Meteorology

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. Full article

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. Full article

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). Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 ($\mathsf{\Delta }x=0.1^{\circ }$, $\mathsf{\Delta }t=0.5$ 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article