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.

Meteorology doi: 10.3390/meteorology2020011

Authors: Wayne Schubert Richard Taft Christopher Slocum

This review discusses a simple family of models capable of simulating tropical cyclone life cycles, including intensification, the formation of the axisymmetric version of boundary layer shocks, and the development of an eyewall. Four models are discussed, all of which are axisymmetric, f-plane, three-layer models. All four models have the same parameterizations of convective mass flux and air–sea interaction, but differ in their formulations of the radial and tangential equations of motion, i.e., they have different dry dynamical cores. The most complete model is the primitive equation (PE) model, which uses the unapproximated momentum equations for each of the three layers. The simplest is the gradient balanced (GB) model, which replaces the three radial momentum equations with gradient balance relations and replaces the boundary layer tangential wind equation with a diagnostic equation that is essentially a high Rossby number version of the local Ekman balance. Numerical integrations of the boundary layer equations confirm that the PE model can produce boundary layer shocks, while the GB model cannot. To better understand these differences in GB and PE dynamics, we also consider two hybrid balanced models (HB1 and HB2), which differ from GB only in their treatment of the boundary layer momentum equations. Because their boundary layer dynamics is more accurate than GB, both HB1 and HB2 can produce results more similar to the PE model, if they are solved in an appropriate manner.

Meteorology doi: 10.3390/meteorology2010010

Authors: Edoardo Bucchignani

The importance of meteorological events is felt in everyday life and the critical impact of the weather on human activities has led to the development of the science of weather forecasting [...]

Meteorology doi: 10.3390/meteorology2010009

Authors: Károly Tar Andrea Bíróné Kircsi

In addition to dynamic methods, purely statistical models, i.e., findings from the statistical analysis of the existing measured database, also play an important role in predicting the different characteristics of climate elements. In our article, we try to estimate the monthly amount of global radiation in each day of the month. In our previous articles, we presented the sliding-average model developed for estimating the average or amount of a climatic element, measured over a time interval, from within the interval. A version of this model for estimating the end-of-interval sums, the sliding-sum model, was used to estimate the amount of monthly global radiation. After generating the characteristics required for the estimation and analyzing their properties, we examined the errors of the performed estimation. Our model can also help solar energy users create the schedule.

Meteorology doi: 10.3390/meteorology2010008

Authors: Paul Joe GyuWon Lee Kwonil Kim

The Women’s Slope Style event of 11–12 February 2018 at the PyeongChang 2018 Olympic Winter Games posed considerable challenges to the competitors and decision-makers, requiring sub-kilometer and sub-minute weather predictions in complex terrain. The gusty wind conditions were unfair and unsafe as the competitors could not achieve sufficient speed to initiate or complete their jumps. The term micro-nowcasting is used here to reflect the extreme high-resolution nature of these science and service requirements. The World Meteorological Organization has conducted several research development and forecast demonstration projects to advance, accelerate and promote the art of nowcasting. Data from compact automatic weather stations, located along the field of play, reported every minute and were post-processed using time series, Hovmöller and wavelet transforms to succinctly present the information. The analyses revealed dominant frequencies of about 20 min, presumed to be associated with vortex shedding from the mountain ridges, but were unable to directly capture the gusts that affected the competitors. The systemic challenges from this and previous projects are reviewed. They include the lack of adequate scientific knowledge of microscale processes, gaps in modeling, the need for post-processing, forecast techniques, managing ever-changing service requirements and highlights the role of observations and the critical role of the forecaster. These challenges also apply to future high-resolution operational weather and warning services.

Meteorology doi: 10.3390/meteorology2010007

Authors: Juerg Schmidli Julian Quimbayo-Duarte

Thermally driven local winds are ubiquitous in deep Alpine valleys during fair weather conditions resulting in a unique wind climatology for any given valley. The accurate forecasting of these local wind systems is challenging, as they are the result of complex and multi-scale interactions. Even more so, if the aim is an accurate forecast of the winds from the near-surface to the free atmosphere, which can be considered a prerequisite for the accurate prediction of mountain weather. This study combines the evaluation of the simulated surface winds in several Alpine valleys with a more detailed evaluation of the wind evolution for a particular location in the Swiss Rhone valley, at the town of Sion during the month of September 2016. Four numerical simulations using the COSMO model are evaluated, two using a grid spacing of 1.1 km and two with a grid spacing of 550 m. For each resolution, one simulation is initialised with the soil moisture from the COSMO analysis and one with an increased soil moisture (+30%). In a first part, a comparison with observations from the operational measurement network of MeteoSwiss is used to evaluate the model performance, while, in a second part, data from a wind profiler stationed at Sion airport is used for a more detailed evaluation of the valley atmosphere near the town of Sion. The analysis focuses on 18 valley wind days observed in the Sion region in September 2016. Only the combination of an increased soil moisture and a finer grid spacing resulted in a significant improvement of the simulated flow patterns in the Sion region. This includes a stronger and more homogeneous along-valley wind in the Wallis and a more realistic cross-valley wind and temperature profile near the town of Sion. It is shown that the remaining differences between the observed and simulated near-surface wind are likely due to very local topographic features. Small-scale hills, not resolved on even the finer model grid, result in a constriction of the valley cross section and an acceleration of the observed low-level up-valley wind in the region of Sion.

Meteorology doi: 10.3390/meteorology2010006

Authors: Pedro Silva Miguel Carmo João Rio Ilda Novo

The length of the fire season has not garnered much attention within the broad field of meteorological research on fire regime change. Fire weather research on the Iberian Peninsula is no exception in this case; there is no solid understanding on fire season lengthening in Portugal, although recent decades do suggest ongoing transitions. Based on a complete record of fire occurrence and burned area between 1980 and 2018, we first searched for consistent trends in the monthly distribution of fire activity. To determine day-scale changes, an exceedance date method based on annual cumulative burned area was developed. Results show an early onset of fire activity in a range of 23–50 days and no significant extension into autumn, suggesting that existing projections of the lengthening of the fire season in Portugal over the present century have been already achieved. Fire weather results show a trend in the cumulative Daily Severity Rating (DSR), with the last two decades (2000–2018) displaying an early build-up of meteorological fire danger in late spring and early summer. The detailed spatio-temporal analysis based on the daily Fire Weather Index (FWI) shows that June stands out with the largest increase (year-round) in days per month with an FWI above 38.3, the threshold above which fire conditions make suppression uncertain. This aggravated fire weather is likely sustaining early fire activity, thus contributing to a longer critical fire season.

Meteorology doi: 10.3390/meteorology2010005

Authors: Meteorology Editorial Office Meteorology Editorial Office

High-quality academic publishing is built on rigorous peer review [...]

Meteorology doi: 10.3390/meteorology2010004

Authors: Man-Lok Chong Hon-Yin Yeung Kai-Kwong Hon

Temperatures over Hong Kong have shown a marked increasing trend since the 1970s due to global warming and urbanization, but outbreaks of intense winter monsoon can bring very low temperatures in Hong Kong at times. This study aims at establishing criteria of extreme cold surges that suit the climatological characteristics of Hong Kong. Surges in this study were selected through percentile ranking of three weather attributes of each cold event: the lowest temperature, the largest temperature drop and the maximum sustained wind speed. Out of 152 cold events in 1991–2020, only four significant cold events in 1991, 1993, 2010 and 2016 met the most extreme 10th percentile of the three attributes concurrently and could be classified operationally as “extreme cold surge”. Very cold temperatures (at or below 7.0 °C), a temperature drop of at least 8.0 °C in two days and gale force wind speed (at or above 17.5 m/s) were recorded in all four surges. The results of classification are illustrated by selected cases. As ensemble products of some numerical weather prediction models tend to have a stable indication of extremity of cold events, the potential applications of cross-referencing the forecast and actual extremity in operational forecasting are also discussed.

Meteorology doi: 10.3390/meteorology2010003

Authors: Kinga Kulesza

The paper aims to analyse the relationship between the amount of global solar radiation (GSR) reaching the Earth’s surface in Poland and the direction of air mass advection, using 72-h backward trajectories (1986–2015). The study determined average daily sums of GSR related to groups of trajectories with certain similarities in shape. It was found that the average daily sums of GSR during air mass inflow from all the directions (clusters) identified were significantly different from the average daily sum in the multi-year period. A significant increase in the amount of GSR over Poland is accompanied by air mass inflow from the north and east. The frequency of these advection directions is 27% of all days. The western directions of advection prompt different GSR sums: from slightly increased during advection from the north-west, to significantly decreased during advection from the west (from the central and western part of the North Atlantic). Special attention was given to days with extremely large (above the 0.95 percentile) and with the largest (above the 0.99 percentile) GSR sums. These are prompted by two main types of synoptic conditions: the Azores High ridge covering Central and Southern Europe; and the high-pressure areas which appear in Northern and Central Europe.

Meteorology doi: 10.3390/meteorology2010002

Authors: Telmo Cosme A. Sumila Simone E. T. Ferraz Angelica Durigon

Unlike global and regional assessments, the spatio-temporal variability of air temperature and precipitation, caused by climate change, must be more useful when the assessment is made at the sub-regional to local scale. Thus, this study aims to assess the possible changes in air temperature and precipitation in patterns for the late 21st century relative to the present climate in Mozambique. The regional model, RegCM4, driven by the global model HadGEM2, was used to perform the downscaling process under two Representative Concentration Pathways (RCPs), moderate RCP4.5 and strong RCP8.5. The three experiments were analyzed in the baseline (1971–2000) and future (2070−2099) range at the subregional scale in Mozambique. In this study domain, the highest amounts of precipitation and the highest air temperatures are observed during the extended summer season. However, the central region is rather warmer and rainier than the northern- and southernmost regions. Hence, the regional model RegCM4 demonstrated agreement relative to the observed weather stations and interpolated dataset from the Climate Research Unit. The strong performance of RegCM4 is revealed by its more realistic local spatio-temporal climate features, tied to the topography and geographical location of the study domain. The future increases in mean annual air temperature are well simulated by the model but, the spatial distribution and magnitude differ between the RCPs and over each of the three regions throughout the country. The sharp hottest response at the end of 21st century occurs in the summer and spring seasons under RCP8.5, spatially over the central and northern region of the study domain, with a hot-spot in the southern region. There is a predominantly drier response in the annual mean precipitation but, during the summer season, a meridional dipolarization pattern is observed, with the wettest response being over the southernmost region and a drier response in the northern and central regions of Mozambique.

Meteorology doi: 10.3390/meteorology2010001

Authors: Renata Barros Vasconcelos Leirias Natalia Fedorova Vladimir Levit

Some meteorological phenomena in South America develop quickly and take on large dimensions. These phenomena cause disasters for aviation, such as incidents and accidents. Mesoscale convective complexes (MCCs) forced a commercial airplane into an emergency landing at Ezeiza International Airport in Buenos Aires (Argentina) in October 2018. The airplane took off from São Paulo (Brazil) to Santiago (Chile) and had to alternate to Ezeiza after encountering unanticipated agglomerations of MCCs along the flight route; its structure was seriously damaged, which affected the safety of the flight. A synoptic and thermodynamic analysis of the atmosphere, prior to the event, was made based on GOES16 infrared satellite data, radiosonde data, maps of several variables such as stream lines, temperature advection, surface synoptic maps and layer thickness from CPTEC/INPE and NCEP reanalysis data. The main observed processes that influenced the formation and development of conglomerates of MCCs were the following: (1) the cyclogenesis of a baroclinic cyclone on the cold front; (2) the coupling of subtropical and polar jet streams; (3) the advection of warm and humid air along a low-level jet stream. Recommendations for meteorologists in weather forecasting and for aviators in flight safety were prepared.

Meteorology doi: 10.3390/meteorology1040032

Authors: Swapan Mallick

Assimilation of cloud properties in the convective scale ensemble data assimilation system is one of the prime topics of research in recent years. Satellites can retrieve cloud properties that are important sources of information of the cloud and atmospheric state. The Advance Baseline Imager (ABI) aboard the GOES-16 geostationary satellite brings an opportunity for retrieving high spatiotemporal resolution cloud properties, including cloud water path over continental United States. This study investigates the potential impacts of assimilating adaptively thinned GOES-16 cloud water path (CWP) observations that are assimilated by the ensemble-based Warn-on-Forecast System and the impact on subsequent weather forecasts. In this study, for CWP assimilation, multiple algorithms have been developed and tested using the adaptive-based thinning method. Three severe weather events are considered that occurred on 19 July 2019, 7 May and 21 June 2020. The superobbing procedure used for CWP data smoothed from 5 to 15 km or more depending on thinning algorithm. The overall performance of adaptively thinned CWP assimilation in the Warn-on-Forecast system is assessed using an object-based verification method. On average, more than 60% of the data was reduced and therefore not used in the assimilation system. Results suggest that assimilating less than 40% of CWP superobbing data into the Warn-on-Forecast system is of similar forecast quality to those obtained from assimilating all available CWP observations. The results of this study can be used on the benefits of cloud assimilation to improve numerical simulation.

Meteorology doi: 10.3390/meteorology1040031

Authors: Moses B. Farr James V. Gasch Evan J. Travis Sarah M. Weaver Veli Yavuz Inna G. Semenova Oleksandr Panasiuk Anthony R. Lupo

In the Mediterranean and occasionally in the Black Sea, low-pressure systems with the character of both mid-latitude and tropical cyclones can form. These hybrid storms are called subtropical storms, subtropical depressions, medistorms/medicanes, or tropical-like cyclones (TLC). A strong low-pressure system given the name Falchion developed in northern part of the Black Sea during 11–20 August 2021. This storm was blamed for damage and more than 30 casualties in the nations bordering the region. At peak intensity, this storm was a as strong as a tropical depression. Falchion developed and moved northeast, reaching peak intensity before becoming nearly stationary. The NCEP reanalyses and satellite data obtained from Eumetsat’s geostationary satellite, Meteosat-8, were used to examine the character of the storm. This study demonstrates that the movement of Falchion was impeded by a blocking event that occurred over central Asia during much of August 2021. The storm did share characteristics with tropical systems, but a comparison of Falchion to tropical depressions and subtropical storms in the North and South Atlantic demonstrated that this storm was more consistent with these types of storms when examining the storm and the proximal environment. This included an examination of integrated water vapor (IVT) plumes, and the plume associated with Falchion did rise to the character of an atmospheric river in spite of the smaller scale.

Meteorology doi: 10.3390/meteorology1040030

Authors: Zdeněk Janků Petr Dobrovolný

This study used homogenised mean, maximum, and minimum daily temperatures from 12 stations located in Brno, Czechia, during the 2011–2020 period to analyse heat waves (HW) and their impact on the canopy urban heat island (UHI). HWs were recognized as at least three consecutive days with Tx ≥ 30 °C and urban–rural and intra-urban differences in their measures were analysed. To express the HWs contribution to UHI, we calculated the UHI intensities (UHII) separately during and outside of HWs to determine the heat magnitude (HM). Our results show that all HW measures are significantly higher in urban areas. UHII is mostly positive, on average 0.65 °C; however, day-time UHII is clearly greater (1.93 °C). Furthermore, day-time UHII is amplified during HWs, since HM is on average almost 0.5 °C and in LCZ 2 it is even 0.9 °C. Land use parameters correlate well with UHII and HM at night, but not during the day, indicating that other factors can affect the air temperature extremity. Considering a long-term context, the air temperature extremity has been significantly increasing recently in the region, together with a higher frequency of circulation types that favour the occurrence of HWs, and the last decade mainly contributed to this increase.

Meteorology doi: 10.3390/meteorology1040029

Authors: Lisa Thalheimer Dorothy Heinrich Karsten Haustein Roop Singh

Populations around the world have already experienced the increasing severity of extreme weather causing disaster displacement. Anthropogenic climate change can intensify these impacts. Extreme event attribution studies center around the question of whether impactful extreme events could have occurred in a pre-industrial climate. Here, we argue that the next step for attribution science is to focus on those most vulnerable populations to future extremes and impacts from climate change. Up until now, the vulnerability dimension has not been systematically addressed in attribution studies, yet it would add urgently needed context, given the vast differences in adaptive capacity. We propose three integrative points to cascade disaster displacement linked to anthropogenic climate change.

Meteorology doi: 10.3390/meteorology1040028

Authors: Erzsébet Kristóf

In this study, a pattern detection method is applied on the RCP4.5 and RCP8.5 simulation outputs of seven GCMs—disseminated by the Coupled Model Intercomparison Project Phase 5 (CMIP5)—to determine whether atmospheric teleconnection patterns detected in the ERA-20C reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will be observable in the future projections of the CMIP5 GCMs. The pattern detection technique—which combines the negative extrema method and receiver operating characteristic (ROC) curve analysis—is used on the geopotential height field at the 500 hPa pressure level in wintertime, in the Northern Hemisphere. It was found that teleconnections obtained from the ERA-20C reanalysis dataset for the period of 1976–2005 remain observable in the majority of the GCM outputs under the RCP4.5 and RCP8.5 scenarios for the periods of 2006–2035, 2021–2050, and 2071–2100. The results imply that atmospheric internal variability is the major factor that controls the teleconnections rather than the impact of radiative forcing.

Meteorology doi: 10.3390/meteorology1040027

Authors: Shaun Lovejoy

Since the first climate models in the 1970s, algorithms and computer speeds have increased by a factor of ≈1017 allowing the simulation of more and more processes at finer and finer resolutions. Yet, the spread of the members of the multi-model ensemble (MME) of the Climate Model Intercomparison Project (CMIP) used in last year’s 6th IPCC Assessment Report was larger than ever: model uncertainty, in the sense of MME uncertainty, has increased. Even if the holy grail is still kilometric scale models, bigger may not be better. Why model structures that live for ≈15 min only to average them over factors of several hundred thousand in order to produce decadal climate projections? In this commentary, I argue that alongside the development of “seamless” (unique) weather-climate models that chase ever smaller—and mostly irrelevant—details, the community should seriously invest in the development of stochastic macroweather models. Such models exploit the statistical laws that are obeyed at scales longer than the lifetimes of planetary scale structures, beyond the deterministic prediction limit (≈10 days). I argue that the conventional General Circulation Models and these new macroweather models are complementary in the same way that statistical mechanics and continuum mechanics are equally valid with the method of choice determined by the application. Candidates for stochastic macroweather models are now emerging, those based on the Fractional Energy Balance Equation (FEBE) are particularly promising. The FEBE is an update and generalization of the classical Budyko–Sellers energy balance models, it respects the symmetries of scaling and energy conservation and it already allows for both state-of-the-art monthly and seasonal, interannual temperature forecasts and multidecadal projections. I demonstrate this with 21st century FEBE climate projections for global mean temperatures. Overall, the projections agree with the CMIP5 and CMIP6 multi-model ensembles and the FEBE parametric uncertainty is about half of the MME structural uncertainty. Without the FEBE, uncertainties are so large that climate policies (mitigation) are largely decoupled from climate consequences (warming) allowing policy makers too much “wiggle room”. The lower FEBE uncertainties will help overcome the current “uncertainty crisis”. Both model types are complementary, a fact demonstrated by showing that CMIP global mean temperatures can be accurately projected using such stochastic macroweather models (validating both approaches). Unsurprisingly, they can therefore be combined to produce an optimum hybrid model in which the two model types are used as copredictors: when combined, the various uncertainties are reduced even further.

Meteorology doi: 10.3390/meteorology1040026

Authors: Jimy Dudhia

At multi-kilometer grid scales, numerical weather prediction models represent surface-based convective eddies as a completely sub-grid one-dimensional vertical mixing and transport process. At tens of meters grid scales, large-eddy simulation models, explicitly resolve all the primary three-dimensional eddies associated with boundary-layer transport from the surface and entrainment at the top. Between these scales, at hundreds of meters grid size, is a so-called grey zone in which the primary transport is neither entirely sub-grid nor resolved, where explicit large-eddy models and sub-grid boundary-layer parameterization models fail in different ways that are outlined in this review article. This article also reviews various approaches that have been taken to span this gap in the proper representation of eddy transports in the sub-kilometer grid range using scale-aware approaches. Introduction of moisture with condensation in the eddies expands this problem to that of handling shallow convection, but similarities between dry and cloud-topped convective boundary layers can lead to some unified views of the processes that need to be represented in convective boundary-layers which will be briefly addressed here.

Meteorology doi: 10.3390/meteorology1040025

Authors: Joshua Chun Kwang Lee Huqiang Zhang Dale Melvyn Barker Song Chen Rajesh Kumar Byoung Woong An Kuldeep Sharma Krishnamoorthy Chandramouli

Singapore is a tiny city-state located in maritime Southeast Asia. Weather systems such as localized thunderstorms, squalls, and monsoon surges bring extreme rainfall to Singapore, influencing the day-to-day conduct of stakeholders in many sectors. Numerical weather prediction models can provide forecast guidance, but existing global models struggle to capture the development and evolution of the small-scale and transient weather systems impacting the region. To address this, Singapore has collaborated with international partners and developed regional numerical weather prediction systems. Steady progress has been made, bringing added value to stakeholders. In recent years, complex earth system and ultra high-resolution urban models have also been developed to meet increasingly diverse stakeholder needs. However, further advancement of weather prediction for Singapore is often hindered by existing challenges, such as the lack of data, limited understanding of underlying processes, and geographical complexities. These may be viewed as opportunities, but are not trivial to address. There are also other opportunities that have remained relatively unexplored over Singapore and the region, such as the integration of earth system models, uncertainty estimation and machine learning methods. These are perhaps key research directions that Singapore should embark on to continue ensuring value for stakeholders.

Meteorology doi: 10.3390/meteorology1040024

Authors: Isidora Jankov Zoltan Toth Jie Feng

Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve.

Meteorology doi: 10.3390/meteorology1040023

Authors: Peter Lynch

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Meteorology doi: 10.3390/meteorology1040022

Authors: Hein Dieter Behr

This study characterizes the spatiotemporal solar radiation and air temperature patterns and their dependence on the general atmospheric circulation characterized by the North Atlantic Oscillation (NAO) Index in Germany from 1991 to 2015. Germany was selected as the study area because it can be subdivided into three climatologically different regions: the North German lowlands are under the maritime influence of the North and Baltic Seas. Several low mountain ranges dominate Germany’s center. In the south, the highest low mountain ranges and the Alps govern solar radiation and air temperature differently. Solar radiation and air temperature patterns were studied in the context of the NAO index using daily values from satellite and ground measurements. The most significant long-term solar radiation increase was observed in spring, mainly due to seasonal changes in cloud cover. Air temperature shows a noticeable increase in spring and autumn. Solar radiation and air temperature were significantly correlated in spring and autumn, with correlation coefficient values up to 0.93. In addition, a significant dependence of solar radiation and air temperature on the NAO index was revealed, with correlation coefficient values greater than 0.66. The results obtained are important not only for studies on the climate of the study area but also for photovoltaic system operators to design their systems. They need to be massively expanded to support Germany’s climate neutrality ambitions until 2045.