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We performed a plateau‐linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off‐farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau‐linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau‐linear RNM was lower than that for a simple repeatability model (RM). With the plateau‐linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be −0.52 from the plateau‐linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau‐linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient. [ABSTRACT FROM AUTHOR]

ABSTRACT In the North China Plain (NCP), the assessment of water surplus or deficit (WSD), which is calculated as precipitation minus reference evapotranspiration (ET0), holds significant implications for water resource management and agricultural irrigation decision‐making, given the region's long‐standing severe shortage of water resources. However, the magnitude, trend and climatic drivers of WSD remain poorly understood in the NCP. This study analysed the spatial and temporal characteristics of WSD, and quantified the contribution of climatic factors to WSD based on the sensitivity and contribution rate analysis methods with climatic data from 75 meteorological stations. The result showed that: (1) Annual WSD decreased mainly in northeastern NCP and increased significantly in southern NCP during 1961–2022. Annual WSD increased slightly from 1961 to 2022 at a rate of 1.63 mm a−2 mainly due to the more significant decrease (−1.88 mm a−2) in ET0 compared to precipitation (−0.25 mm a−2). (2) In terms of the sensitivity of WSD to climatic factors, relative humidity had the highest sensitivity, followed by net radiation, wind speed, precipitation and average air temperature. (3) Significant declines of wind speed were the most dominant factor affecting WSD variation in most part of NCP during most of a year, and net radiation of four stations in the western high‐elevation regions played the most important role. This study enhances comprehension of the impact of climate change on WSD in the NCP and provides a reference for improving management of agricultural water resources under NCP's evolving climatic conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT This work studies the simultaneous and sequential occurrence of hot and dry months in the summer season in Argentina, north of 40°S, based on three different databases: meteorological stations, a gridded observational dataset and a reanalysis product. The influence of the El Niño‐Southern Oscillation (ENSO) over the occurrence of these compound events is specially analysed using a logistic regression model. Monthly maximum temperature and precipitation data are used for the period 1979–2022 in four sub‐regions of Argentina: Northwestern Argentina (NOA), Northeastern Argentina (NEA), Cuyo (central‐western Argentina) and the Pampas (central‐eastern Argentina). Simultaneous hot and dry months and hot months preceded by dry months are the most frequent compound events. The highest frequencies are found in the centre part of the study region and NEA for simultaneous compound events, and in NOA and the Pampas region for sequential ones. In general terms, all datasets show a good representation of the spatio‐temporal variability of hot and dry months. The insights of the influence of ENSO on compound events revealed that La Niña enhances the occurrences of hot and dry months throughout the study region, with the exception of NOA, where El Niño conditions promote the occurrence of these events. Based on logistic regression models, we successfully quantify the relationship between ENSO and hot and dry months and demonstrate that ENSO plays a significant role as a driver of compound hot and dry events in the central region, Cuyo, NEA and a portion of the Pampas. This research contributes to the understanding of compound events in Argentina and how they are influenced by major drivers of climate variability providing useful information for the development of a predictive system for such events. [ABSTRACT FROM AUTHOR]

The article examines the risk, attributable fraction, and attributable number of cause-specific heat-related emergency hospital admissions in Switzerland from 1998 to 2019. It includes figures showing lag-specific risk ratios, exposure-response functions by age and gender, and sensitivity analyses for different temperature variables. Additionally, a table lists weather stations used in the study by canton, detailing median, minimum, and maximum daily maximum temperatures. [Extracted from the article]

ABSTRACT In this study, the characteristics of tropical cyclones (TCs) over the Bay of Bengal (BoB) that affect snowfall on the Tibetan Plateau (TP) and spatiotemporal distribution of snowfall related to BoB TCs are statistically analysed by using multi‐sources data from 1981 to 2020, with partitioning TC‐influenced snowfall by tracking cloud clusters. The results show that 141 TCs formed during the 40‐year period of 1981–2020, of which about 35% (50 TCs) impacted snowfall at 83% of meteorological stations on the TP during their northward or westward movement, and the average distance between the TC centre and the snowfall stations is 1277 km. The proportion of snowfall‐related TC frequency shows a significantly decreasing trend with a predominant cycle of 10a. The TC‐influenced snowfall frequency (SF), precipitation amount (PA) on a snowfall day and snow depth (SD) during 1981–2020 all show a non‐significant weak decreasing trend, while TC‐influenced snowfall is significantly increased in the eastern and southern edges of Xizang, western Sichuan and the southern margin of Qinghai. PA and SD in December account for more than 75% and 55% of the monthly total, respectively. The spatial pattern of PA could be objectively categorised into west‐type (24%) and southeast‐type (76%). The moisture transported by the BoB TC and a southerly jet stream formed between the trough and the western Pacific subtropical high (WPSH), the convergence of cold air and warm–moist airstream over the TP and the change in position of the south Asian high in the upper troposphere are significant factors causing the different spatial distribution. The results can provide reference for TC‐related snowfall, SD prediction and disaster assessment on the TP. [ABSTRACT FROM AUTHOR]

ABSTRACT Increasing temperatures cause the weather to become more severe. Studying how heatwaves (HWs) impact individual heat exposure and susceptibility is vital for building climate change mitigation and adaptation measures. So, this study explores the impact of HWs on individual heat exposure and susceptibility. The assessment was based on the highly complex topographic region contribution to HWs in the Republic of Armenia (RA) using data from 16 meteorological stations for the extended warm season (May–September). We developed a regional HW catchment program to estimate the HW indices' responses to climatic change and to present them in terms of topography changes. The Mann–Kendall (MK) test was used to determine the statistical significance of the trends for 10 distinct HW indicators using linear and exponential trends along with graphical interpretations. This study reveals a large‐scale, significant increasing trend in annual maximum temperatures (Tmax) and observed HWs over the period 1955–2019. The rising temperature is accompanied by an increase in HW indices, particularly at low altitudes up to 1250 m above sea level (ASL), where the main population centres and national crop production are concentrated. According to our classification, the above‐mentioned areas have faced extreme and severe increases in HW intensity, covering more than 60% of the country's territory. Moreover, not only the intensity and frequency have been identified but the HW period extension as well. This extreme increase has been found in the low‐lying highly populated and intensively cultivated areas, such as in the Ararat valley. These results have implications for future climate assessments, adaptation strategies, agriculture and public health in Armenia. The development of targeted mitigation measures and adaptation strategies informed by these findings is essential for addressing the escalating challenges posed by HWs in the region. [ABSTRACT FROM AUTHOR]

A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily Tmax, Tmin, and Tmean. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations. [ABSTRACT FROM AUTHOR]

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS's performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly. [ABSTRACT FROM AUTHOR]

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context‐specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time‐consuming and often impractical for large‐scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high‐resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen–Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS‐based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally. [ABSTRACT FROM AUTHOR]

Drought is a recurring natural hazard impacting agriculture, water resources, and various socioeconomic sectors. This study evaluated the performances of multiple meteorological and hydrological drought index estimation methods in the Tekeze River basin, northwestern Ethiopia. Monthly rainfall and temperature data from 15 meteorological stations with varying record lengths (28–59 years) and streamflow data from nine functional stations (1991–2018) were used to compute hydrometeorological drought indices. Standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), reconnaissance drought index (RDI), rainfall anomaly index (RAI), decile index (DI), and streamflow drought index (SDI) were calculated using DrinC software, R programming, and empirical formulas at 1, 3, 6, and 12‐month timescales. Results from the analysis revealed a basin‐wide prevalence of mild drought, with SPI indicating severity and negative values over longer timescales. The RDI analyses showed lower drought severity but also decreased its frequency over longer timescales. The DI calculation using basin station averages indicated 48.4%, 10.7%, and 8.0% incidence rates for severe, moderate, and mild droughts, respectively. RAI had generally lower positive and negative values compared to SPI. SPEI‐1 analysis classified almost all stations as experiencing mild drought. Conversely, SDI6 analysis at a 6‐month timescale revealed diverse impacts, with the Tekeze River basin experiencing sustained severe drought (SDI6 < −2.0) for three consecutive years (2004–2006). Spearman's rank correlation coefficients of the indices were positive, with RAI showing particularly strong and statistically significant positive correlations with other indices. This study provides valuable insights for policymakers, researchers, and water resource managers in the Tekeze River basin, aiding in the development of effective drought mitigation and preparedness strategies. [ABSTRACT FROM AUTHOR]

We study the air temperature with 10 years of hourly measured in 12 automatic weather stations, located in the State of Bahia (Brazil), considering mainly its auto‐ and cross‐correlation. We found that, in general, there is a positive linear trend, indicating warming in this variable for all stations. Also, we observe that the probability distribution function clearly exhibits a bimodal distribution, meaning that there are two main seasonal cycles (the largest relating to winter, the other to summer). In order to have a more robust statistical analysis, detrended fluctuation analysis (DFA) method was applied in these time‐series to analyzing data where traditional methods might struggle due to nonstationary. In this case, DFA was useful in identifying long‐range autocorrelations in these time‐series (with persistent and antipersistent behavior) with specific seasonal time‐scales (1 day and 1 year). However, if we quantify the cross‐correlations between the air temperature in Salvador and the other stations, by ρDCCA, we found that detrended cross‐correlation analysis cross‐correlation coefficient is always positive with two peaks, the first is located at n = 24 (1 day) and the second at n = 8766 (1 year). In this sense, ρDCCA coefficient provides insights how air temperature in Salvador is correlated with air temperature in other weather stations over different time‐scales, helping to understand their long‐term interactions. These results, for example, allow us to identify temporal scales that may be useful to better understand trends in the air temperature and that can help us further answer global warming questions. [ABSTRACT FROM AUTHOR]

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region. [ABSTRACT FROM AUTHOR]

Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions. [ABSTRACT FROM AUTHOR]

A drought is a complex event characterized by low rainfall and has negative implications for agricultural and hydrological systems, as well as for community life. A common meteorological drought index used for drought monitoring and water resource management is the Standardized Precipitation Evapotranspiration Index (SPEI). Using SPEI can assist in predicting drought onset and estimating drought severity. The objective of this research is to assess the accuracy of machine learning models in estimating the SPEI-1 (one-month) index in semi-arid climates. To achieve this goal, the data will be analyzed using remote sensing parameters, a worldwide database, and meteorological station information. SPEI-1 was predicted in Tabriz, Iran, between 1990 and 2022 using multilayer perceptron (MLP) and random forest (RF) techniques combined with genetic algorithm (GA) methods. The parameters used are average air temperature, average relative humidity, monthly precipitation, wind speed, sunny hours, as well as the one-month standard precipitation index (SPI-1) (from ground data), daily precipitation products from satellites named PERSIANN (PRC-PR) (from remote sensing), and SPEIbase data (from global databases). The results suggest that the use of satellite remote sensing characteristics and global databases has significantly enhanced the precision and efficiency of prediction models. Based on the GA-RF model with an R2 of 0.992 and an RMSE of 0.124, it exhibits the best performance among all models in Scenario 1. By combining remote sensing parameters, this study presents an innovative approach to predicting the SPEI index and demonstrates their capabilities in drought management and mitigation. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive drought analysis using climate data obtained from 219 homogeneously distributed meteorological stations in Türkiye between 1991 and 2022. In this context, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) drought indices were used and comparative analysis was made. Türkiye. The study demonstrates that below-normal precipitation over extended periods and increasing temperatures have contributed to the increased frequency of meteorological drought events. Türkiye's topographic conditions, particularly its location in the Mediterranean basin, significantly influence drought occurrences. It is noted that over the past 20 years, Türkiye has been trending towards drier conditions, with rising temperatures reinforcing this trend. The study observes that the moderate drought class range is the most frequently recurring in the SPI, SPEI, and RDI methods utilized. Regarding atmospheric conditions affecting the climate in Türkiye, it is observed that increased drought severity stands out prominently in years when the North Atlantic Oscillation is positive. During these years, increased drought severity is evident in the SPI, SPEI, and RDI indices, particularly in winter and autumn, while a wide area experiences drought effects in the summer months. Long-term analyses emphasize that drought periods occur less frequently but have more prolonged impacts, attributed to variations in precipitation patterns from year to year and the influence of rising temperatures due to global climate change. The potential future increase in drought in the Mediterranean basin due to global climate change and Türkiye's vulnerability to this situation could have adverse effects on water resources, food security, energy sources, and ecosystems. [ABSTRACT FROM AUTHOR]

The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs. [ABSTRACT FROM AUTHOR]

Using monthly data from 19 meteorological stations in Guangxi Zhuang Autonomous region from 1961 to 2020, this study calculated the standardized precipitation evapotranspiration index (SPEI) based on the Penman-Monteith evapotranspiration model (SPEI_PM). The temporal and spatial variation of meteorological drought were analyzed via linear tendency estimation, and Mann-Kendall trend test. The correlation between El Niño-Southern Oscillation (ENSO) and meteorological drought in Guangxi was also explored by cross-correlation function, cross wavelet transform and wavelet coherence. The values for summer, autumn, and annual SPEI_PM from 1961 to 2020 exhibited a significant linear trend of increasing wetness over time. Annual and seasonal droughts throughout the region were predominately mild and moderate, with less frequent severe and extreme droughts. Spring droughts were concentrated in the central part of the region, summer droughts in the southwest, autumn droughts in the southeast and northeast, and winter droughts spread throughout the region. Annual droughts were concentrated in the northeast. The rate of drought occurrence as measured by the stations in Guangxi exhibited a distinctly chronological character. Large-scale droughts (especially severe and extreme droughts) across the entire region occurred most often in the 1960s, 1970s, and 2000s. The impact of ENSO on the meteorological drought in Guangxi was characterized by spatial heterogeneity. Correlation between the ocean Niño index (ONI) and meteorological drought was highest in the northeast and southeast, as well as the northwest. There was no significant correlation between drought and ONI in coastal areas and most southwestern parts of the region. As a typical example from meteorological stations in northeastern Guangxi, SPEI-3 and ONI at the Guilin Meteorological station showed interannual oscillation cycles of 16-48 months in the period 1962-1976, and 12-64 months in the period 1990-2019. These results can serve as a useful reference for future drought forecasting and assessment in Guangxi. [ABSTRACT FROM AUTHOR]

Freezing damage in spring is one of the serious agricultural meteorological disasters for winter wheat production in the Huang-Huai-Hai plain. In order to effectively identify freezing damages of winter wheat in spring, this paper used a deep learning long short-term memory model LSTM to identify the occurrences of freezing damages of winter wheat in spring in Henan province, which was an important winter wheat planting area in China. The spatiotemporal distributions and decadal changes of freezing damages of winter wheat in spring based on the multi-source meteorological, crop, and disaster data from 99 meteorological stations from 1981 to 2020 were explored. The results indicated that: (1) the optimized LSTM model effectively identified freezing damages of winter wheat in spring. The daily minimum temperature simulated by LSTM model from 2017 to 2020 showed an average absolute percentage error (MAPE) of 8.73% and a goodness of fit (R2 ) with 0.90 compared to the actual minimum temperature. Based on the disaster data and freezing damage index for winter wheat in spring, the actual disaster situations from 2017 to 2020 were found to be consistent with the simulated results by the optimized LSTM model. (2) From 1981 to 2020, the harm of mild freezing damage to winter wheat in spring in Henan province had gradually weakened, and the high frequency area of mild freezing damage moved from the Northeast to the East. The overall distribution of mild freezing damage of winter wheat in spring in Henan province between 1981 and 2020 was higher in the northeastern Henan, and lower in the southwestern and southeastern Henan, with an average frequency of 0-1.75 times·10y-1 during the past 40 years. The frequency of mild freezing damage of winter wheat in spring decreased per decade, gradually decreasing from 0.843 times per decade to 0.157 times per decade. The high frequency area of mild freezing damage of winter wheat in spring moved from the Northeast to the East. (3) From 1981 to 2020, the frequency of severe freezing damage of winter wheat in spring in Henan province showed a trend of first increasing and then decreasing. The high frequency area of severe freezing damage of winter wheat in spring moved from the Northeast to the East. The overall distribution of severe freezing damage during the 40a period was higher in the East than in the West, and higher in the North than in the South. The average frequency of severe freezing damage in the 40a period was 0-2.75times·10y-1 . The frequency of severe freezing damage of winter wheat in spring increased from 0.508 times per decade to 0.857 times per decade and then decreased to 0.289 times per decade. The high frequency area of severe freezing damage moved from the Northeast to the East. The overall trend of severe freezing damage of winter wheat in spring in Henan Province was decreasing, but the frequency of extreme weather events under climate warming was increasing. Strengthening the researches on the impacts of major agricultural meteorological disasters on agricultural production in various regions in China under climate change was still one of the future research focuses. Various accuracy evaluation indicators of the deep learning LSTM model for identifying freezing damage of winter wheat in spring proposed in this study were improved, and the simulation results of freezing damage by LSTM model were basically consistent with the actual occurrences. Our results can provide ideas and methods for large-scale freezing damage identification of winter wheat under global climate change, and also have certain reference values for other agricultural meteorological disaster identification. [ABSTRACT FROM AUTHOR]

The hourly average temperature and average relative humidity data of 260 meteorological stations in Chongqing from February to June in 2019-2023 were used to simulate the geographic distribution of spring potato late blight infection risk using the CARAH late blight model. The accuracy of the simulation was tested by using the late blight infection data of 26 monitoring stations in Wuxi county, Chongqing in 2022. Based on the geographic distribution of spring potato late blight infection risk simulated, the maximum entropy model was constructed using the climate grid data of the monthly average temperature, maximum temperature, minimum temperature, water vapor pressure and precipitation from February to June in 1970-2000 to analyze the climate impact factors of spring potato late blight in Chongqing, and to evaluate the climate risk of spring potato late blight, providing a reference for the prediction and scientific prevention of the disease. The results showed that simulations of late blight infection based on hourly weather data had high accuracy, with a false positive rate of 12.5%, false negative rate of 18.5% and TS score of 0.73. The mean area under curve (AUC) of the receiver operating characteristic (ROC) was above 0.9, indicating higher accuracy of the simulation results. Precipitation was the dominant climate factor affecting the risk distribution of late blight of spring potato in Chongqing, while relative humidity and temperature were important climate factors. Climate variables at the seedling stage and bud flowering stage had a great impact on the distribution of late blight risk. The low risk area of late blight of each maturity(early/late) and susceptibility (resistant/susceptible) combination of spring potato was less than or close to 10000km², with an average area proportion of 10.2%. The medium risk area and high risk area were both more than 30000km², with an average area proportion of 43.7% and 46.1%, respectively. The climate risk of spring potato late blight showed a spatial distribution characteristic of "high in the middle and low in the periphery" in Chongqing. The high risk area was concentrated in the parallel valley area in eastern Sichuan, the medium risk area was mainly distributed in Daba mountain area in northeast Chongqing, Wuling mountain area in southeast Chongqing, and the hilly area in central Sichuan in west Chongqing, and the low risk area was scattered in the fringe of Chongqing. Spring potato production in Chongqing area faces a high climate risk of late blight, with significant spatial differentiation characteristics. It should be addressed through reasonable production layout and improved cultivation techniques. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022. [ABSTRACT FROM AUTHOR]

The Normalized Differential Spectral Attenuation (NDSA) technique was proposed years ago as an active method for measuring integrated water vapor (IWV) along a Ku/K-band radio link immersed (totally or partially) in the troposphere. The approach is of the active kind, as it relies on the transmission of a couple of sinusoidal signals, whose power is measured at the receiver, thus providing the differential attenuation measurements from which IWV estimates can be in turn derived. In 2018, a prototype instrument providing such differential attenuation measurements was completed and set up for a first measurement campaign aimed at demonstrating the NDSA method. By the end of June 2022, the instrument was profoundly modified and upgraded so that a second measurement campaign could be carried out from 1 August to 30 November 2022. The transmitter was placed on the top of Monte Gomito (44.1277°lat, 10.6434°lon, 1892 m a.s.l.) and the receiver on the roof of the Department of Information Engineering of the University of Florence (43.7985°lat, 11.2528°lon, 50 m a.s.l.). The resulting radio link length was 61.15 km. Four ground weather stations of the regional weather service were selected among those available. In this paper, we describe the upgraded instrument and present the outcomes of the new measurement campaign, whose purpose was mainly to compare the IWV estimates provided by the instrument with the ground sensor measurements of air temperature, air humidity, barometric pressure, and rainfall. In particular, we show that the temporal trends of the two IWV estimates are qualitatively consistent, and that the instrument is able to provide IWV estimates also in the presence of fog and rainfall. Conversely, a quantitative evaluation through comparison with IWV data from point weather station measurements appears challenging due to the significant spatial variability in temperature and relative humidity, even between couples of stations that are quite close to each other. [ABSTRACT FROM AUTHOR]

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff. [ABSTRACT FROM AUTHOR]

Introduction: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring. [ABSTRACT FROM AUTHOR]

Eight spatial interpolation models were used to map the spatial distribution of precipitation in the southwestern sector of the Iberian Peninsula (22330 km2) over 40 years (1980/1981–2019/2020). Rainfall data from 103 meteorological stations were used to generate the interpolation models, namely inverse distance weight (IDW) with 6, 12 and 24 points, regression spline (RS), thin spline (TS), universal kriging with spherical and Gaussian variogram (UK_Sphe and UK_gauss, respectively) and multilinear regression (MR), based on physiographic and geographic variables. Furthermore, 32 rainfall stations were used to assess the performance of the previous methods through 7 statistical metrics Ordinary Least Squares (OLS), Root Mean Square Error (RMSE), Normalized root mean square error (NRMSE), Coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Mean Absolute Error (Bias and MAE). Based on these metrics, UK_gauss and IDW_6 provided the best adjustments, whereas MR presented the highest errors. All methods were suitable to predict the spatial distribution of rainfall, but adjustments are conditioned by the features of the study area, gauge density and gauge spatial distribution. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the applicability of remote sensing precipitation products in southwest China, to reveal the spatial and temporal characteristics of precipitation in Southwest China, and to provide scientific references for the assessment of droughts and floods in southwest China and the utilization of water resources. [Methods] Firstly, combined with precipitation data from 100 ground- based meteorological stations in the study region, the applicability of TRMM and GPM precipitation products were evaluated on both the multi-year average and inter-annual scales, as well as the meteorological station scale. Subsequently, based on the preferred remote sensing precipitation product, the coefficient of variation method, Theil-Sen median trend analysis coupled with Mann-Kendall significance test and Hurst exponent were adopted to analyze spatiotemporal changes of precipitation and assess their sustainability. Moreover, our effort predicted the precipitation trends in the future. [Results (1) Both TRMM and GPM products demonstrated good consistency and applicability in southwest China, albeit with some overestimation. However, compared with TRMM, GPM displayed a higher spatial resolution and better data quality, which was overall closer to the measured data. (2) Precipitation in southwest China had experienced low and moderate fluctuations over the past 19 years, with 87.54% of the region. The trend for precipitation changes in this region had mostly been increasing (83.71%), mainly weak to moderate sustainability (50.38% weak. 24.30% moderate). The prediction for future precipitation trend as a whole suggested a general increase in precipitation, with 62.24% of the region showing increasing trends, mainly in the central part of the study region, and 13.23% showing trends, mainly in the central Yunnan and southwestern Sichuan regions. This effort provided valuable insights for decision-making related to agricultural production and water resources management in southwest China. [Conclusion GPM is more suitable than TRMM to characterize the precipitation characteristics in southwest China and can be effectively used to carry out spatiotemporal dynamic analysis of precipitation. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions like Iran, sustainable urban and agricultural development is intimately intertwined with the severity, frequency, and duration of meteorological droughts. Prolonged meteorological droughts can trigger hydrological and socio-economic droughts, posing significant challenges to the region's sustainability. Therefore, the primary objective of this study is to perform a comprehensive Regional Frequency Analysis (RFA) for extreme meteorological drought events across Iran. To this end, we meticulously calculated the severity, duration, and magnitude of the Standardized Precipitation Index (SPI) over the 3-, 6-, 9-, and 12-month accumulation periods. The data was derived from monthly precipitation records collected at 106 meteorological stations during the period spanning 1993–2016. Subsequently, we employed a rigorous approach, utilizing Ward's clustering analysis and the test for heterogeneity using L-moment, to effectively classify and categorize the extreme drought characteristics prevalent across the country. The analysis revealed that the generalized Pareto and Pearson type 3 models are the most suitable distribution functions for RFA across almost all the examined clusters. Additionally, the spatiotemporal analysis of the SPI series at various time scales indicated that the extreme events' characteristics, such as severity, duration, and magnitude, remain surprisingly unaffected by the local climate conditions. The results also showed that even in the typically humid regions located in the north, west, and northwest of Iran, areas that receive considerable annual precipitation, severe droughts can still occur. These droughts primarily stem from temporal events rather than being solely dependent on the local climate conditions. Therefore, the study concludes that the drought events can largely be attributed to mismanagement and inadequate regional water allocation. Moreover, these situations might have been further exacerbated by the occurrence of extreme climatic conditions. [ABSTRACT FROM AUTHOR]

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In this study, the effect of using a hybrid solar thermal-activated adsorption desalination system for brackish water is evaluated under the climatic conditions of the Brazilian semiarid region. The proposed theoretical model utilizes climatic data from the meteorological station in Campina Grande, PB, and adsorptive kinetics data of Fuji Davison RD 260 silica gel to predict the performance indices of the specific daily water production (SDWP), specific cooling power (SCP), and coefficient of performance (COP) performance coefficients over a characteristic day. The SDWP value of 6.26 m3/ton, SCP ranging from 50 to 300 W/kg, and an average COP of 0.5 were obtained, considering variations in global horizontal irradiance in the ACDS system and transient ambient temperature. It was observed that both the production of desalinated water and the refrigeration effect increase with the rise in daily solar irradiance. The variation in the number of solar collectors used in the system and their optimality, as well as the variation in the salinity index of the feed source, impacted the evaluated performance coefficients. [ABSTRACT FROM AUTHOR]

Assessing drought is crucial for effective water resources management and the development of mitigation strategies. Drought indices serve as indispensable tools in this evaluation process, and choosing an appropriate index is vital for accurate drought assessment. The characteristics and classification of drought depend entirely on the chosen index. Based on the existing literature, the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are the most commonly and widely used indices, and there is a significant need for a comprehensive comparison between SPI and SPEI to understand their differences and implications for drought assessment. This research aims to compare SPI and SPEI based on drought indices, characteristics, and classifications using the innovative drought classification matrix (IDCM) for spatiotemporal drought evaluation, and the comparison process is done for events and monthly scales. Also, it aims to investigate the comparison between SPEI obtained from in situ meteorological stations and from the SPEI database. The application and the comparison are presented for Istanbul city between 1951 and 2020. The results show similar variations and high correlation (more than 0.65) between SPI and SPEI. For drought characteristics, there is no consistent relationship between SPI and SPEI at the drought event scale. The outcomes revealed that approximately 60% of the months exhibit consistent drought classifications between the two indices. Finally, it shows a significant difference between SPEI based on in situ meteorological stations and satellite data. The comparison between SPI and SPEI based on different aspects is necessary and essential for drought studies and water resources management. [ABSTRACT FROM AUTHOR]

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies. [ABSTRACT FROM AUTHOR]

Flood early warning requires rainfall data with a high temporal and spatial resolution for flood risk analysis to simulate flood dynamics in all small and large basins. However, such high-quality data are still very scarce in many developing countries. In this research, in order to identify the best and most up-to-date rainfall estimation tools for early flood forecasting in arid and semi-arid regions, the northeastern region of Iran with 17 meteorological stations and four rainfall events was investigated. The rainfall products of satellites (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, Global Satellite Mapping of Precipitation, Climate Hazards Group InfraRed Precipitation with Station, European Reanalysis (ERA5), Global Precipitation Measurement) along with the most widely used microphysical schemes of Weather Research and Forecasting (WRF) model (Purdue-Lin (Lin), WRF Single-Moment class 3, 6, and WRF Double-Moment class 6. were used for rainfall modeling. The efficiency of each of these models to forecasting the amount of rainfall was verified by four methods: Threat Scores (TS), False Alarm Ratio, Hit Rate (H), and False Alarm (F). Analysis of research findings showed that the WRF meteorological model has better accuracy in rainfall modeling for the next 24 h. In this model, Lin's microphysical scheme has the highest accuracy, and its threat score (TS) quantity is up to 98% efficient in some stations. The best accuracy of satellite products for estimating the amount of rainfall is up to 50%. This accuracy value is related to the satellite product (ERA5). In this method, an 18 km distance from the ground station is the best distance for setting up the space station, which is used for input to hydrological/hydraulic models. Based on the results of this research, by using the connection of the WRF model with hydrology/hydraulic models, it is possible to predict and simulate rainfall-runoff up to 72 h before its occurrence. Also, by using these space stations, the amount of rainfall is estimated for the entire area of the basin and an early flood warning is issued. [ABSTRACT FROM AUTHOR]

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development. [ABSTRACT FROM AUTHOR]

This article examines the impact of climate change on the hydrometeorological indicators of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Two meteorological variables' annual and seasonal trends at three weather stations in 1986–2023 were analyzed. The non-parametric Mann–Kendall and Sen's slope methods were used to determine the presence of a positive or negative trend in weather data and their statistical significance. Hydrometric indicators were studied using the ArcGIS 10.8 program from 1995 to 2023. The results indicate an increasing average spring air temperature, with an annual rise of 0.08–0.09 °C. A significant trend in increasing average annual precipitation was observed in Saumalkol, with a rise of 4.7 mm per year. In contrast, no significant trends were found in the annual and seasonal precipitation data for Sergeyevka. It was also found that the area of Lake Saumalkol increased by 1.6% due to a rise in annual precipitation. In contrast, the area of Lake Kopa decreased by 6.04% because of an increase in the annual average temperature. [ABSTRACT FROM AUTHOR]

Pomacea canaliculata, a freshwater snail native to South America, has become widely naturalised in many countries, causing serious damage to agricultural and native ecosystems. Although climate warming is likely to expand the distribution of this species, limited information exists regarding its impact on feeding activities. In this study, we examined the effects of temperature on the feeding activity of this species and estimated the impact of climate warming on its feeding potential. The feeding activity was determined by measuring the amount of standard food (Japanese mustard spinach, komatsuna) ingested at different temperatures. It tended to increase from 15 to 25 °C but became almost stable from 25 to 35 °C. The respiration rate determined by the O2 consumption rate, showed a similar response to temperature. Based on these findings, we constructed a simple model to estimate the relative feeding activity using climatic data recorded at meteorological stations throughout the Japanese Archipelago. The model estimated that, with warming of + 2 °C, annual feeding potential (relative value) increased by 21.1% at the present northern distribution limit of this species. The effect of warming on percentage increase in feeding potential was estimated to be smaller at the southern distribution limit (9.9%), although the absolute feeding potential was larger than that at the northern sites. The model also suggested that if this species expanded its northern distribution range as a result of climate warming, it would have a high feeding potential comparable to that of the southern regions. [ABSTRACT FROM AUTHOR]

To address the need for reduced vehicle weight and improved environmental sustainability, the automotive industry has increasingly turned to mixing lightweight materials and alloys with metal alloys. However, this integration of dissimilar materials has heightened the risk of galvanic corrosion. This study addresses the gap in modeling of galvanic corrosion under dynamic thin film electrolyte by incorporating data derived from real-world weather conditions and finite element simulations. The presented model successfully captures the trend of galvanic corrosion rate for a given atmospheric environmental condition. The model predictions are compared with experimental data in the literature. Good agreements are observed. The model is further used for prediction of galvanic corrosion of two identical vehicles located in two different geographic locations (i.e., Miami Beach in Florida and Wendover in Nevada) in the year 2021 leveraging weather station data. Additionally, a Bayesian estimation method is used to account for uncertainties in the model parameters and estimation of the probability of failure. [ABSTRACT FROM AUTHOR]

Purpose: In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers' adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies. Design/methodology/approach: This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation. Findings: (1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers' decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas. Originality/value: By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers' adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration. Design/methodology/approach: Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated. Findings: This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed. Practical implications: Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation. Originality/value: Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions. [ABSTRACT FROM AUTHOR]

The widespread utilization of reanalysis-based precipitation (RP) data has significantly facilitated hydrometeorological studies in areas where observations are scarce. However, assessing the applicability of the RP data before being applied in any basin is necessary considering that the inherited errors of the RP data vary with surface conditions, seasonal cycles, and different climatic zones. In this study, the Ili River Basin (IRB) was selected as the study area to evaluate the accuracy and impact of ERA5 reanalysis precipitation data, a type of RP data, on the hydrological cycling in the IRB. Four methods, linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM), were tested to correct the bias of the ERA5 reanalysis precipitation. We found the following results. (1) The ERA5 precipitation exhibits a significant overestimation of gauge-observed precipitation within the IRB, indicating poor accuracy. Compared to the gauge-observed precipitation, the comprehensive performance of ERA5 precipitation in the dry season is better than that in the entire year, while the one in the entire year is better than that in the rainy season. The main error of the ERA5 precipitation lies in its insufficient ability to distinguish between no rain (0–0.1 mm) and light rain (0.1–4 mm) events, (2) All bias correction methods effectively address the biases in the ERA5 precipitation, although the extent of correction varies. The LS and DM methods outperformed the others regarding the time-series-based indices, while the DM method outperformed the others regarding the exceedance probability, and (3) In evaluating runoff simulation accuracy indices, hydrologic simulations driven by the corrected ERA5 precipitation using the DM method markedly outperform those driven by the ERA5 precipitation alone and even those corrected by the PT and LS methods. Specifically, the DM method exhibits the best correction of the flow duration curve and peak flow, with Nash-Sutcliffe efficiency coefficient (NSE) at 0.83 and relative deviation (RD) at 9.23% in the calibration period, and NSE at 0.85 and RD at 1.29% in the verification period. Corrected ERA5 precipitation data can fill in the gaps left by meteorological stations, effectively addressing the issue of inaccurate peak values in runoff simulations caused by using uncorrected ERA5 precipitation data. Practical Applications: Due to the constraints of complex terrain and natural conditions, the hydrometeorological monitoring stations are sparse and unevenly distributed in western China's Ili River Basin (IRB). The precipitation data obtained from the monitoring sites can hardly meet the needs of hydrological simulations. Furthermore, it challenges us to manage the IRB better when facing extreme weather conditions (e.g., drought and flooding). The ERA5 reanalysis precipitation data with comprehensive coverage and high resolution is a promising alternative to observing precipitation at the basin level. However, verifying the feasibility of using ERA5 precipitation data in IRB is necessary. In this study, we first noticed that the ERA5 precipitation data has a significant deviation and low accuracy in IRB by comparing it with available observed precipitation. Thus, we corrected ERA5 reanalysis precipitation using four different methods: linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). We found that the DM method can better capture extreme precipitation events, and the corresponding simulated runoff has the highest agreement with the observed runoff. [ABSTRACT FROM AUTHOR]

Summary A road‐weather information system (RWIS) is an attractive application in the context of intelligent transportation systems (ITSs), which is used where climatic conditions and meteorological events worsen traffic safety. The primary goal is to significantly reduce potential accidents by sending early warning messages to prompt immediate action. For this purpose, road weather information stations are placed at critical points on the highways. In addition to responsible transportation agencies, enabling direct warning message transfer from roadside infrastructure to vehicles can significantly improve the effectiveness of a typical RWIS application. Dedicated short range communication (DSRC) and cellular vehicle to everything (C‐V2X) technologies are extensively used for data exchange between vehicles (V2V) and between vehicles and road infrastructure (V2I or I2V) under the umbrella term of vehicle‐to‐everything (V2X) communications. Latency and coverage are key performance indicators of the vehicular network that determine how drivers react to risks in adverse road and weather conditions. V2X‐based connectivity mainly aims to offer low latency in message delivery, but it is not possible to obtain a wide‐range coverage due to significant propagation loss in the dedicated 5.9 GHz ITS frequency band. In this study, we presented a long range (LoRa)‐based RWIS application that uses I2 V communication at sub‐GHz bands. Our main contribution is proposing a novel method for RWIS implementations, where the LoRa gateways run in a half‐duplex way to collect information from road weather information stations in the uplink channel and then broadcast warning messages to vehicles in the downlink channel. To validate the applicability of the proposed method, we presented a feasibility study that includes transmission range and latency analysis. Our theoretical calculations' results validate the more extended transmission range and applicability of LoRa connectivity on RWIS applications, especially for common scenarios like black ice where the climate risks do not occur suddenly. We also showed that our methodology would be beneficial due to the ability to offer wide coverage, especially in the areas where cellular base stations have not yet existed or V2X road infrastructure has not yet been established. [ABSTRACT FROM AUTHOR]

The ISLAS2020 field campaign during February and March 2020 set out to obtain a unique dataset describing the Arctic water cycle using stable water isotope (SWI) observations. Our observation strategy focused on measuring evaporation, deposition, and precipitation, all of which are commonly sub-grid scale processes in numerical weather and climate models. Uncertain parameterizations for these processes can lead to compensating errors, which can go unnoticed; however, evaporation and precipitation can also be investigated with SWIs, as they are an integrated tracer for processes that atmospheric moisture has undergone. The campaign can be divided into two efforts: the primary field experiment in Ny-Ålesund focused on evaporation and deposition, and the larger precipitation collection network around the Nordic Seas. The primary field experiment lasted three weeks, from 23 February to 15 March 2020, with temperatures reaching below −30 °C. During these weeks, we obtained near-surface, high-resolution (approx. 20 cm) SWI profiles at two deployment sites. Using a newly developed profiling system, we measured SWI gradients in the lowermost 5 and 2 m over fjord water and snow-covered tundra, respectively. These profiles are complemented by fiber-optic distributed sensing (FODS) columns and nearby meteorological stations. The FODS columns supply continuous, high-resolution (2 cm or finer) temperature profiles above both locations, whereas the meteorological stations provide information on wind speed and direction. We also made a short deployment to the Zeppelin mountain observatory (472 m a.s.l) for measurements of the isotopic signal in the free-troposphere. Additionally, numerous water samples from the snowpack in and around Ny-Ålesund were taken, in addition to daily fjord water samples from Kongsfjorden. These samples provide the context for the surface conditions under which profiles were collected. Isotopic connections on the synoptic scale are achieved by linking Ny-Ålesund observations with precipitation sampling at locations across the European Arctic, namely Longyearbyen, Tromsø, Andenes, Ålesund, and Bergen. The resulting dataset provides comprehensive insight into the Arctic hydrological cycle and can facilitate the study of phase change processes and transport of water vapour into and out of the Svalbard region. Datasets from the field campaign are publicly available at the PANGAEA data repository (doi.pangaea.de/10.1594/PANGAEA.971241 , Seidl et al., 2024). [ABSTRACT FROM AUTHOR]

The Beiji Pavilion Observatory, also referred to as the National Research Institute of Meteorology, Academia Sinica, is a meteorological observatory in China. Research indicates that as early as the 1930s, the surface synoptic maps drawn up by the National Research Institute of Meteorology of Academia Sinica in the identification of fronts and air masses reflect the polar front theory, including some synoptic analysis principles, had been introduced to China. It offers a different perspective to traditional ‘Western’ theory. China assimilated many ideologies from the West and internalised them within Chinese meteorology. [ABSTRACT FROM AUTHOR]

ABSTRACT The Köppen‐Geiger (K‐G) climate classification is the most commonly used climate classification method in the world, and there are many K‐G climate classification studies focusing on Türkiye using different datasets. However, the differences in the datasets used in these studies lead to substantial differences and errors in K‐G climate zone maps. The differences and disagreements in these maps also cause significant discrepancies in climate studies. In this respect, accurate identification of climate classes and types is very important for understanding the distribution of climate types and for many climate‐based studies to achieve accurate results. In this study, the K‐G climate types of Türkiye and the regime characteristics of these climate types were determined using the CHELSA dataset corrected based on the measurements of 337 meteorological stations. According to the results that were obtained, 14 climate types were identified in Türkiye. Since the CHELSA dataset reflected topographic conditions well, many microclimates were identified within broad areas of climate types. The distribution of the microclimate types was compared to the distribution of the vegetation, and the accuracy of the results was evaluated. Apart from microclimates, other prominent features of this study were the co‐occurrence of multiple climate types in a limited area in the Eastern Black Sea Region and the detection of the EF climate type for the first time at the summit of Mount Ararat. Climate types vary according to altitude conditions, and temperature changes due to altitude are an important factor in the formation of climate sub‐types within the same main climate type in Türkiye. [ABSTRACT FROM AUTHOR]

The Extended European Alpine Region (EEAR) exhibits a well-established and very high-density network of in-situ weather stations, hardly attainable in other mountainous regions of the world. However, the strong fragmentation into national and regional administrations and the diversity of data sources have so far hampered full exploitation of the available data for climate research. Here, we present EEAR-Clim, a new observational dataset gathering in-situ daily measurements of air temperature and precipitation from a variety of meteorological and hydrological services covering the whole EEAR. Data collected include time series from recordings up to 2020, the longest ones spanning up to 200 years. The overall observational network encompasses about 9000 in-situ weather stations, significantly enhancing data coverage at high elevations and achieving an average spatial density of one station per 6.8 8 km2 over the period 1991–2020. Data collected from many sources were tested for quality to ensure internal, temporal, and spatial consistency of time series, including outliers removal. Data homogeneity was assessed through a cross-comparison of the outcomes using three methods well established in the literature, namely Climatol, ACMANT, and RH Test. Quantile matching was applied to adjust inhomogeneous periods in time series. Overall, about 4 % of data were flagged as non-reliable and about 20 % of air temperature time series were corrected for one or more inhomogeneous periods. In the case of precipitation time series, fewer breakpoints were detected, confirming the well-known challenge of properly identifying inhomogeneities in noisy data. The dataset aims to serve as a powerful tool for better understanding climate change over the European Alps. [ABSTRACT FROM AUTHOR]

The objective of this study is to determine the relationship between short-term temperature variability on neighboring days and mortality. The change in maximum temperature in Northern Virginia, Richmond, Roanoke, and Norfolk, Virginia, on neighboring days was calculated from airport observations and associated with total mortality over a multi-county area surrounding each weather station. The association between day-to-day temperature change and mortality, lagged over a 28-day period, was analyzed using distributed lag non-linear models that controlled for air quality, temporal trends, and other factors. Days following large temperature declines were associated with an increased risk of mortality in three of the four locations, and temperature increases were linked to higher mortality risk in two cities. For example, the relative risk of mortality for a 12°C daily temperature decline (1st percentile) was 1.74 [0.92, 3.27] in Roanoke and 1.16 [0.70, 1.92] in Richmond. The net effect of short-term temperature increases was smaller, with the largest relative risk of 1.03 [0.58, 1.83] for a 12°C increase (99th percentile) in maximum temperature in Norfolk. In Richmond and Roanoke, there was an observed lagged effect of increased mortality (maximum relative risks varying from 1.08 to 1.10) that extended from 5 to 25 days associated with large temperature declines of 15°C or more. In contrast, there was a strong and immediate (lag 0–3 day) increase in the risk of mortality (1.10 to 1.15) in northern Virginia and Norfolk when the temperature increase exceeded 10°C (short-term warming). In general, consecutive day warming had a more immediate mortality impact than short-term cooling, when the peak mortality is lagged by one week or more. However, cooling of at least 10°C after a hot (summer) day reduced mortality relative to comparable cooling following a cold (winter) day, which is associated with high mortality. This differential mortality response as a function of temperature suggests that there is some relationship between average temperature, temperature variability, and season. The findings of this study may be useful to public health officials in developing mitigation strategies to reduce the adverse health risks associated with short-term temperature variability. [ABSTRACT FROM AUTHOR]

The growth of a city is typically accompanied by densification and sprawl, the former through verticalization, urban renewal, and the filling in of empty spaces. All of these activities extend and intensify the urban heat island (UHI), which is quantified in this study as the difference in daily minimum temperature between urban and rural areas. Here, we investigate this phenomenon in the area of Rennes (France) and 17 surrounding cities using the Rennes Urban Network which comprises 93 weather stations. This study aims to 1) determine the optimal method for spatializing UHI in Rennes, France, 2) estimate and spatialize the UHI in the small peri-urban cities surrounding Rennes. For this, we model mean UHI and intense UHI using three methods of interpolation--multi-linear regression (MLR), ordinary kriging (OK), and regression kriging (RK)--based on data from 2022. We find that the RK method is the most suitable overall, with an RMSE of 0.11°C for mean UHI and 0.25°C for intense UHI. This approach allows stochasticity to be taken into account, and thus provides a better representation of UHI variation within Rennes and its peri-urban cities. [ABSTRACT FROM AUTHOR]

The Total Carbon Column Observing Network (TCCON) and the Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) are two ground-based networks that provide the retrieved concentrations of up to 30 atmospheric trace gases, using solar absorption spectrometry. Both networks provide reference measurements for the validation of satellites and models. TCCON concentrates on long-lived greenhouse gases (GHGs) for carbon cycle studies and validation. The number of sites is limited, and the geographical coverage is uneven, covering mainly Europe and the USA. A better distribution of stations is desired to improve the representativeness of the data for various atmospheric conditions and surface conditions and to cover a large latitudinal distribution. The two successive Fiducial Reference Measurements for Greenhouse Gases European Space Agency projects (FRM4GHG and FRM4GHG2) aim at the assessment of several low-cost portable instruments for precise measurements of GHGs to complement the existing ground-based sites. Several types of low spectral resolution Fourier transform infrared (FTIR) spectrometers manufactured by Bruker, namely an EM27/SUN, a Vertex70, a fiber-coupled IRCube, and a Laser Heterodyne spectro-Radiometer (LHR) developed by UK Rutherford Appleton Laboratory are the participating instruments to achieve the Fiducial Reference Measurements (FRMs) status. Intensive side-by-side measurements were performed using all four instruments next to the Bruker IFS 125HR high spectral resolution FTIR, performing measurements in the NIR (TCCON configuration) and MIR (NDACC configuration) spectral range. The remote sensing measurements were complemented by AirCore launches, which provided in situ vertical profiles of target gases traceable to the World Meteorological Organization (WMO) reference scale. The results of the intercomparisons are shown and discussed. Except for the EM27/SUN, all other instruments, including the reference TCCON spectrometer, needed modifications during the campaign period. The EM27/SUN and the Vertex70 provided stable and precise measurements of the target gases during the campaign with quantified small biases. As part of the FRM4GHG project, one EM27/SUN is now used as a travel standard for the verification of column-integrated GHG measurements. The extension of the Vertex70 to the MIR provides the opportunity to retrieve additional concentrations of N2O, CH4, HCHO, and OCS. These MIR data products are comparable to the retrieval results from the high-resolution IFS 125HR spectrometer as operated by the NDACC. Our studies show the potential for such types of spectrometers to be used as a travel standard for the MIR species. An enclosure system with a compact solar tracker and meteorological station has been developed to house the low spectral resolution portable FTIR systems for performing solar absorption measurements. This helps the spectrometers to be mobile and enables autonomous operation, which will help to complement the TCCON and NDACC networks by extending the observational capabilities at new sites for the observation of GHGs and additional air quality gases. The development of the retrieval software allows comparable processing of the Vertex70 type of spectra as the EM27/SUN ones, therefore bringing them under the umbrella of the COllaborative Carbon Column Observing Network (COCCON). A self-assessment following the CEOS-FRM Maturity Matrix shows that the COCCON is able to provide GHG data products of FRM quality and can be used for either short-term campaigns or long-term measurements to complement the high-resolution FTIR networks. [ABSTRACT FROM AUTHOR]

Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05° resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region. [ABSTRACT FROM AUTHOR]

In the arid Arabian Peninsula, particularly within the United Arab Emirates (UAE), the perception of rainfall has shifted from a natural blessing to a significant challenge for infrastructure and community resilience. The unprecedented storm on 17 April 2024, exposed critical vulnerabilities in the UAE's urban infrastructure and flood management practices, revealing substantial gaps in handling accumulated precipitation. This study addresses the necessity of updating the Intensity–Duration–Frequency (IDF) curves for the Sharjah Emirate by utilizing recent precipitation data from 2021 to April 2024, alongside previously published 2020 data. By recalibrating the IDF curves based on data from three meteorological stations, this study reveals a substantial increase in rainfall intensities across all durations and return periods. Rainfall intensities increased by an average of 36.76% in Sharjah, 26.52% in Al Dhaid, and 17.55% in Mleiha. These increases indicate a trend towards more severe and frequent rainfall events, emphasizing the urgent need to revise hydrological models and infrastructure designs to enhance flood resilience. This study contributes valuable insights for policymakers, urban planners, and disaster management authorities in the UAE and similar regions worldwide. [ABSTRACT FROM AUTHOR]

Water vapor transport is an important foundation and prerequisite for the occurrence of rainstorms. Consequently, the understanding of water vapor transport as well as the sources of water vapor during rainstorm processes should be considered as essential to study the formation mechanism of rainstorms. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is adopted for backward tracking of water vapor transport trajectories and sources during the "7·20" extraordinary heavy rain process in Zhengzhou City of China that occurred on 20 July 2021. On this basis, the trajectory clustering method is applied to quantitatively analyze the contributions of water vapor sources, aiming to provide a basis for exploring the maintenance mechanism of this extreme rainstorm event. The spatio-temporal characteristics of this rainstorm event show that there are 4 consecutive days with the precipitation reaching or exceeding the rainstorm level across the whole Zhengzhou City, with the daily rainfall amounts at eight national meteorological stations all breaking their respective historical extreme values. The regional-averaged rainfall amount in Zhengzhou City is 527.4 mm, while the maximum accumulated rainfall amount reaches 985.2 mm at Xinmi station and the maximum hourly rainfall amount at Zhengzhou national meteorological station reaches 201.9 mm h−1. The water vapor sources for this rainfall process, ranked in descending order of contribution, are the Western Pacific, inland areas of Northwest China and South China, and South China Sea. The water vapor at lower levels is mainly transported from the Western Pacific and the South China Sea, while those from the inland areas of Northwest China and South China provide a supply of water vapor at upper levels to a certain extent. The water vapor at 950 hPa is mainly sourced from the Western Pacific and South China Sea, accounting for 56% and 44%, respectively. The water vapor at 850 hPa mainly derives from the Western Pacific and the inland areas of South China, contributing 58% and 34% of the total, respectively. The water vapor at 700 hPa mainly comes from the inland areas of Northwest China and South China Sea. Specifically, the water vapor from inland Northwest China contributes 44% of the total, acting as the primary source. The water vapor at 500 hPa is mainly transported from the inland areas of South China and Northwest China, with that from the inland South China (56%) being more prominent. The water vapor at all levels is mainly transported to the rainstorm region through the eastern and southern regions of China from the source areas. Additionally, there are some differences in the water vapor trajectories at a 6 h interval. [ABSTRACT FROM AUTHOR]

Arctic cyclones are key drivers of sea ice and ocean variability. During the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, joint observations of the coupled air‐ice‐ocean system were collected at multiple spatial scales. Here, we present observations of a strong mid‐winter cyclone that impacted the MOSAiC site as it drifted in the central Arctic pack ice. The sea ice dynamical response showed spatial structure at the scale of the evolving and translating cyclonic wind field. Internal ice stress and ocean stress play significant roles, resulting in timing offsets between the atmospheric forcing and the ice response and post‐cyclone inertial ringing in the ice and ocean. Ice motion in response to the wind field then forces the upper ocean currents through frictional drag. The strongest impacts to the sea ice and ocean from the passing cyclone occur as a result of the surface impacts of a strong atmospheric low‐level jet (LLJ) behind the trailing cold front and changing wind directions between the warm‐sector LLJ and post cold‐frontal LLJ. Impacts of the cyclone are prolonged through the coupled ice‐ocean inertial response. Local impacts of the approximately 120 km wide LLJ occur over a 12 hr period or less and at scales of a kilometer to a few tens of kilometers, meaning that these impacts occur at combined smaller spatial scales and faster time scales than most satellite observations and coupled Earth system models can resolve. Plain Language Summary: Arctic winter cyclones are an important part of the Arctic climate system. Yet, due to sparse observations, processes of the coupled sea ice‐ocean response to cyclones are not fully understood. During the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, observations of the atmosphere, sea ice, and ocean were collected at a range of spatial scales. Here, we describe the atmospheric structure and coupled ice‐ocean response to a strong winter cyclone using data from surface weather stations, weather balloons, radar, and a weather model. We then describe the sea ice motion using a large set of global positioning system buoys and ice radar images. Finally, we examine the upper ocean currents and structure using ocean buoy data. The most important part of the storm structure for the sea ice is the development of an atmospheric low‐level jet (LLJ), a narrow region of fast‐moving air that eventually circles around the storm. The sudden change in ice drift speed at the time that the LLJ passes overhead enhances motion of the ice and ocean. Periodic currents in the ocean initiated by the sudden wind change of the LLJ continue for days following the passage of the storm, prolonging its effects. Key Points: A strong cyclone crossed the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) in midwinter 2020Detailed, multi‐platform observations enable characterization of coupled air‐ice‐ocean interactions during the passage of the cycloneThe development of a low‐level atmospheric jet is a key factor in the spatially and temporally varying sea ice‐ocean response to the storm [ABSTRACT FROM AUTHOR]