trusted with high confidence.

Meteorology doi: 10.3390/meteorology5010001

Authors: Larissa de Paula Miranda Jeferson Prietsch Machado Jaci Bilhalva Saraiva Débora Gadelha de Barros Elaine Siqueira Goulart Hugo Nunes Andrade

Extratropical cyclones are the main drivers of high-energy wave events along the southern coast of Brazil, frequently producing hazardous coastal conditions. Between 2001 and 2020, we identified 51 high-impact coastal storms based on Marine Weather Warnings and ERA5 reanalysis. Events showed a clear seasonal pattern, with the highest occurrence in winter and autumn. Composite analyses revealed that these extreme events are consistently associated with strong meridional pressure gradients and southerly to southeasterly low-level winds, which establish long wind-fetch zones that favor the generation and shore-normal propagation of energetic waves. Significant wave heights typically exceeded 4 m along the entire coastline, with maxima south of 35° S. EOF analyses showed that the dominant mode of variability is a recurrent low-pressure system centered between 40 and 45° S over the southwestern Atlantic. In contrast, the second mode represents the dipole between continental high pressure and oceanic low pressure that intensifies storm-related wave generation. Case studies from 2008 and 2015 confirmed that these synoptic patterns result in prolonged hazardous sea states and coastal impacts, including bar closures at the Port of Rio Grande, totaling 355 h of inoperability. These findings provide a clear characterization of the meteoceanographic patterns associated with high-impact coastal storms in southern Brazil and offer a climatological basis for improving early warning, navigation safety, and coastal risk management.

Meteorology doi: 10.3390/meteorology4040033

Authors: Bushra Khalid Sherly Shelton Amber Inam Ammara Habib Debora Souza Alvim

Heat extremes or heatwave events have significantly impacted socioeconomic activities and ecological systems, causing serious health issues and increased mortality rates in Pakistan over the past few decades. This study investigates the relationship between heat extremes in the northern Indian Ocean’s sea surface temperature (SST) and atmospheric temperature over Land (ATL) in Pakistan, and their connection to the Niño 3.4 Index, for monthly (March–August) and seasonal (spring and summer) basis from 1979 to 2015. Results show that SST has a higher frequency of heat extreme anomalies over different stretches of days than ATL. On a seasonal scale, heat extremes in ATL showed a significant correlation with SST, while the relationship was insignificant on a monthly basis. Both ATL and SST exhibited strong associations with the Niño 3.4 Index for land and ocean. These findings suggest that large-scale ocean-atmosphere interactions, particularly El Niño Southern Oscillation (ENSO), play a key role in modulating heat extremes in the region. The results of this study support SDGs by improving adaptive capacity and resilience on health, hunger, and climate by guiding policymakers in mitigating heat extremes. Integrating the findings of this study into national and provincial heat extreme plans may facilitate timely resource allocation and adaptation strategies in one of the world’s most climate-vulnerable regions.

Meteorology doi: 10.3390/meteorology4040032

Authors: Matheus Bonjour Laviola da Silva Fernando Tulio Camilo Barreto Leonardo Carvalho de Jesus Kaio Calmon Lacerda Maxsuel Marcos Rocha Pereira Edson Pereira Marques Filho Julio Tomás Aquije Chacaltana

This study investigates the impact of high-resolution Sea Surface Temperature (SST) boundary conditions on atmospheric simulations over the southwestern Atlantic Ocean (12–27° S, 32–48° W). Numerical experiments were conducted using the WRF model with two distinct SST configurations: standard resolution GFS SST data (0.5°) and high-resolution RTG-SST-HR satellite-derived data (0.083°). Simulations covered contrasting seasonal periods (January and July 2016) to capture varying upwelling intensities and atmospheric circulation patterns. Model performance was evaluated against observational data from the Brazilian National Buoy Program (PNBOIA) using statistical metrics including RMSE and Pearson correlation coefficients for wind components. The high-resolution SST experiment demonstrated significant improvements in wind field representation, with RMSE reductions of up to 0.5 m/s for zonal wind components and correlation improvements of approximately 0.1 across multiple validation sites. Most notably, the enhanced SST resolution enabled better representation of mesoscale atmospheric systems, including improved organization and intensification of cyclonic systems in areas near the cyclogenesis regions. The RTG-SST data captured sharp thermal gradients and coastal upwelling signatures that were spatially smoothed in the GFS fields, leading to more realistic surface heat flux patterns and atmospheric boundary layer dynamics. These improvements were particularly pronounced during summer months when thermal gradients were strongest, highlighting the critical importance of accurate SST representation for capturing high-intensity atmospheric phenomena in regions of strong air-sea interaction.

Meteorology doi: 10.3390/meteorology4040031

Authors: Julia Eduarda Araujo Inocêncio Oliveira Mulaveia Maurício Santana de Paula Fabiani Denise Bender Fernando Coelho Eugenio Jefferson Vieira José Adma Viana Santos Lucas da Costa Santos

Climate variability and extreme events disproportionately affect rural regions with limited adaptive capacity. In Minas Gerais, Brazil, mesoregions with semi-arid characteristics face severe vulnerabilities, underscoring the importance of detailed regional climate trend analyses. This study analyzed historical air temperature (maximum, minimum, and average) and precipitation from 1990 to 2019 in four mesoregions of Minas Gerais. The goal was to support climate planning and the development of local responses. Daily data from the National Institute of Meteorology (INMET) and a gridded meteorological database were analyzed using Mann–Kendall and Sen’s non-parametric tests, with a 95% confidence level (p-value ≤ 0.05) to identify significant trends. Annual results showed significant increases in maximum temperature in 15 of 24 evaluated areas, with rates from −0.03 to +0.15 °C year−1. For minimum and average temperatures, significant increases were observed in 17 locations. Annual precipitation showed a downward trend in 21 areas. Monthly and seasonal analyses confirmed this pattern of warming and reduced rainfall. These findings indicate an intensification of climate stress in over 80% of the studied locations, potentially impacting agriculture, public health, and ecosystems, requiring specific regional adaptive responses.

Meteorology doi: 10.3390/meteorology4040030

Authors: Anastasia Revokatova Mikhail Nikitin Iliya Lomakin Gdaliy Rivin Inna Rozinkina

Polar mesocyclones are often the cause of sudden worsening of weather conditions, including strong winds, snowfall with low visibility, and storms. The short lifetime, rapid development, high movement speeds, and small sizes, combined with a lack of meteorological observations over the Arctic seas, create difficulties in forecasting associated weather phenomena. High-resolution numerical modeling can help address this issue. The emergence and development of polar lows (PLs) significantly depend on the properties of the underlying surface, which largely determine the dynamic properties of the atmosphere in the boundary layer. This article is dedicated to assessing the sensitivity of the configuration ICON-Ru of the model ICON with a 2.0 km grid spacing to changes in the sea ice boundary and sea surface temperature (SST) when forecasting the formation and development of PLs. The results showed that the presence of artificial ice in the model almost completely suppresses the development of PLs in cases where the vortex does not have a strong connection with the jet stream. Heating the SST to 278.15 K while simultaneously shifting the ice boundary northward leads to increased thermal instability, rising sensible and latent heat fluxes, and higher CAPE, which enhances PLs, with the degree of enhancement depending on the nature of the vortex formation itself.

Meteorology doi: 10.3390/meteorology4040029

Authors: Jason J. Senter Anthony R. Lupo

Understanding the intricate dynamics of precipitation patterns is essential for effective water resource management and climate adaptation in Missouri. Existing analyses of Missouri’s climate variability lack the spatial granularity needed to capture nuanced variations across climate divisions. The Missouri historical agricultural weather database, an open-source tool that contains key weather measurements gathered at Mesonet stations across the state, is beginning to fill in the data sparsity gaps. The aim of this study is to identify core patterns associated with ENSO in the global wavelet output. Using a continuous wavelet transform analysis on data from 32 stations (2000–2024), we identified significant precipitation cycles. Where previous studies used just four Automated Surface Observing Systems (ASOSs) located at airports across Missouri to characterize climate variability, this study uses an additional 28 from the Missouri Mesonet. The use of a global wavelet power spectrum analysis reveals that precipitation patterns, with the exception of southeast Missouri, have a distinct annual cycle. Furthermore, separating the stations based on the significance of their ENSO (El Niño–Southern Oscillation) signal results in the identification of three precipitation zones: an annual, ENSO, and residual zone. This spatial data analysis reveals that the Missouri climate division boundaries broadly capture the three precipitation zones found in this study. Additionally, the results suggest a corridor in central Missouri where precipitation is particularly sensitive to an ENSO signal. These findings provide critical insights for improved water resource management and climate adaptation strategies.

Meteorology doi: 10.3390/meteorology4040028

Authors: Andrea Abbate Francesco Apadula

Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and landslides) which impact people, human activities, buildings, and infrastructure. Therefore, having a tool able to reconstruct rainfall processes easily and understandably is advisable for non-expert stakeholders and researchers who deal with rainfall management. In this work, an evolution of the LUME (Linear Upslope Model Experiment), designed to simplify the study of the rainfall process, is presented. The main novelties of the new version, called LUME 2D, regard (1) the 2D domain extension, (2) the inclusion of warm-rain and cold-rain bulk-microphysical schemes (with snow and hail categories), and (3) the simulation of convective precipitations. The model was completely rewritten using Python (version 3.11) and was tested on a heavy rainfall event that occurred in Piedmont in April 2025. Using a 2D spatial and temporal interpolation of the radiosonde data, the model was able to reconstruct a realistic rainfall field of the event, reproducing rather accurately the rainfall intensity pattern. Applying the cold microphysics schemes, the snow and hail amounts were evaluated, while the rainfall intensity amplification due to the moist convection activation was detected within the results. The LUME 2D model has revealed itself to be an easy tool for carrying out further studies on intense rainfall events, improving understanding and highlighting their peculiarity in a straightforward way suitable for non-expert users.

Meteorology doi: 10.3390/meteorology4040027

Authors: Mahmoud Osman Prakrut Kansara Taufique H. Mahmood

The hydrology of the transboundary region encompassing the western Red River Basin headwaters, such as Devils Lake Basin (DLB) in North America, is complex and highly sensitive to climate variability, impacting water resources, agriculture, and flood risk. Understanding hydrological shifts in this region is critical, particularly given recent hydroclimatic changes. This study aimed to simulate and analyze key hydrological processes and their evolution from 1981 to 2020 using an integrated modeling approach. We employed the NASA Land Information System (LIS) framework configured with the Noah-MP land surface model and the HyMAP routing model, driven by a combination of reanalysis and observational datasets. Simulations revealed a significant increase in precipitation inputs and consequential positive net water storage trends post-1990, indicating increased water retention within the system. Snow dynamics showed high interannual variability and decadal shifts in average Snow Water Equivalent (SWE). Simulated streamflow exhibited corresponding multi-decadal trends, including increasing flows within a major DLB headwater basin (Mauvais Coulee Basin) during the period of Devils Lake expansion (mid-1990s to ~2011). Furthermore, analysis of decadal average seasonal hydrographs indicated significant shifts post-2000, characterized by earlier and often higher spring peaks and increased baseflows compared to previous decades. While the model captured these trends, validation against observed streamflow highlighted significant challenges in accurately simulating peak flow magnitudes (Nash–Sutcliffe Efficiency = 0.33 at Mauvais Coulee River near Cando). Overall, the results depict a non-stationary hydrological system responding dynamically to hydroclimatic forcing over the past four decades. While the integrated modeling approach provided valuable insights into these changes and their potential drivers, the findings also underscore the need for targeted model improvements, particularly concerning the representation of peak runoff generation processes, to enhance predictive capabilities for water resource management in this vital region.

Meteorology doi: 10.3390/meteorology4040026

Authors: Sâmia R. Garcia Maria A. M. Rodrigues Mary T. Kayano Alan J. P. Calheiros

Extreme precipitation events have become a central focus of the scientific community due to their increased occurrence in recent years. This study aims to analyze the variability and trends in aspects associated with the rainy seasons in the South American Monsoon System (SAMS) area from 1979 to 2022. The dates for the onset and demise of the rainy season (ONR and DER, respectively) were determined using antisymmetric outgoing longwave radiation (OLR) data relative to the equator (AOLR) for the clustered regions defined in a previous work. Based on these dates, the duration of the rainy seasons and the total precipitation for each rainy season were also calculated. The main advantage of this study is the analysis of trends within homogeneous regions derived from cluster analysis, which enables a more reliable assessment of precipitation patterns across the spatially heterogeneous SAMS domain. The non-parametric Mann–Kendall test and Sen’s slope estimator were applied to the ONR, DER, rainy season length, and total precipitation time series for each group over the 1979–2022 period. Quartile analysis was performed on the total precipitation time series to identify the most and least intense rainy seasons in the SAMS’s regions. These analyses revealed a trend of shortening of the SAMS rainy season over the 44 years of analysis, with a positive trend in the ONR dates and a negative trend in the DER dates, which is further confirmed by the decreasing trends in rainy season length and accumulated precipitation in most analyzed regions. The most (above the third quartile) and least (below the first quartile) intense rainy seasons were found to be concentrated at the beginning and end of the study period, respectively, for all monsoon regions. After removing the linear trend, the distribution of events appeared more uniform over time, yet the major droughts that occurred after 2010 remained clear. The results of this study contribute to a better understanding of the precipitation characteristics in the SAMS area, and these findings may assist climate forecasting and monitoring centers in improving regional precipitation assessments.

Meteorology doi: 10.3390/meteorology4030025

Authors: Eduardo R. Eras Paulo Y. Kubota Juliana A. Anochi Haroldo F. de Campos Velho

A parameterization for the planetary boundary layer (PBL) based on the statistical theory of turbulence formulated by Geoffrey Ingram Taylor is derived to be applied in the Brazilian Global Atmospheric Model (BAM). The BAM model is the operational system employed by the National Institute for Space Research (INPE), Brazil, to produce numerical weather and climate predictions. A comparison of the BAM model simulations using Taylor’s parameterization is carried out against other three turbulent representations. The forecasting from different parameterizations with BAM is evaluated with the ERA-5 reanalysis. Predictions were performed on different initial conditions, representing two types of climate seasons: dry and wet seasons, for the Southern Hemisphere. The comparison shows that Taylor’s approach is competitive with other turbulence parameterizations, especially for the dry season. It must be highlighted that the forecasting over the Amazon region—one of the regions on the planet with the most intense rainfall, where Taylor’s approach provided more effective precipitation forecasting, a particularly challenging meteorological variable to predict.

Meteorology doi: 10.3390/meteorology4030024

Authors: Michael Splitt Steven Lazarus

Wind gustiness at airports, which is generally determined using gust factors, is impactful across a range of considerations from piloting to airport planning. Yet advisory materials to help assess their quality and representativeness, particularly for aviators, are limited. To address this, a climatological analysis of both gust factors is conducted using Automated Surface Observing System (ASOS) wind observations. Data for multi-year periods at selected airports in the United States are used to assess their site representativeness and for turbulence attribution purposes. Both gust factors vary by direction in response to local terrain features and nearby obstructions and are generally not well correlated with each other. The meteorological gust factor is shown to be more responsive to local obstructions in proximity to the ASOS systems. Excluding lower gusts leads to a marked improvement in the correlation between the two gust factors. Due to ASOS’s siting limitations, attributing observed gustiness to turbulence from nearby terrain or structures is difficult. The gustiness is often localized and may not represent conditions across the full airport. Excluding lower gusts increases the aviation gust factor’s sensitivity to local obstructions. This suggests that obstructions may play a meaningful role in shaping the higher observed gust factors. The potential exists to provide pilots and other users of this data with site- and direction-specific metadata regarding observed gustiness, thereby improving situational awareness.

Meteorology doi: 10.3390/meteorology4030023

Authors: Joshua M. Gilliland

This study examines wind speed trends based on seven mandatory pressure levels of the atmosphere for Brazil from 1980 to 2022 using radiosonde and climate reanalysis products. The results show that austral summer (DJF) and winter (JJA) wind speed trends are predominately influenced by upper tropospheric circulations in each reanalysis model. A vertical wind profile shows that the lowest wind speed trend changes occur below 500 hPa, while the largest wind speed trend tendencies develop in the upper troposphere (400–200 hPa). To further quantify this finding, a spatial profile of wind speed change is developed through a three-dimensional model. The model shows that two synoptic features are possibly controlling upper-level air trends across Brazil. During summer, decreased (increased) upper-level wind speeds across southern and northeastern (central-west and southeastern) Brazil are related to changes in temperature and geopotential heights occurring in proximity of the Bolivian high. This anticyclone gradually dissipates and the role of the subtropical jet stream affects upper-level wind trends across the subtropical latitudes of Brazil during winter. Finally, an upper-level wind analysis is also conducted to support the geographical findings shown in the three-dimensional wind trend model. The results provide a foundation for understanding how wind speeds vary not only from a vertical but also from a spatial (horizontal) perspective across Brazil.

Meteorology doi: 10.3390/meteorology4030022

Authors: Constantinos Demetrios Chatzithomas

Accurate estimations of reference evapotranspiration (ETo) are critical for hydrologic studies, efficient crop irrigation, water resources management and sustainable development. The evaluation of an empirical method was carried out to estimate hourly ETo, utilizing short-wave radiation and relative humidity as a surrogate of vapor pressure deficit (VPD), calibrated under semi-arid conditions and validated for different climatic regimes (hyper-arid, arid, subhumid) using American Society of Civil Engineers Penman–Monteith (ASCE PM) (2005) values as a standard, for the state of California. For hyper-arid climatic conditions, the empirical method resulted in underestimation and had coefficient of determination (R2) values of 0.88–0.95 and root mean square error (RMSE) values of 0.062–0.115 mm h−1. Hyper-arid climatic conditions correspond to lower R2 and different relations between the vapor pressure deficit (VPD) and the relative humidity function (1/lnRH) that the empirical method utilizes. For the other climatic regimes (arid, semi-arid, subhumid), the empirical method performed satisfactorily. The RMSE was calculated for groups of empirical estimates corresponding to various wind velocity values, and it was satisfactory for >99% of wind speed values (u2). The RMSE was also calculated for grouped values of the estimates of the empirical method corresponding to observed VPDs and was satisfactory for >97% of all observed values of VPD, except for hyper-arid stations (59% of u2 and 60% of all observed values of VPD).

Meteorology doi: 10.3390/meteorology4030021

Authors: Nicholas J. Cook

The Offset Elliptical Normal (OEN) mixture model represents the seasonal–diurnal surface wind vector for wind engineering design applications. This study upgrades the parameterization of OEN by accounting for changes in format of the global database of surface observations, improving performance by eliminating manual supervision and extending the scope of the model to include skewness. The previous coordinate transformation of binned speed and direction, used to evaluate the joint probability distributions of the wind vector, is replaced by direct kernel density estimation. The slow process of sequentially adding additional components is replaced by initializing all components together using fuzzy clustering. The supervised process of sequencing each mixture component through time is replaced by a fully automated unsupervised process using pattern matching. Previously reported departures from normal in the tails of the fuzzy-demodulated OEN orthogonal vectors are investigated by directly fitting the bivariate skew generalized t distribution, showing that the small observed skew is likely real but that the observed kurtosis is an artefact of the demodulation process, leading to a new Offset Skew Normal mixture model. The supplied open-source R scripts fully automate parametrization for locations in the NCEI Integrated Surface Hourly global database of wind observations.

Meteorology doi: 10.3390/meteorology4030020

Authors: Jun Du

In an ensemble prediction system, each member performs differently from each other for individual cases. To adaptively (not only statistically) calibrate or post-process raw ensemble forecasts and produce more reliable and accurate forecast products case by case, it is necessary to understand how individual ensemble members behave inside an ensemble cloud. For example, how (randomly or orderly) does an individual member’s relative performance (including the best and worst members) vary with location and time? To quantify and understand these variations, this study proposes the “Performance Rank Variation Score (PRVS)” to measure the degree of ensemble member’s relative performance variation (the “motion” of members). The PRVS was applied to four real cases (representing the winter, spring, summer, and fall seasons) from the NCEP global ensemble forecast system (GEFS). Many interesting results were observed, which are otherwise hard to elucidate without this new score. At the same time, based on the revealed results, possible ensemble post-processing strategies are discussed for future developments, where a new concept of “transformed ensemble” was demonstrated as an example.

Meteorology doi: 10.3390/meteorology4030019

Authors: Vladimir S. Kostsov Maria V. Makarova

Quantifying long-term variations in the cloud liquid water path (LWP) is crucial to obtain a better understanding of the processes relevant to cloud–climate feedback. The 12-year (2013–2024) time series of LWP values obtained from ground-based measurements by the RPG-HATPRO radiometer near the Gulf of Finland is analysed, and the linear trends of the LWP for different sampling subsets of data are assessed. These subsets include all-hour, daytime, and night-time measurements. Two different approaches have been used for trend assessment, which produced similar results. Statistically significant linear trends have been detected for most data subsets. The most pronounced general trend over the period 2013–2024 has been detected for the daytime LWP, and it constitutes −0.0011 ± 0.00015 kg m−2 yr−1. This trend is driven mainly by the daytime LWP trend for the warm season (May–July, −0.0014 ± 0.00015 kg m−2 yr−1), which is considerably larger than the trend for the cold season (November–January, −0.00064 ± 0.00026 kg m−2 yr−1). Additionally, the analysis shows that the absolute number of clear-sky measurements decreased approximately by a factor of 4 if the years 2013 and 2024 are compared.

Meteorology doi: 10.3390/meteorology4030018

Authors: David Romero Eric J. Alfaro

The Standardized Precipitation Index (SPI) is widely used to determine drought severity worldwide. However, inconsistencies exist regarding its application in warm, humid tropical climatic zones. Originally developed for temperate regions with a continental climate, the index may not adequately reflect drought conditions in tropical environments where rainfall regimes differ substantially. This study identifies the following two principal reasons why the traditional calculation method fails to characterize drought severity in tropical domains: first, the marked humidity contrast between the consistently humid rainy season and the rest of the year, and second, the diverse drought types in tropical regions, which include both long-term and short-term events. Using data from meteorological stations in Mexico’s humid tropics and comparing them with temperate regions, the study demonstrates significant discrepancies between SPI-based drought classifications and actual precipitation patterns. Our analysis shows that the abundant precipitation during the rainy season causes biases in longer time scales integrated into multivariate drought indices. Considerations are established for adapting the SPI for decision makers who monitor drought in humid tropics, with specific recommendations on time scale limits to avoid biases. This work contributes to more accurate drought monitoring in tropical regions by addressing the unique climatic characteristics of these environments.

Meteorology doi: 10.3390/meteorology4030017

Authors: Fábio Farias Pereira Mahelvson Bazilio Chaves Claudia Rivera Escorcia José Anderson Farias da Silva Bomfim Mayara Camila Santos Silva

The São Francisco River provides water for agriculture, urban areas, and hydroelectric power generation, benefiting millions of people in Brazil. Its Basin supports various species, some of which are endemic and rely on its unique habitats for survival. Currently, monitoring maximum air temperature in the São Francisco River Basin is limited due to sparse weather stations. This study proposes three linear regression models to estimate maximum air temperature using satellite-derived land surface temperature from the Aqua’s moderate resolution imaging spectroradiometer across the Basin’s three main biomes: Caatinga, Cerrado, and Mata Atlântica. With over 94,000 paired observations of ground and satellite data, the models showed good performance, accounting for 46% to 54% of temperature variation. Cross-validation confirmed reliable estimates with errors below 2.7 °C. The findings demonstrate that satellite data can improve air temperature monitoring in areas with limited ground observations and suggest that the proposed biome-specific models could assist in environmental management and water resource planning in the São Francisco River Basin. This includes providing more informed policies for climate adaptation and sustainable development or analyzing variations in maximum air temperature in arid and semi-arid regions to contribute to desertification mitigation strategies in the São Francisco River Basin.

Meteorology doi: 10.3390/meteorology4020016

Authors: Fabio A. A. Andrade Torge Lorenz Marcos Moura Thomas Spengler Manoel Feliciano Stephanie Mayer

We present a model evaluation of road weather forecasts in Norway with the METRo model in a quasi-operational setting. The road weather forecasts are initialized with measurements made by road weather stations and driven by mesoscale weather forecast data from the Norwegian Meteorological Institute. One important source of hazardous driving conditions in Norway are freezing road-surface temperatures. We quantify the skill of our model setup to predict such conditions by computing the hit rates and false-alarm rates for incidences of freezing temperatures, relative to the climatological rates of occurrence. The METRo forecasts consistently add skill in wintertime and the crucial transitional seasons of spring and fall. Our study illustrates a successful proof-of-concept for novel, operational road weather forecasts in Norway, that could easily be realized with an open-source prediction model and readily available input data.

Meteorology doi: 10.3390/meteorology4020015

Authors: Cosimo Cagnazzo Sara Angelini

Atmospheric parameter acquisition along the vertical profile of the troposphere across different locations on the Earth is of primary importance in gaining knowledge of the evolution of large-scale meteorological systems and the relative movements of air masses. Normally, this happens thanks to the launch, into the atmosphere, of radiosondes connected to balloons filled with helium gas. However, on a small scale, and in particular geomorphological contexts, different and peculiar meteorological situations may arise, in which the air column in the lower layers can behave differently from normal, giving rise to the so-called thermal inversions. In this work, in a particular sinkhole in the Apulia region, the use of a multi-rotor UAV (Unmanned Aerial Vehicle) equipped with a temperature data logger was tested. The flight along the vertical, starting from the lowest point of the sinkhole, made it possible to archive the temperature data of the air column in the first 80 m of altitude. The data validation confirmed the goodness of the UAV acquisitions and their subsequent processing made it possible to extrapolate the vertical temperature profile of the sinkhole during the winter thermal inversion phenomenon. In addition to confirming the predisposition of this sinkhole to strong thermal inversions, the preliminary results of this work have highlighted the efficiency of this new methodology. It has proved to be useful in assessing small-scale vertical profiles of atmospheric variables in a relatively low altitude range. Furthermore, this methodology can represent a strong scientific and technological innovation applicable in the meteorological field and in that of environmental monitoring.

Meteorology doi: 10.3390/meteorology4020014

Authors: Anning Cheng Fanglin Yang

In this study, we evaluate aerosol, cloud, and radiation interactions in GFS.V17.p8 (Global Forecast System System Version 17 prototype 8). Two experiments were conducted for the summer of 2020. In the control experiment (EXP CTL), aerosols interact with radiation only, incorporating direct and semi-direct aerosol effects. The sensitivity experiment (EXP ACI) couples aerosols with both radiation and Thompson microphysics, accounting for aerosol indirect effects and fully interactive aerosol–cloud dynamics. Introducing aerosol and cloud interactions results in net cooling at the top of the atmosphere (TOA). Further analysis shows that the EXP ACI produces more liquid water at lower levels and less ice water at higher levels compared to the EXP CTL. The aerosol optical depth (AOD) shows a good linear relationship with cloud droplet number concentration, similar to other climate models, though with larger standard deviations. Including aerosol and cloud interactions generally enhances simulations of the Indian Summer Monsoon, stratocumulus, and diurnal cycles. Additionally, the study evaluates the impacts of aerosols on deep convection and cloud life cycles.

Meteorology doi: 10.3390/meteorology4020013

Authors: Ronald G. Ramírez-Nina Maria Assunção Faus da Silva Dias Pedro Leite da Silva Dias

A seasonal climatology of the diurnal cycle of precipitation (DCP) and the assessment of its observed trend since the beginning of the 21st century using the IMERG product are performed for South America (SA). Its high spatial–temporal resolution (Δx=0.1∘, Δt=0.5 h) enables the examination of the fine-scale features of the DCP associated with the complex physical characteristics of SA. Using 20 years of precipitation rate data, diurnal and semi-diurnal scale processes are analyzed through harmonic analysis. Diurnal metrics—including the hourly mean precipitation rate, normalized amplitude, and phase—are employed to quantify the DCP. The results indicate that large-scale mechanisms, such as the South American Monsoon System (SAMS), seasonally modulate the DCP. These mechanisms in combination with local factors (e.g., land use, topography, and water bodies) influence the timing of peak and intensity of precipitation rates. Cluster analysis identifies regions with homogeneous DCP; however, some distant regions are classified as homogeneous, suggesting that local-scale physical processes triggering precipitation onset operate similarly across these regions (e.g., thermally induced local circulations). The trend analysis of the DCP reveals that, over the past 20 years, the tropical region of SA has undergone changes in the intensity and hourly distribution of this fine-scale climate variability mode. This trend is heterogeneous in space and time and is possibly associated with land-use changes.

Meteorology doi: 10.3390/meteorology4020012

Authors: Chelsea Henry Alan W. Black

Illinois is a leading state for agricultural production in the United States, and corn production in the state has rapidly increased since the 1970s. Intensification of agriculture has been shown to have impacts on the atmosphere by altering humidity, and changes in land cover and soil moisture have resulted in changes in stability and temperature in the planetary boundary layer. Using descriptive statistics and regression analysis, this study assessed changes in temperature and dew point across different land cover classes, parts of the growing season, and by the geographic location of the station (north vs. south) in Illinois from 2005–2022 using data from 58 hourly weather stations. Overall, dew points are not increasing more rapidly in cultivated agriculture areas compared to other land cover classes in the state. Dew points are increasing across land cover classifications, particularly in the later part of the growing season. Temperatures are not as consistent, with decreases in temperature observed in cultivated agricultural areas and during the peak of the growing season. While dew points are increasing in both the northern and southern regions of the state, temperature increases are only found in the north. Dew point increases in Illinois do not appear to be driven by changing agricultural practices. However, future work should examine additional regions inside and outside of the Corn Belt to determine if changes in land cover and agricultural practices have impacts on the climates of those regions.

Meteorology doi: 10.3390/meteorology4020011

Authors: Henri Pinheiro Tercio Ambrizzi Kevin Hodges

Cut-off low (COL) pressure systems significantly influence local weather in regions with high COL frequency, particularly in western North America. Nonetheless, future changes in COL frequency, intensity, and precipitation patterns remain uncertain. This study examines projected COL changes and their drivers in western North America under a high greenhouse gas concentration pathway (SSP585) using a multi-model ensemble from CMIP6 and a feature-tracking algorithm. We compare historical simulations (1980–2009) and future projections (2070–2099), revealing a marked increase in COL track density during summer in the northeast Pacific and western United States, while a strong decrease is projected for winter, associated with shifts in jet streams. Climate models project an increase in COL-related precipitation in future climate, with winter and spring experiencing more intense and localized precipitation, while autumn showing a more widespread precipitation pattern. Additionally, there is an increased frequency of extreme precipitation events, though accompanied by large uncertainties. The projected increase in extreme precipitation highlights the need to understand COL dynamics for effective climate adaptation in affected areas. Further research should aim to refine projections and reduce uncertainties, supporting better-informed policy and decision-making.

Meteorology doi: 10.3390/meteorology4020010

Authors: Nina Miklavčič Rudi Vončina Maja Ivanovski

Accurate weather forecasting is essential for various industries, particularly in sectors like energy, agriculture, and disaster management. In Slovenia, weather predictions are crucial for estimating electrical current transmission efficiency through power lines and ensuring the reliable supply of electricity to consumers. This study focuses on quantifying measurement uncertainty in meteorological forecasts generated by the CALMET model, specifically addressing its impact on energy transmission reliability. The research highlights those local factors, such as topography, that contribute significantly to measurement uncertainty, which affects the accuracy of weather forecasts. The study examines meteorological parameters like temperature, wind speed, and solar radiation, identifying how environmental variations lead to fluctuations in forecast reliability. Understanding these uncertainties is critical for improving the precision of forecasts, especially for energy transmission, where even small errors can have substantial consequences. The primary goal of this study is to enhance forecast reliability by addressing measurement uncertainty. By improving the interpretation of data, refining measurement methods, and integrating advanced models, the study proposes ways to reduce uncertainty. These improvements could support better decision-making in energy transmission and other sectors that rely on accurate weather predictions. Ultimately, the findings suggest that addressing measurement uncertainty is key to ensuring more dependable and accurate forecasting in critical industries.

Meteorology doi: 10.3390/meteorology4020009

Authors: Prabodha Kumar Pradhan Sushant Kumar Lokesh Kumar Pandey Srinivas Desamsetti Mohan S. Thota Raghavendra Ashrit

Cyclone Mocha, classified as an Extremely Severe Cyclonic Storm (ESCS), followed an unusual northeastward trajectory while exhibiting a well-defined eyewall structure. It experienced rapid intensification (RI) before making landfall along the Myanmar coast. It caused heavy rainfall (~90 mm) and gusty winds (~115 knots) over the coastal regions of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, such as the coasts of Bangladesh and Myanmar. The factors responsible for the RI of the cyclone in lower latitudes, such as sea surface temperature (SST), tropical cyclone heat potential (TCHP), vertical wind shear (VWS), and mid-tropospheric moisture content, are studied using the National Ocean and Atmospheric Administration (NOAA) SST and National Center for Medium-Range Weather Forecasting (NCMRWF) Unified Model (NCUM) global analysis. The results show that SST and TCHP values of 30 °C and 100 (KJ cm−2) over the Bay of Bengal (BoB) favored cyclogenesis. However, a VWS (ms−1) and relative humidity (RH; %) within the range of 10 ms−1 and >70% also provided a conducive environment for the low-pressure system to transform into the ESCS category. The physical mechanism of RI and recurvature of the Mocha cyclone have been investigated using forecast products and compared with Cooperative Institute for Research in the Atmosphere (CIRA) and Indian Meteorological Department (IMD) satellite observations. The key results indicate that a dry air intrusion associated with a series of troughs and ridges at a 500 hPa level due to the western disturbance (WD) during that time was very active over the northern part of India and adjoining Pakistan, which brought north-westerlies at the 200 hPa level. The existence of troughs at 500 and 200 hPa levels are significantly associated with a Rossby wave pattern over the mid-latitude that creates the baroclinic zone and favorable for the recurvature and RI of Mocha cyclone clearly represented in the NCUM analysis. Moreover the Q-vector analysis and steering flow (SF) emphasize the vertical motion and recurvature of the Mocha cyclone so as to move in a northeast direction, and this has been reasonably well represented by the NCUM model analysis and the 24, 7-, and 120 h forecasts. Additionally, a quantitative assessment of the system indicates that the model forecasts of TC tracks have an error of 50, 70, and 100 km in 24, 72, and 120 h lead times. Thus, this case study underscores the capability of the NCUM model in representing the physical mechanisms behind the recurving and RI over the BoB.

Meteorology doi: 10.3390/meteorology4020008

Authors: Prabodha Kumar Pradhan Vinay Kumar Akhilesh Kumar Mishra Lokesh Kumar Pandey Nagarjuna Rao Dabbugottu

Arabian Sea (AS) and Bay of Bengal (BoB) cyclones around the Indian subcontinent cause widespread floods and other natural hazards. There is no single convincing answer to this puzzle in the era of global warming. The warming of the western and central Indian Ocean is one of the few prominent features of local warming. The availability of moisture in the atmosphere in the last decade is an important factor in the rapid intensification and strengthening of tropical cyclones (TCs) before landfall. Essentially, the AS basin has shown an upward trend in the number and intensity of very severe cyclones during the period of 2009–2019. The decadal variation (1991–2001, 2002–2011, and 2012–2021) in SST, vorticity, wind shear, and moisture is primarily responsible for the genesis and intensification of cyclones during the post-monsoon season (October–November–December) over the AS. The results showed that slight changes in wind conditions, such as increased wind shear and the northward shift of the Asian Jet Stream over the region, facilitate TC formation.

Meteorology doi: 10.3390/meteorology4010007

Authors: Günther Heinemann

Low-level jets (LLJs) are important features in the Arctic atmospheric boundary layer (ABL). In the present paper, a LLJ event during winter 2014/15 is investigated, which was observed at the Tiksi observatory (71.586° N, 128.918° E, 7 m asl) in the Laptev Sea region. Besides the routine synoptic observations, data from a meteorological tower and SODAR/RASS (sound detection and ranging/radio acoustic sounding system) were available. The latter yielded vertical profiles of wind and temperature in the ABL with a vertical resolution of 10 m and a temporal resolution of 20 min. In addition to the measurements, simulations were performed using the regional climate model CCLM with a 5 km resolution. CCLM was run with nesting in ERA5 data in a forecast mode, and the ABL measurements were used for comparison with a LLJ occurring from 31 December 2014 to 1 January 2015. The CCLM simulations agreed well with near-surface and SODAR observations and represented the LLJ development very well. The simulations showed that the LLJ at Tiksi was part of a downslope wind event and that LLJ structures were present over a large region. The flow was preconditioned by a barrier wind and channeling in the Lena Valley in the initial phase, but synoptic forcing from a low over the Laptev Sea dominated the mature and dissipation phases of the LLJ. High turbulence intensity occurred in the mature phase of the LLJ, which seemed to be associated with wave breaking. Downslope wind events are likely the reason for most LLJs at Tiksi.

Meteorology doi: 10.3390/meteorology4010006

Authors: Vinícius Albuquerque de Almeida Juliana Aparecida Anochi José Roberto Rozante Haroldo Fraga de Campos Velho

A machine learning (ML)-based methodology for predicting frosts was applied to the southern and southeastern regions of Brazil, as well as to other countries including Uruguay, Paraguay, northern Argentina, and southeastern Bolivia. The machine learning model (using TensorFlow (TF)) was compared to the frost index (IG from the Portuguese: Índice de Geada) developed by the National Institute for Space Research (INPE, Brazil). The IG is estimated using meteorological variables from a regional weather numerical model (RWNM). After calculating the two indices using the ML model and the RWNM, a voting committee (VC) was trained to select between the computed outputs. The AdaBoostClassifier algorithm was employed to implement the voting committee. The study area was subdivided into three distinct subregions: R1 (outside Brazil), R2 (the south of Brazil), and R3 (southeastern Brazil). Two forecasting time scales were evaluated: 24 h and 72 h. The 24 h forecasts from both approaches (TF and RWNM) exhibited a similar performance in terms of the number of accurate predictions. However, in the region covering Uruguay and northern Argentina, the TensorFlow model demonstrated superior frost prediction accuracy. Additionally, the TensorFlow model outperformed the RWNM for the 72 h forecast horizon.

Meteorology doi: 10.3390/meteorology4010005

Authors: Angela Maria de Arruda Luana Nunes Centeno André Becker Nunes

This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in Rio Grande do Sul (RS) during 45-day subseasonal periods from 2006 to 2022. Precipitation data from 670 rain gauges were categorized into three clusters: cluster 1, located in western RS, displayed the lowest precipitation variation; cluster 2, in eastern RS, exhibited the greatest variability; and cluster 3, situated in northern RS. ENSO demonstrated the strongest positive correlation with precipitation during spring in clusters 1 and 3 (0.65–0.79), while PDO also correlated positively, especially in summer and spring. AOC exhibited negative correlations, most pronounced in spring. Significant inter-index correlations were identified, including a high positive correlation between SASH and AOC (0.7) and a high negative correlation between NINO34 and SOI (−0.73). Within clusters, NINO34 and PDO showed low positive correlations with precipitation (0.24–0.32), while SOI demonstrated low negative correlations (−0.21 to −0.30). Seasonal analysis revealed that NINO34 influenced summer and spring precipitation, correlating with above-average rainfall during El Niño events. SASH and PDO also showed positive correlations with summer and spring rainfall, with PDO’s positive phase associated with a 25% increase in precipitation. These findings provide valuable insights into the complex interactions between global climatic indices and regional precipitation patterns, enhancing the understanding of subseasonal climate variability in RS and supporting the development of more accurate climate prediction models for the region.

Meteorology doi: 10.3390/meteorology4010004

Authors: António Manuel Rochette Cordeiro

This study investigates the formation of cold air pools during calm, anticyclonic winter nights in a topographic basin bounded by a medium-sized mountain to the east and near-flat terrain elsewhere. The main objective is to understand how local topography drives unique topoclimatic conditions—specifically cold air lakes and an inversion layer at approximately 100/120 m altitude—in a peri-urban depression where a major cement factory and several residential areas are located. To achieve this, the research design combined surface measurements (collected at 10:00 p.m., 3:00 a.m., 7:00 a.m., and 3:00 p.m.) using a motorized vehicle, with vertical measurements (at 7:00 a.m.) collected via two unmanned aerial vehicles (UAVs), with the three vehicles equipped with Tinytag data loggers. The Empirical Bayesian Kriging tool in ArcGIS Pro was employed to generate the surface temperature cartograms. The results show that shortly after sunset, a cold air layer of approximately 100–120 m thickness forms, with nocturnal air temperature variations of up to 8 °C on the night measurements. An inversion layer was detected at around 120–130 m, while near-zero wind speeds in the basin’s core facilitate the retention of cold air. Surface spatialization confirms earlier findings of a cold air lake and thermal belts on the basin’s perimeter, forming in the early evening and dissipating by late morning. A 3D visualization underscores the influence of the mountain in directing cold air downslope, leading to stabilization and stratification within the lower atmospheric layers. These findings carry significant health implications: air pollutants released by the cement plant tend to accumulate within the cold air pool and beneath the inversion layer, posing potential risks to nearby populations.

Meteorology doi: 10.3390/meteorology4010003

Authors: Nazario Tartaglione

On 15 September 2022, a flood affected the Marche region, an Italian region that faces the Adriatic Sea. Unlike previous floods that affected the same area, no typical weather system, such as cyclones or synoptic fronts, caused the recorded extreme precipitation. In fact, the synoptic situation was characterized by a zonal flow, which normally does not cause intense precipitation over that area. The aim of this study was to understand which ingredients led to extraordinary precipitation in the region. ERA5 and the Weather Research Forecast (WRF) model were used to describe the synoptic situation and to reproduce rainfall. While limited area models with a horizontal resolution of a few km failed to forecast the precipitation, as confirmed by a WRF simulation with a horizontal resolution of 3 km, reducing the horizontal grid spacing to about 500 m improved the rain’s reproducibility. Together with a zonal flow that interested most of Italy, an atmospheric river starting in the eastern Mediterranean Sea transported moisture over the region. The interaction between the zonal flow and orography resulted in frontogenesis in the Apennine Lee. This process deformed the thermal structures in the area and created conditions of convective instability, transforming the moisture into copious rainfall. Moreover, ERA5 and the time series of observed rainfall from 1959 to 2022 were used to explore whether similar events, in terms of geopotential height configuration and rainfall, occurred in the past. Three metrics were employed to compare the event’s 700 hPa geopotential height pattern with all the other patterns, and the result was that the event was unique in the sense that a zonal flow, like that observed during the event of 15 September 2022, had never produced such an amount of precipitation in the time range considered, while all the events with the highest rainfall were usually associated with cyclonic structures.

Meteorology doi: 10.3390/meteorology4010002

Authors: Lidia Irene Benítez-Valenzuela Zulia M. Sánchez-Mejía Enrico A. Yepez

Coastal lagoons play an essential role in the energy balance and heat exchange to the atmosphere. Furthermore, at mesoscale Monsoon systems and at local scales, sea breeze influences surface processes; however, there is a lack of information on such processes in arid and semiarid regions. We aimed to characterize the atmospheric conditions during sea and land breeze in different seasons and analyze at different temporal scales the variation of atmospheric stability, turbulent fluxes, lifting condensation level, and atmospheric boundary layer height. The study site is a subtropical semiarid coastal lagoon, Estero El Soldado, located in Northwestern Mexico (27°57.248′ N, 110°58.350′ W). Measurements were performed from January 2019 to September 2020 with an Eddy Covariance system (EC) and micrometeorological instruments over the water surface. Results show that there is a strong seasonality that enhances sea–land breeze dominance; sea breeze was 83% more frequent during the Monsoon, and the land breeze was 55% more frequent in the Post-Monsoon. Specific humidity (23.32 ± 3.84 g kg−1, q), potential temperature (307 ± 2.98 K, θp), latent heat (135 W m−2, LE), and turbulent kinetic energy (0.81 m2 s−2, TKE) were significantly higher during the Monsoon season at sea breeze events. Atmospheric boundary layer (ABL) and lifting condensation level (LCL) were higher in the Pre-Monsoon season (3250 ± 71 m and 1142 ± 565 m, respectively). During the Monsoon, surface conditions lead to lower LCL (~800 m) due to the amount of water vapor (q = 23.3 g kg−1).

Meteorology doi: 10.3390/meteorology4010001

Authors: Juliana Aparecida Anochi Marilia Harumi Shimizu

Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. By employing a supervised neural network (NN) within a machine learning framework, we developed a methodology to forecast precipitation and subsequently calculate the Standardized Precipitation Index (SPI) for predicting drought conditions across the continent. The proposed model was trained with precipitation data from the Global Precipitation Climatology Project (GPCP) for the period 1983–2023. It provided monthly drought forecasts, which were validated against observational data and compared with predictions from the North American Multi-Model Ensemble (NMME). Key findings indicate the neural network’s ability to capture complex precipitation patterns and predict drought conditions. The model’s architecture effectively integrates precipitation data, demonstrating superior performance metrics compared to traditional approaches like the NMME. This study reinforces the relevance of using machine learning algorithms as a robust tool for drought prediction, providing critical information that can assist in decision-making for sustainable water resource management.

Meteorology doi: 10.3390/meteorology3040021

Authors: Liviany Pereira Viana João Gerd Zell de Mattos

This article describes the main features of the impacts of global observations on the reduction of errors in the data assimilation (DA) cycle carried out in the Brazilian Global Atmospheric Model (BAM) at Center for Weather Forecast and Climate Studies [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)]. These results show the importance of studying and evaluating the contribution of each observation to the DA system, therefore, two experiments (exp1/exp2) were performed with different configurations of the BAM model, with exp2 presenting the best fit between the Gridpoint Statistical Interpolation (GSI) and BAM systems. The BAM model was validated by the statistical metrics of root mean-square error and correlation anomaly, but this validation is not explored in this paper. A metric was applied that does not depend on the adjoint-based method, but only on the residuals that are made available in the GSI system for the observation space, given by the total impact, the fractional impact and the fractional beneficial impact. In general, the average daily showed that the observations of the global system that contribute most to the reduction of errors in the DA cycle are from the pilot balloon data (−3.54/−3.45 J kg−1)and the profilers (−2.13/−1.97 J kg−1), and the smallest contributions came from the land (−0.28/−0.29 J kg−1) and sea (−0.44/−0.44 J kg−1) surfaces. The same pattern was observed for the synoptic times presented. However, when verifying the fraction of the impact by each type of observation, it was found that the radiance data (64.88/30.30%), followed by radiosondes (14.85/27.42%) and satellite winds (11.03/22.70%), are the most important fractions for both experiments. These results show that the DA system is working to generate the best analyses at the research center and that the deficiencies found in some observations can be adjusted to improve the development of the GSI and the BAM model, since together, the entire database used is evaluated, as well as the forecast model itself, indicating the relationship between the assertiveness of the atmospheric model and the DA system used at the research center.

Meteorology doi: 10.3390/meteorology3040020

Authors: Toshihiro Kitada Sajan Shrestha Sangeeta Maharjan Suresh Bhattarai Ram Prasad Regmi

On the evening of 31 March 2019, Parsa and Bara Districts in central Nepal were severely hit by a wind storm which was the first documented tornadic incidence in Nepal.In this paper, we investigate the background of the tornado formation via numerical simulations with the WRF-ARW model. The results show that: (1) a flow situation favorable to the generation of mesocyclones was formed by a combination of local plain-to-mountain winds consisting of warm and humid southwesterly wind in the lower atmosphere and synoptic northwesterly wind aloft over the southern foothills of the Himalayan Mountain range, leading to significant vertical wind shear and strong buoyancy; (2) the generated mesocyclone continuously shed rain-cooled outflow with 600∼800 m depth above the ground into the Chitwan valley while moving southeastward along the Mahabharat Range at the northeastern rim of the Chitwan valley; (3) the cold outflow propagated in the valley, forming a front; and (4) the tornado was generated when this cold outflow passed over the Siwalik Hills bordering the southern rim of the Chitwan valley. At this point, descending flow around a high mountain generated positive vertical vorticity near the ground; blocking by this high mountain and channeling through a mountain pass enhanced updrafts at the front by forming a hydraulic jump. These updrafts amplified the positive vertical vorticity via stretching, and this interaction of the cold outflow with the Siwalik Hills contributed to tornadogenesis. The simulated location and time of the disaster showed generally good agreement with the reported location and time.

Meteorology doi: 10.3390/meteorology3040019

Authors: Jake Wiley Christopher Elcik

This study investigates the synoptic conditions associated with lake-effect snow (LES) over northwestern Pennsylvania with a focus on classifying cases based on the tracks of cyclones influencing the region, including Nor’easters (NEs), Alberta Clippers (ACs), Colorado Lows (COs), and Great Lakes Lows (GLs). Synoptic composites were constructed using the North American Regional Reanalysis (NARR) for all cases, as well as each cyclone group, using an LES repository spanning from 2006–2020. Additionally, 95 percent bootstrapped confidence intervals were created for each cyclone track to compare the initial mesoscale environmental properties (i.e., surface lake/air temperature and wind direction/speed) and LES impact (i.e., duration, maximum snowfall, and property damage). Synoptic composites of all LES cases exhibited an archetypal LES synoptic pattern consisting of an upper-level low geopotential height anomaly over the Hudson Bay and surface dipole structure centered across the Great Lakes basin. Regarding the different tracks, NEs and COs featured dynamic support in the form of enhanced turbulent mixing and synoptic vertical forcing, while ACs and GLs had greater thermodynamic support in the form of higher lapse rates and heightened heat and moisture fluxes. However, the bootstrapping analysis revealed minimal differences in LES impact between the cyclone types.

Meteorology doi: 10.3390/meteorology3040018

Authors: Emily Gleeson Ekaterina Kurzeneva Wim de Rooy Laura Rontu Daniel Martín Pérez Colm Clancy Karl-Ivar Ivarsson Bjørg Jenny Engdahl Sander Tijm Kristian Pagh Nielsen Metodija Shapkalijevski Panu Maalampi Peter Ukkonen Yurii Batrak Marvin Kähnert Tosca Kettler Sophie Marie Elies van den Brekel Michael Robin Adriaens Natalie Theeuwes Bolli Pálmason Thomas Rieutord James Fannon Eoin Whelan Samuel Viana Mariken Homleid Geoffrey Bessardon Jeanette Onvlee Patrick Samuelsson Daniel Santos-Muñoz Ole Nikolai Vignes Roel Stappers

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included.

Meteorology doi: 10.3390/meteorology3040017

Authors: Abhishek Kaul Alex Paparas Venkata K. Jandhyala Stergios B. Fotopoulos

Climatological variables undergo changes over time, and it is important to understand such dynamic changes at global, regional, and local levels. While global and regional studies are common in the study of climate, such studies at a local level are not as common. The aim of this article is to study temporal changes in precipitation, snowfall, and temperature variables at specific stations located on the rims of Lake Erie and Lake Michigan. The identification of changes is carried out by applying change-point analysis to precipitation, snowfall, and temperature data from Buffalo, Erie, and Cleveland stations located on the rim of Lake Erie and at Chicago, Milwaukee, and Green Bay stations located on the rim of Lake Michigan. We adopt mainly the Bayesian information criterion (BIC) method to identify the number and locations of change points, and then we apply the generalized likelihood ratio statistic to test for the statistical significance of the identified change points. We follow this up by finding 95% confidence intervals for those change points that were found to be statistically significant. The results from the analysis show that there are significant changes in precipitation, snowfall, and temperature variables at all six rim stations. Changes in precipitation show consistently significant increases, whereas there is no similar consistency in snowfall increases. Temperature increases are generally quite sharp, and they occur consistently around 1985. Overall, upon combining the amounts of changes from all six stations, the average amount of change in annual average temperature is found to be 0.96 °C, the average percentage of change in precipitation is 16%, and the average percentage of change in snowfall is 17%. The changing local climatic conditions identified in the study are important for local city planners, as well as residents, so that they can be well prepared for changing climatic scenarios.

Meteorology doi: 10.3390/meteorology3030016

Authors: Eliana Cristine Gatti Izabelly Carvalho da Costa Daniel Vila

Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulation within 1 h. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, one case study focusing on polarimetric variables is included. The results reveal that the generating system (synoptic or mesoscale) of intense rain events significantly influences the rainfall pattern, mainly in the South, Southeast, and Midwest regions. Regional CFADs unveil primary convective columns with 40–50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely, specific warnings.

Meteorology doi: 10.3390/meteorology3030015

Authors: Nicholas John Cook

Most damage to buildings across the contiguous United States of America (USA) is caused by gusts in convective events associated with thunderstorms. Design rules for structures to resist these events rely on the integrity of meteorological observations and the methods of assessment. These issues were addressed for the US Automated Surface Observation System (ASOS) in six preliminary studies published in 2022 and 2023, allowing this present study to focus on the analysis and reporting of gust events observed between 2000 and 2023 at 642 well-exposed ASOS stations distributed across the contiguous USA. It has been recently recognized that the response of buildings to convective gusts, which are non-stationary transient events, differs in character from the response to the locally stationary atmospheric boundary gusts, requiring gust events to be classified and assessed by type. This study sorts the mixture of all observed gust events exceeding 20 kn, but excluding contributions from hurricanes and tropical storms, into five classes of valid meteorological types and two classes of invalid artefacts. The valid classes are individually fitted to optimal sub-asymptotic models through extreme value analysis. Classes are recombined into a joint mixture model and compared with current design rules.

Meteorology doi: 10.3390/meteorology3030014

Authors: Mahelvson Bazilio Chaves Fábio Farias Pereira Claudia Rivera Escorcia Nathacha Cavalcante

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends.

Meteorology doi: 10.3390/meteorology3020013

Authors: Weihong Qian Jun Du Yang Ai Jeremy Leung Yongzhu Liu Jianjun Xu

Tropical cyclones (TCs) can cause severe wind and rain hazards. Unusual TC tracks and their extreme precipitation forecasts have become two difficult problems faced by conventional models of primitive equations. The case study in this paper finds that the numerical computation of the climatological component in conventional models restricts the prediction of unusual TC tracks. The climatological component should be a forcing quantity, not a predictor in the numerical integration of all models. Anomaly-based variable models can overcome the bottleneck of forecast time length or the one-week forecasting barrier, which is limited to less than one week for conventional models. The challenge in extreme precipitation forecasting is how to physically get the vertical velocity. The anomalous moisture stress modulus (AMSM), as an indicator of heavy rainfall presented in this paper, considers the two conditions associated with vertical velocity and anomalous specific humidity in the lower troposphere. Vertical velocity is produced by the orthogonal collision of horizontal anomalous airflows.

Meteorology doi: 10.3390/meteorology3020012

Authors: Adrian F. Tuck

The twin problems of closure and dissipation have been barriers to the analytical solution of the Navier–Stokes equation for fluid flow by top-down methods for two centuries. Here, the statistical multifractal analysis of airborne observations is used to argue that bottom-up approaches based on the dynamic behaviour of the basic constituent particles are necessary. Contrasts among differing systems will yield scale invariant turbulence, but not with universal analytical solutions to the Navier–Stokes equation. The small number of publications regarding a molecular origin for turbulence are briefly considered. Research approaches using suitable observations are recommended.

Meteorology doi: 10.3390/meteorology3020011

Authors: Edoardo Bucchignani

In the frame of the current growing awareness of climate change and its impact on society and ecosystems [...]

Meteorology doi: 10.3390/meteorology3020010

Authors: John R. Lawson Corey K. Potvin Kenric Nelson

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

Meteorology doi: 10.3390/meteorology3020009

Authors: Mary T. Kayano Wilmar L. Cerón Rita V. Andreoli Rodrigo A. F. Souza Marília H. Shimizu Leonardo C. M. Jimenez Itamara P. Souza

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales.

Meteorology doi: 10.3390/meteorology3020008

Authors: Daniel Martín Pérez Emily Gleeson Panu Maalampi Laura Rontu

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration.

Meteorology doi: 10.3390/meteorology3020007

Authors: André S. W. Teruya Víctor C. Mayta Breno Raphaldini Pedro L. Silva Dias Camila R. Sapucci

Instead of using the traditional space-time Fourier analysis of filtered specific atmospheric fields, a normal-mode decomposition method was used to analyze South American intraseasonal variability (ISV). Intraseasonal variability was examined separately in the 30–90-day band, 20–30-day band, and 10–20-day band. The most characteristic structure in the intraseasonal time-scale, in the three bands, was the dipole-like convection between the South Atlantic Convergence Zone (SACZ) and the central-east South America (CESA) region. In the 30–90-day band, the convective and circulation patterns were modulated by the large-scale Madden–Julian oscillation (MJO). In the 20–30-day and 10–20-day bands, the convection structures were primarily controlled by extratropical Rossby wave trains. The normal-mode decomposition of reanalysis data based on 30–90-day, 20–30-day, and 10–20-day ISV showed that the tropospheric circulation and CESA–SACZ convective structure observed over South America were dominated by rotational modes (i.e., Rossby waves, mixed Rossby-gravity waves). A considerable portion of the 30–90-day ISV was also associated with the inertio-gravity (IGW) modes (e.g., Kelvin waves), mainly prevailing during the austral rainy season. The proposed decomposition methodology demonstrated that a realistic circulation can be reproduced, giving a powerful tool for diagnosing and studying the dynamics of waves and the interactions between them in terms of their ability to provide causal accounts of the features seen in observations.

Meteorology doi: 10.3390/meteorology3010006

Authors: Katherine E. Lukens Kevin Garrett Kayo Ide David Santek Brett Hoover David Huber Ross N. Hoffman Hui Liu

Accurate atmospheric 3D wind observations are one of the top priorities for the global scientific community. To address this requirement, and to support researchers’ needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses the following: a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019–August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. The recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC’s capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture.

Meteorology doi: 10.3390/meteorology3010005

Authors: Jason Naylor Megan E. Berry Emily G. Gosney

Idealized simulations with a cloud-resolving model are conducted to examine the impact of a simplified city on the structure of a supercell thunderstorm. The simplified city is created by enhancing the surface roughness length and/or surface temperature relative to the surroundings. When the simplified city is both warmer and has larger surface roughness relative to its surroundings, the supercell that passes over it has a larger updraft helicity (at both midlevels and the surface) and enhanced precipitation and hail downwind of the city, all relative to the control simulation. The storm environment within the city has larger convective available potential energy which helps stimulate stronger low-level updrafts. Storm relative helicity (SRH) is actually reduced over the city, but enhanced in a narrow band on the northern edge of the city. This band of larger SRH is ingested by the primary updraft just prior to passing over the city, corresponding with enhancement to the near-surface mesocyclone. Additional simulations in which the simplified city is altered by removing either the heat island or surface roughness length gradient reveal that the presence of a heat island is most closely associated with enhancements in updraft helicity and low-level updrafts relative to the control simulation.

Meteorology doi: 10.3390/meteorology3010004

Authors: Erzsébet Kristóf Ferenc Ács Annamária Zsákai

We characterized the thermal load of a person walking and/or standing in the fog by analyzing the thermal resistance of clothing, rcl, and operative temperature, To. The rcl–To model applies to individuals using weather data. The body mass index and basal metabolic flux density values of the person analyzed in this study are 25 kg m−2 and 40 W m−2, respectively. Weather data are taken from the nearest automatic weather station. We observed 146 fog events in the period 2017–2024 in Martonvásár (Hungary’s Great Plain region, Central Europe). The main results are as follows: (1) The rcl and To values were mostly between 2 and 0.5 clo and −4 and 16 °C during fog events, respectively. (2) The largest and smallest rcl and To values were around 2.5 and 0 clo and −7 and 22 °C, respectively. (3) The rcl differences resulting from interpersonal and wind speed variability are comparable, with a maximum value of around 0.5–0.7 clo. (4) Finally, rcl values are significantly different for standing and walking persons. At the very end, we can emphasize that the thermal load of the fog depends noticeably on the person’s activity and anthropometric characteristics.

Meteorology doi: 10.3390/meteorology3010003

Authors: Edoardo Bucchignani

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output.

Meteorology doi: 10.3390/meteorology3010002

Authors: Andrew R. Jakovlev Sergei P. Smyshlyaev

Tropical sea surface temperature (SST) variability, mainly driven by the El Niño–Southern Oscillation (ENSO), influences the atmospheric circulation and hence the transport of heat and chemical species in both the troposphere and stratosphere. This paper uses Met Office, ERA5 and MERRA2 reanalysis data to examine the impact of SST variability on the dynamics of the polar stratosphere and ozone layer over the period from 1980 to 2020. Particular attention is paid to studying the differences in the influence of different types of ENSO (East Pacific (EP) and Central Pacific (CP)) for the El Niño and La Niña phases. It is shown that during the CP El Niño, the zonal wind weakens more strongly and changes direction more often than during the EP El Niño, and the CP El Niño leads to a more rapid decay of the polar vortex (PV), an increase in stratospheric air temperature and an increase in the concentration and total column ozone than during EP El Niño. For the CP La Niña, the PV is more stable, which often leads to a significant decrease in Arctic ozone. During EP La Niña, powerful sudden stratospheric warming events are often observed, which lead to the destruction of PV and an increase in column ozone.

Meteorology doi: 10.3390/meteorology3010001

Authors: Julie Sherman Christian Sampson Emmanuel Fleurantin Zhimin Wu Christopher K. R. T. Jones

Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study interannual variability in stratospheric zonal winds and sudden stratospheric warming (SSW) events. These models are most sensitive to two main parameters: Λ, forcing the mean radiative zonal wind gradient, and h, a perturbation parameter representing the effect of Rossby waves. We take one such reduced order model with 20 years of ECMWF atmospheric reanalysis data and estimate Λ and h using both a particle filter and an ensemble smoother to investigate if the highly-simplified model can accurately reproduce the averaged reanalysis data and which parameter properties may be required to do so. We find that by allowing additional complexity via an unparameterized Λ(t), the model output can closely match the reanalysis data while maintaining behavior consistent with the dynamical properties of the reduced-order model. Furthermore, our analysis shows physical signatures in the parameter estimates around known SSW events. This work provides a data-driven examination of these important parameters representing fundamental stratospheric processes through the lens and tractability of a reduced order model, shown to be physically representative of the relevant atmospheric dynamics.

Meteorology doi: 10.3390/meteorology2040030

Authors: C. Arturo Sánchez P. Alan J. P. Calheiros Sâmia R. Garcia Elbert E. N. Macau

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall’s spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network’s response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks.

Meteorology doi: 10.3390/meteorology2040029

Authors: Shaun Lovejoy Lenin Del Rio Amador

Beyond their deterministic predictability limits of ≈10 days and 6 months, the atmosphere and ocean become effectively stochastic. This has led to the development of stochastic models specifically for this macroweather regime. A particularly promising approach is based on the Fractional Energy Balance Equation (FEBE), an update of the classical Budyko–Sellers energy balance approach. The FEBE has scaling symmetries that imply long memories, and these are exploited in the Stochastic Seasonal and Interannual Prediction System (StocSIPS). Whereas classical long-range forecast systems are initial value problems based on spatial information, StocSIPS is a past value problem based on (long) series at each pixel. We show how to combine StocSIPS with a classical coupled GCM system (CanSIPS) into a hybrid system (CanStoc), the skill of which is better than either. We show that for one-month lead times, CanStoc’s skill is particularly enhanced over either CanSIPS or StocSIPS, whereas for 2–3-month lead times, CanSIPS provides little extra skill. As expected, the CanStoc skill is higher over ocean than over land with some seasonal dependence. From the classical point of view, CanStoc could be regarded as a post-processing technique. From the stochastic point of view, CanStoc could be regarded as a way of harnessing extra skill at the submonthly scales in which StocSIPS is not expected to apply.

Meteorology doi: 10.3390/meteorology2040028

Authors: Ayman Elyoussoufi Curtis L. Walker Alan W. Black Gregory J. DeGirolamo

Adverse weather conditions impact mobility, safety, and the behavior of drivers on roads. In an average year, approximately 21% of U.S. highway crashes are weather-related. Collectively, these crashes result in over 5300 fatalities each year. As a proof-of-concept, analyzing weather information in the context of traffic mobility data can provide unique insights into driver behavior and actions transportation agencies can pursue to promote safety and efficiency. Using 2019 weather and traffic data along Colorado Highway 119 between Boulder and Longmont, this research analyzed the relationship between adverse weather and traffic conditions. The data were classified into distinct weather types, day of the week, and the direction of travel to capture commuter traffic flows. Novel traffic information crowdsourced from smartphones provided metrics such as volume, speed, trip length, trip duration, and the purpose of travel. The data showed that snow days had a smaller traffic volume than clear and rainy days, with an All Times volume of approximately 18,000 vehicles for each direction of travel, as opposed to 21,000 vehicles for both clear and wet conditions. From a trip purpose perspective, the data showed that the percentage of travel between home and work locations was 21.4% during a snow day compared to 20.6% for rain and 19.6% for clear days. The overall traffic volume reduction during snow days is likely due to drivers deciding to avoid commuting; however, the relative increase in the home–work travel percentage is likely attributable to less discretionary travel in lieu of essential work travel. In comparison, the increase in traffic volume during rainy days may be due to commuters being less likely to walk, bike, or take public transit during inclement weather. This study demonstrates the insight into human behavior by analyzing impact on traffic parameters during adverse weather travel.

Meteorology doi: 10.3390/meteorology2040027

Authors: Vladimir S. Kostsov Dmitry V. Ionov

Liquid water path (LWP) is one of the most important cloud parameters and is crucial for global and regional climate modelling, weather forecasting, and modelling of the hydrological cycle and interactions between different components of the climate system: the atmosphere, the hydrosphere, and the land surface. Space-borne observations by the SEVIRI instrument have already provided evidence of the systematic difference between the cloud LWP values derived over the land surface in Northern Europe and those derived over the Baltic Sea and major lakes during both cold and warm seasons. In the present study, the analysis of this LWP land-sea contrast for the period 2011–2017 reveals specific temporal and spatial variations, which, in some cases, seem to be artefacts rather than of natural origin. The geographical objects of investigation are water bodies and water areas located in Northern Europe that differ in size and other geophysical characteristics: the Gulf of Finland and the Gulf of Riga in the Baltic Sea and large and small lakes in the neighbouring region. The analysis of intra-seasonal features has detected anomalous conditions in the Gulf of Riga and the Gulf of Finland, which show up as very low values of the LWP land-sea contrast in August with respect to the values in June and July every year within the considered time period. This anomaly is likely an artefact caused by the LWP retrieval algorithm since the transition from large LWP contrast to very low contrast occurs sharply, synchronically, and at a certain date every year at different places in the Baltic Sea.

Meteorology doi: 10.3390/meteorology2040026

Authors: Adrian F. Tuck

Four observational results: the intermittency of air temperature; its correlation with ozone photodissociation rate; the diurnal variation of ozone in the upper stratosphere; and the cold bias of meteorological analyses compared to observations, are reviewed. The excitation of photofragments and their persistence of velocity after collision is appealed to as a possible explanation. Consequences are discussed, including the interpretation of the Langevin equation and fluctuation–dissipation in the atmosphere, the role of scale invariance and statistical multifractality, and what the results might mean for the distribution of isotopes among atmospheric molecules. An adjunct of the analysis is an exponent characterizing jet streams. Observational tests are suggested.

Meteorology doi: 10.3390/meteorology2040025

Authors: Léa Berthomier Laurent Perier

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model’s robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model’s superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min.

Meteorology doi: 10.3390/meteorology2030024

Authors: Mohamed Hachaichi

Cities are progressively heightening their climate aspirations to curtail urban carbon emissions and establish a future where economies and communities can flourish within the Earth’s ecological limits. Consequently, numerous climate initiatives are being launched to control urban carbon emissions, targeting various sectors, including transport, residential, agricultural, and energy. However, recent scientific literature underscores the disproportionate distribution of climate policies. While cities in the Global North have witnessed several initiatives to combat climate change, cities in the Global South remain uncovered and highly vulnerable to climate hazards. To address this disparity, we employed the Balanced Iterative Reducing and Clustering using the Hierarchies (BRICH) algorithm to cluster cities from diverse geographical areas that exhibit comparable socioeconomic profiles. This clustering strives to foster enhanced cooperation and collaboration among cities globally, with the goal of addressing climate change in a comprehensive manner. In summary, we identified similarities, patterns, and clusters among peer cities, enabling mutual and generalizable learning among worldwide peer-cities regarding urban climate policy exchange. This exchange occurs through three approaches: (i) inner-mutual learning, (ii) cross-mutual learning, and (iii) outer-mutual learning. Our findings mark a pivotal stride towards attaining worldwide climate objectives through a shared responsibility approach. Furthermore, they provide preliminary insights into the implementation of “urban climate policy exchange” among peer cities on a global scale.

Meteorology doi: 10.3390/meteorology2030023

Authors: Vasilică Istrate Dorin Podiuc Dragoș Andrei Sîrbu Eduard Popescu Emil Sîrbu Doru Dorian Popescu

Using a database of 378 hail days between 1981 and 2020, the climatic characteristics of 23 convective parameters from sounding data and ERA5 data were statistically analysed. The goal of this work is to evaluate the usefulness and representativeness of convective parameters derived from sounding data and reanalysis data for the operational forecast of the hail phenomenon. As a result, the average values from 12:00 UTC were 433 J/kg for CAPE in the case of data from ERA5 and 505 J/kg from rawinsonde, respectively. The Spearman correlation coefficient matrix between the values of the parameters indicates high correlations among the parameters calculated based on the parcel theory, humidity indices, and the complex indices. The probability for large hail increases with high values of low-level and boundary-layer moisture, high CAPE, and a high lifting condensation level (LCL) height.

Meteorology doi: 10.3390/meteorology2030022

Authors: James A. Schiavone

Better understanding of roll vortices that often occur in the tropical cyclone (TC) boundary layer is required to improve forecasts of TC intensification and the granularity of damaging surface winds. It is especially important to characterize rolls over a wide variety of TCs, their environments, and TC development phases. Boundary layer rolls have been observed in TCs since 1998, but only recently in a TC during its extratropical transition phase. The work reported herein is the first to analyze how boundary layer rolls are distributed among the extratropical features of a transitioning TC. To this end, routine and special operational observations recorded during landfalling Post-tropical Cyclone Sandy (2012) were leveraged, including radar, surface, rawinsonde, and aircraft reconnaissance observations. Large rolls occurred in cold airstreams, both in the cold conveyor belt within the northwestern storm quadrant and in the secluding airstream within the northeastern quadrant, but roll presence was much diminished within the intervening warm sector. The large size of the rolls and their confinement to cold airstreams is attributed to an optimum inflow layer depth, which is deep enough below a strong stable layer to accommodate deep and strong positive radial wind shear to promote roll growth, yet not so deep as to limit radial wind shear magnitude, as occurred in the warm sector.

Meteorology doi: 10.3390/meteorology2030021

Authors: Nicholas John Cook

The reliability of extreme wind speed predictions at large mean recurrence intervals (MRI) is assessed by bootstrapping samples from representative known distributions. The classical asymptotic generalized extreme value distribution (GEV) and the generalized Pareto (GPD) distribution are compared with a contemporary sub-asymptotic Gumbel distribution that accounts for incomplete convergence to the correct asymptote. The sub-asymptotic model is implemented through a modified Gringorten method for epoch maxima and through the XIMIS method for peak-over-threshold values. The mean bias error is shown to be minimal in all cases, so that the variability expressed by the standard error becomes the principal reliability metric. Peak-over-threshold (POT) methods are shown to always be more reliable than epoch methods due to the additional sub-epoch data. The generalized asymptotic methods are shown to always be less reliable than the sub-asymptotic methods by a factor that increases with MRI. This study reinforces the previously published theory-based arguments that GEV and GPD are unsuitable models for extreme wind speeds by showing that they also provide the least reliable predictions in practice. A new two-step Weibull-XIMIS hybrid method is shown to have superior reliability.

Meteorology doi: 10.3390/meteorology2030020

Authors: Jayesh Phadtare

During the post-monsoon cyclone season, the landfalls of westward-moving cyclonic systems often lead to extreme rainfall over the east coast of the Indian peninsula. A stationary cyclonic system over the coast can produce heavy rainfall for several days and cause catastrophic flooding. This study analyzes the dynamics of a propagating and stationary cyclonic system over the east coast, highlighting the possible cause behind the stagnation. The vorticity budgets of these two systems are presented using a reanalysis dataset. Vortex stretching and horizontal vorticity advection were the dominant terms in the budget. Vertical advection and tilting terms were significant over the orography. The horizontal advection of vorticity was positive (negative) on the western (eastern) side of the systems and, thus, favored westward propagation. Vortex stretching was confined to the upstream of orography in the stationary vortex. In the propagating vortex, the vortex stretching occurred over the orography during its passage. Data from the radiosonde soundings over a coastal station showed orographic blocking of the low-level winds in the stationary case. Conversely, the flow crossed the orographic barrier in the propagating case. Thus, the predominance of the upstream orographic convergence over the vortex circulation can be the reason for system stagnation over the coast.

Meteorology doi: 10.3390/meteorology2030019

Authors: Felipe M. de Andrade Victor A. Godoi José A. Aravéquia

El Niño is generally associated with negative rainfall anomalies (below-average rainfall) in northern Northeast Brazil (NNEB). In 2019, however, the opposite rainfall pattern was observed during an El Niño episode. Here, we explore the mechanisms that overwhelmed typical El Niño-related conditions and resulted in positive rainfall anomalies (above-average rainfall) in NNEB. We focus on the austral autumn when El Niño is most prone to rainfall anomalies in the region. The analysis of several datasets, including weather station data, satellite data, reanalysis data, and modelled data derived from a dry linear baroclinic model, allowed us to identify that the austral autumn 2019 above-average rainfall in NNEB was likely associated with four combined factors; these are (1) the weak intensity of the 2019 El Niño; (2) the negative phase of the Atlantic Meridional Mode; (3) local and remote diabatic heating anomalies, especially over the western South Pacific and tropical South Atlantic, which resulted in anticyclonic and cyclonic circulations in the upper and lower troposphere, respectively, over the tropical South Atlantic; and (4) sub-seasonal atmospheric convection anomalies over the western South Pacific, which reinforced the low-frequency convection signal over that region. This latter factor suggests the influence of the Madden–Julian Oscillation on rainfall in NNEB during the first ten days of March 2019. We discuss these mechanisms in detail and provide evidence that, even during an El Niño event, above-average rainfall in NNEB in the austral autumn may occur, and its modulation is not limited to the influence of a single climate phenomenon. Our results may assist in the planning of several crucial activities, such as water resources management and agriculture.

Meteorology doi: 10.3390/meteorology2030018

Authors: Rae-Seol Park Song-You Hong

In atmospheric models, the representation of cloudiness is a direct linkage between the moisture amount and associated radiative forcing. This paper begins by providing a review of the parameterization of cloudiness that has been used for numerical weather predictions and climate studies. The inherent uncertainties in representing a partial fraction of clouds for radiation feedback and in evaluating it against the corresponding observations are focused. It is also stated that the major hydrometeor categories of water substances such as cloud ice and water that are responsible for cloud cover are readily available in modern weather and climate models. Inconsistencies in cloud cover and hydrometeors, even in the case of the prognostic method, are discussed. The compensating effect of cloudiness for radiative feedback is found to imply that the condensed water amount itself is more influential on the radiative forcing, rather than the accuracy of the cloudiness. Based on the above perspectives, an alternative diagnostic parameterization method is proposed, utilizing a monotonic relation between the cloud water amounts and cloudiness that are obtained from aircraft and satellite observations. The basic premise of this approach lies in the accuracy of the water substance in the models, indicating that future efforts need to be given to improvements in physical processes concerning hydrometeor properties for the accurate representation of cloud radiative feedback.

Meteorology doi: 10.3390/meteorology2020017

Authors: Nicholas John Cook

Most damage to buildings across the contiguous United States, in terms of number and total cost, is caused by gusts in convective events associated with thunderstorms. Their assessment relies on the integrity of meteorological observations. This study examines the impact on risk due to valid gust observations culled erroneously by the real-time quality control algorithm of the US Automated Surface Observation System (ASOS) after 2013. ASOS data before 2014 are used to simulate the effect of this algorithm at 450 well-exposed stations distributed across the contiguous USA. The peak gust is culled in around 10% of these events causing significant underestimates of extreme gusts. The full ASOS record, 2000–2021, is used to estimate and map the 50-year mean recurrence interval (MRI) gust speeds, the conventional metric for structural design. It is concluded that recovery of erroneously culled observations is not possible, so the only practical option to eliminate underestimation is to ensure that the 50-year MRI gust speed at any given station is not less than the mean for nearby surrounding stations. This also affects stations where values are legitimately lower than their neighbors, which represents the price that must be paid to eliminate unacceptable risk.

Meteorology doi: 10.3390/meteorology2020016

Authors: Günther Heinemann Lukas Schefczyk Rolf Zentek Ian M. Brooks Sandro Dahlke Andreas Walbröl

Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.

Meteorology doi: 10.3390/meteorology2020015

Authors: Thomas A. Andretta

A heuristic technique for tornado forecasting in the complex terrain of southern Wyoming is proposed for the weather sciences community. This novel approach is based on seasonal tornado climatology and observed mesoscale conditions obtained from in-situ surface and Doppler weather radar sources. The methodology is applied to four severe thunderstorm events which formed tornadoes during the spring and summer months of 2018 and 2019 in Albany County of Wyoming. Tornadic evolution is associated with supercell thunderstorms forming along moisture convergence axes of a dryline and updraft interactions with air mass stretching and shearing over the complex terrain. Applying Bayes’ theorem to each case, there is a low to high (30 to 80%) posterior probability associated with vortex detection.

Meteorology doi: 10.3390/meteorology2020014

Authors: Matej Ogrin Domen Svetlin Sašo Stefanovski Barbara Lampič

Although the urban heat island (UHI) phenomenon is more commonly studied in summer, its influence is also important in winter. In this study, the authors focused on the winter UHI in Ljubljana (Slovenia) and its impact on the urban population, as well as in comparison with a UHI study from 2000. Through a combination of mobile and stationary temperature measurements in different parts of the city, the winter intensity of the UHI in Ljubljana was studied in a dense spatial network of measurements. It was found that the intensity of the winter UHI in Ljubljana decreases as winters become warmer and less snowy. The results showed that the winter UHI in Ljubljana intensifies during the night and reaches the greatest intensity at sunrise. During the winter radiation type of weather, the warmest part of Ljubljana reaches an intensity of 3.5 °C in the evening. In total, 22% of the urban area is in the evening UHI intensity range of 2–4 °C, and 65% of the urban population lives in this range. In the morning, the UHI in Ljubljana has a maximum intensity of 5 °C. The area of >4 °C UHI intensity covers 7% of the urban area, and 28% of the total urban population lives in this area. Higher temperatures in urban centers in winter lead to a longer growing season, fewer snow cover days, lower energy consumption and cold stress, and lower mortality from cold-related diseases compared to the colder periphery.

Meteorology doi: 10.3390/meteorology2020013

Authors: Christopher J. Slocum Richard K. Taft James P. Kossin Wayne H. Schubert

Just before making landfall in Puerto Rico, Hurricane Maria (2017) underwent a concentric eyewall cycle in which the outer convective ring appeared robust while the inner ring first distorted into an ellipse and then disintegrated. The present work offers further support for the simple interpretation of this event in terms of the non-divergent barotropic model, which serves as the basis for a linear stability analysis and for non-linear numerical simulations. For the linear stability analysis the model’s axisymmetric basic state vorticity distribution is piece-wise uniform in five regions: the eye, the inner eyewall, the moat, the outer eyewall, and the far field. The stability of such structures is investigated by solving a simple eigenvalue/eigenvector problem and, in the case of instability, the non-linear evolution into a more stable structure is simulated using the non-linear barotropic model. Three types of instability and vorticity rearrangement are identified: (1) instability across the outer ring of enhanced vorticity; (2) instability across the low vorticity moat; and (3) instability across the inner ring of enhanced vorticity. The first and third types of instability occur when the rings of enhanced vorticity are sufficiently narrow, with non-linear mixing resulting in broader and weaker vorticity rings. The second type of instability, most relevant to Hurricane Maria, occurs when the radial extent of the moat is sufficiently narrow that unstable interactions occur between the outer edge of the primary eyewall and the inner edge of the secondary eyewall. The non-linear dynamics of this type of instability distort the inner eyewall into an ellipse that splits and later recombines, resulting in a vorticity tripole. This type of instability may occur near the end of a concentric eyewall cycle.

Meteorology doi: 10.3390/meteorology2020012

Authors: Johnny C. L. Chan

This paper presents the latest analyses and integrates results of many past studies on the spatial and temporal variations of the annual frequency and intensity of tropical cyclones (TCs) making landfall along different areas of the East Asian (EA) coast. Future projections of such variations based on the past investigations are also presented. No statistically significant trend in the number of landfalling TCs could be identified in most of the EA coastal regions, except for an increasing one in Vietnam and a decreasing one in South China. Multi-decadal as well as interannual variations in the frequency of landfalling TCs are prevalent in almost all the EA coastal regions. Only TCs making landfall in Vietnam and the Korean Peninsula showed an increase in landfall intensity, with no trend in the other regions. Nevertheless, more intense landfalling TCs were evident in most regions during the past two decades. Multidecadal variations were not observed in some regions although interannual variations remained large. Various oscillations in the atmospheric circulation and the ocean conditions can largely explain the observed changes in the frequency and intensity of landfalling TCs in different regions of the EA coast. In the future, most climate models project a decrease in the number of TCs making landfall but an increase in the intensity of these TCs in all the EA coastal regions, especially for the most intense ones.