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Potential evapotranspiration (PET) is a key factor in hydrological cycle and energy balance and plays an important role in drought and global climate change response. Existing observational and modeling methods for PET retrieval have their limitations, such as low precision and poor spatial resolution, which becomes the focus of this study. A hybrid PET fusion (HPF) method is proposed by fusing station- and grid-based PET, in which the PET expression is determined by considering the factors of location, temperature, and zenith total delay (ZTD). In addition, an improved Helmert variance component estimation method is introduced to determine the optimal weights of the HPF model. Corresponding data, which include monthly Thornthwaite (TH)-derived PET data with a spatial resolution of 0.25° × 0.25° and Penman–Monteith (PM)-derived PET data at 704 meteorological stations, over the past 60 years from 1959 to 2018 in China are selected. The 10-fold cross-validation method is introduced to evaluate the internal and external accuracies of the proposed HPF method. Statistical result shows that the average root mean square (RMS) of the proposed HPF method is 13.98 mm, with an average RMS improvement rate (IR) of 46.71 % compared with TH-derived PET, when PM-derived PET is regarded as a reference. Moreover, the performance of the HPF-derived standardized precipitation evapotranspiration index (SPEI) is evaluated at different time scales, and the average RMS is 0.3, with an average RMS IR of 26.33 % compared with TH-derived SPEI. Such results verify the good performance of the proposed HPF model and enrich the methods for obtaining PET with high precision and resolution. [ABSTRACT FROM AUTHOR]

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property. [ABSTRACT FROM AUTHOR]

This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons. For the most challenging task, the 30-minute ahead forecasts, the model achieved a wind speed Mean Absolute Error (MAE) of 0.78 m/s and a wind direction MAE of 33.06°. Furthermore, the use of Gaussian noise concatenation to both input and label training data yielded the most consistent results. A case study further validated the model's efficacy, with MAE values below 0.43 m/s for wind speed and between 33.93° and 35.03° for wind direction across different forecast horizons. This approach shows that combining strategically deployed sensor networks with machine learning techniques offers improvements in wind nowcasting for airports in complex environments, possibly enhancing operational efficiency and safety. [ABSTRACT FROM AUTHOR]

"Atmospheric River: Friend or Foe?" explores the impact of atmospheric rivers on the West Coast, delivering a significant portion of annual precipitation but also causing major floods. These powerful streams of water can bring both drought relief and destructive flooding, with forecasting and research efforts focused on understanding and predicting their behavior. The article highlights the complex interactions between ocean and atmosphere, as well as the role of climate change in intensifying extreme weather events like atmospheric rivers. [Extracted from the article]

Air temperature is an important indicator to analyze climate change in mountainous areas. ERA5 reanalysis air temperature data are important products that were widely used to analyze temperature change in mountainous areas. However, the reliability of ERA5 reanalysis air temperature over the Qilian Mountains (QLM) is unclear. In this study, we evaluated the reliability of ERA5 monthly averaged reanalysis 2 m air temperature data using the observations at 17 meteorological stations in the QLM from 1979 to 2017. The results showed that: ERA5 reanalysis monthly averaged air temperature data have a good applicability in the QLM in general (R2=0.99). ERA5 reanalysis temperature data overestimated the observed temperature in the QLM in general. Root mean square error (RMSE) increases with the increasing of elevation range, showing that the reliability of ERA5 reanalysis temperature data is worse in higher elevation than that in lower altitude. ERA5 reanalysis temperature can capture observational warming rates well. All the smallest warming rates of observational temperature and ERA5 reanalysis temperature are found in winter, with the warming rates of 0.393°C/10a and 0.360°C/10a, respectively. This study will provide a reference for the application of ERA5 reanalysis monthly averaged air temperature data at different elevation ranges in the Qilian Mountains. [ABSTRACT FROM AUTHOR]

Climate change vulnerability is the biggest threat to ecosystems and economies of the world. Hence, this study aims to assess the vulnerability to climate variability adaptation strategies of rural households in Begemdir District, Northwest Ethiopia. In this study, the cross-sectional research design was used to gain a wider and better understanding of vulnerability to climate variability. Both primary and secondary data were used to triangulate the study to maintain validity. A multi-stage random sampling technique was used to select 120 sample households from the study area. Moreover, climatic data, such as rainfall and temperature data were collected from meteorological stations. The data gathered from primary data sources analyzed by using descriptive statistics. Finally, a logistic regression model was employed to identify the factors that affecting households' decisions to climate adaptation strategies in the study area. The results of the study reveal that the overall IPCC-LVI score is 0.49, and the perceived rainfall has decreased over the last decade. This implies livelihoods of the households are vulnerable to climate variability and low adaptive capacity. The age, sex, education level, extension services, land size, credit access, access to climatic information, access to credit, and extension services affected significantly (p < 0.05) households' vulnerability to climate variability in the study area. Thus, the local governments, policymakers, non-governmental organizations, and farming communities need to consider these variables to realize climate change adaptation strategies in the study area. Moreover, higher focus should be given to enhancing education, expanding access to credit, increasing land management support, as well as strengthening extension services to build long-term sustainable climate-resilient practices and mitigate the impacts of climate change vulnerability to households in the study area. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau (QTP), a high mountain area prone to destructive rainstorm hazards and inducing natural disasters, underscores the importance of developing precipitation intensity-duration-frequency (IDF) curves for estimating extreme precipitation characteristics. Here we introduce the Qinghai-Tibet Plateau Precipitation Intensity-Duration-Frequency Curves (QTPPIDFC) dataset, the first gridded dataset tailored for estimating extreme precipitation characteristics in QTP. The generalized extreme value distribution is chosen to fit the annual maximum precipitation samples at 203 weather stations, based on which the at-site IDF curves are estimated; then, principal component analysis is done to identify the southeast-northwest spatial pattern of at-site IDF curves, and its first principal component gives a 96% explained variance; finally, spatial interpolation is done to estimate gridded IDF curves by using the random forest model with geographical and climatic variables as predictors. The dataset provides precipitation information within 1, 2, 3, 6, 12, 24 hours and 5, 10, 20, 50,100 return years, with a 1/30° spatial resolution. The QTPPIDFC dataset can solidly serve for hydrometeorological-related risk management and hydraulic/hydrologic engineering design in QTP. [ABSTRACT FROM AUTHOR]

The current work focuses on the establishment of an enhanced Internet of Things (IoT) model in expectation to improve the sunflower seeds output in Uzbekistan. The presented framework involves examination of air quality, soil moisture, temperature, humidity, light intensity, GPS and weather station which is anticipated in giving a complete control and monitoring of the environmental probes at real time. The main goal is to establish an argument that such architecture will increase the yields in agriculture. It is done by simulating a model based on correlation and regression on secondary data which shows that the model will provide solutions to the problems associated with conventional farming which include conventional approaches towards provision of water and failure to internalize the conditions within which farming activities occur. The connection of the proposed sensors with the platform based on Arduino allows to gather and analyze the data that is essential for making appropriate decisions by the farmers. As the results the use of the developed framework in selected fields of sunflower will enhance yield with a potential of up to 25% in yield increase. Thus, the results shows that the implementation of such an innovative IoT architecture can greatly help farmers to increase efficiency, make proper use of resources, and minimize the negative effects on the environment while contributing to the development of sustainable agriculture. At the end the study recommends that further studies shall include more variables in the framework and test it for other crops and in other regions. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine the impact of season, temperature and humidity on the severity of Raynaud phenomenon (RP) in systemic sclerosis. Methods: Data from the Australian Scleroderma Cohort Study were used to assess associations of patient‐reported worsened RP in the month preceding each study visit. Mean monthly weather data were obtained from the closest weather station to the patient's address. We evaluated the relationship between worsened RP and health‐related quality of life (HRQoL) measured using the Short Form 36 instrument. Results: Among 1,972 patients with systemic sclerosis, RP was a near‐universal finding, and worsened RP in the preceding month was reported in 26.7% of 9,175 visits. "Worsened RP" showed significant environmental variability. On multivariable analysis, worsened RP was associated with low mean maximum temperatures (odds ratio [OR] 0.91, 95% confidence interval [95% CI] 0.90–0.92, P < 0.001), high relative humidity (OR 1.05, 95% CI 1.04–1.05, P < 0.001) and lower mean daily evaporation (OR 0.77, 95% CI 0.73–0.81, P < 0.001). Worsened RP was strongly associated with telangiectasia, calcinosis, and digital ulceration, as well as demonstrating an association with anticentromere antibody and gastroesophageal reflux disease and a negative correlation with diffuse disease. Worsened RP was also strongly associated with worse HRQoL. Conclusion: Lower environmental temperature and higher relative humidity had significant associations with worsened RP in this systemic sclerosis cohort, suggesting an important role for dry warmth in managing this condition. [ABSTRACT FROM AUTHOR]

This study investigates the characteristics of wind speed in two synoptic weather stations in Niger. Hence, three hourly wind data including wind speed and direction from 2009 to 2019 were used. Prior to the analysis, the dataset was pre‐processed to remove corrupted and irrelevant data. Statistical analysis was conducted on the dataset to understand the variability of the wind speed and wind direction at the two synoptic stations. The results showed that the highest prevailing wind is recorded in the Agadez region with average wind speeds ranging from [3; 6] m/s and [6; 10] m/s at 10 m altitude. The dominant directions are the East from September to May, then the West from June to August. At the Tahoua station, the prevailing winds have average speeds also belonging to [3; 6] m/s and [6; 10] m/s at 10 m altitude, but the dominant directions are East and East‐North‐East from October to April, then West and South‐West from May to September. These findings are useful for the effective planning of a wind energy project in Niger as they provide useful insight regarding the wind characteristics in two windy regions in Niger. [ABSTRACT FROM AUTHOR]

Weather is one of the important elements that greatly determines human activities, especially those related to economic factors. Therefore, understanding weather conditions using weather parameters as a reference is important for human life, so a method is needed to classify weather according to its category so that the information produced can be used for various needs. Determining weather conditions in an area will not run well without a reliable method that can analyze existing weather parameters. Therefore, in this study, the weather condition classification process was carried out using the multilayer perceptron algorithm, a type of neural network (NN) algorithm. All data analyzed were weather parameter data collected by mini weather stations placed on land. The weather parameters used were temperature, humidity, air pressure, wind speed, dew point, wind chill, daily rainfall, solar radiation, and UV index. This study was conducted in Palu city, Central Sulawesi Province, Indonesia. The classification process carried out by the multilayer perceptron algorithm was carried out on the Altair AI Studio application and produced an accuracy value of 93.87%, recall of 92.33%, and precision of 91.29%. [ABSTRACT FROM AUTHOR]

We use a multivariate analysis to study droughts in Finland using the Joint Deficit Index (JDI). Subsequently, the joint probability of occurrence of drought characteristics was analysed using Vine copulas. For this purpose, we used monthly precipitation from 22 meteorological stations across Finland in the period 1980–2021. The JDI time series showed that Finland had a normal wetness condition most of the time during the studied period. Trend analysis of the JDI time series using a modified Mann–Kendall test showed that there was no significant trend in the values of this index during the studied period. The drought characteristics, including severity, duration and inter‐arrival time (IAT), were extracted from the JDI time series for each station. The trend analysis of drought characteristics showed that only the Tohmajärvi Kemie station in eastern Finland had a significant negative trend in drought duration and severity. Furthermore, of the 22 stations studied, only two stations showed a significant increasing trend in the duration and severity of drought at the 10% level. The drought characteristics at the remaining stations showed no significant trend at the 10% level of significance. For stations with non‐stationary drought characteristics, generalised additive models for location, scale, and shape (GAMLSS) were used for frequency analysis. The correlation between the three characteristics of severity, duration and IAT was investigated using Kendall's Tau statistic. The results showed a high correlation between the two variables duration and severity and a moderate and acceptable correlation between drought severity and IAT as well as the pair of duration and IAT. In the following, copula functions were used to construct a trivariate distribution of the drought characteristics. Among the copulas tested, the R‐vine copula and its independent mode have the best fit for the variables under study and provide a suitable tree sequence. Finally, using the aforementioned copulas and their conditional density, the frequency analysis of the three drought variables was performed. The results of this study were presented in the form of four‐dimensional graphs to estimate the joint probability of occurrence of drought characteristics based on the JDI. These graphs are presented according to the precipitation conditions of each station, and by having a drought characteristic, other characteristics can be estimated with different probabilities. The proposed method is very efficient in analysing the joint frequency of drought characteristics due to the consideration of the effective parameters and the use of conditional density. [ABSTRACT FROM AUTHOR]

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]

The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region. [ABSTRACT FROM AUTHOR]

This paper assessed aridity and humidity conditions in Bačka (north Serbia) in the period 1949–2018. The assessment was based on the analysis of air temperature and total precipitation from 5 meteorological stations. Spatio-temporal changes were determined based on aridity indices, statistical and interpolation procedures. The Mann–Kendall indicates no statistical significance in aridity trends at most stations. The annual value of the Lobova index indicates arid conditions. Monthly values of the De Martonne index (IDM) do not show a statistically significant positive or negative trend, except for May, September to December. The annual values of the IDM indicate diversity of aridity conditions with a statistically significant positive trend on annual level only at one station (Sombor). Dry and wet years are equally distributed based on Rainfall Anomaly Index (RAI). The 2010 belongs to extremely wet category while the 2000 stands out as extremely dry. Statistically significant positive aridity trend was observed for RAI on annual level for two stations (Palić and Sombor). According to interpolation technique, annual and seasonal values of the IDM belong to humid and semi-humid conditions. The spatial variability of the RAI is between normal and slightly dry. The Lobova index shows different patterns of aridity. This study provides insight into the dynamics of aridity, and its results can be used in planning and implementing climate change adaptation measures. Since agricultural productivity is highly dependent on aridity and drought conditions, agricultural activities face numerous challenges. Therefore, the presented results can provide a solid basis for designing and implementing adaptation strategies and interventions on a regional scale in order to mitigate the impacts of climate change and aridity on agricultural production in the studied region. [ABSTRACT FROM AUTHOR]

The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure. Plain Language Summary: This study addresses the challenge of accurately computing the amount of water stored in snow (known as snow water equivalent or SWE) in mountainous areas, which is important for managing water resources. Typically, there are no tools that can measure SWE across large areas in complex high alpine surroundings, only at specific points. However, at Mt. Zugspitze at the border of Germany and Austria, a special device called a superconducting gravimeter can detect changes in gravity caused by the snow, providing a way to estimate SWE over large areas. We used data from this gravimeter to test two versions of a snow model called Alpine3D. In the first version, the model relied only on weather station data. In the second version, we improved the model by using satellite images to adjust the amount and spatial distribution of precipitation (snowfall) in the model. The results showed that the model gets more accurate by using satellite data to predict SWE changes during the melting season. This finding suggests that satellite images could be a useful tool for analyzing SWE in mountainous regions with limited infrastructure. Key Points: Evaluation of a distributed physically based snow model using superconducting gravimetry in a high alpine areaPrecipitation scaling based on a single satellite snow depth image significantly improves the simulated gravity signal of the snowpackAccurate spatial distribution of snow depth is found to be key to simulate snow mass (SWE) evolution in complex alpine terrain [ABSTRACT FROM AUTHOR]

Accurate forecasting of wind speed is essential for daily life and social production. While numerical weather prediction products are widely used, they rely on global data and mathematical models to solve atmospheric dynamics' equations, often failing to capture localized micrometeorological phenomena accurately. Factors such as surface conditions, land‐sea differences, and topography, particularly in coastal areas, further impact the accuracy of wind speed forecasts. This study presents a new method to enhance short‐term wind speed forecasting along China's coast by incorporating local and neighborhood spatiotemporal information. The approach integrates meteorological data from adjacent grid points as new inputs in the LightGBM, CatBoost, and XGBoost algorithms. Stacking ensemble technique is then employed to effectively combine with the aforementioned foundational models. Two sets of experiments are conducted: Experiments 1 exclude surrounding information, while Experiments 2 include it. Each set consists of five experiment groups: annual, spring, summer, autumn, and winter. Within each group, four models are tested: XGBoost, LightGBM, CatBoost, and stacking. Results show that incorporating surrounding site information improves forecast accuracy. In all five groups with added surrounding site information, the stacking model performs best. Compared to ECMWF forecast data, the stacking model improves wind speed forecast accuracy from 53.3%, 50.9%, 55.2%, 53.0%, and 54.0% to 77.2%, 73.1%, 76.7%, 78.2%, and 77.1%, respectively. These findings demonstrate the potential effectiveness of the proposed method for improving short‐term wind speed forecasts in China's coastal areas. Plain Language Summary: Wind speed prediction plays a crucial role in weather forecasting, ensuring safety, promoting economic development, agricultural management, and meteorological research. The primary method for forecasting wind speed is through numerical weather forecasts. However, due to the complexity of atmospheric systems, these forecasts often contain errors. Therefore, it is necessary to revise the forecast results. This study utilized machine learning to train on historical data from meteorological stations, resulting in the development of a wind speed correction model that significantly improved the accuracy of wind speed forecasts. Additionally, the study revealed the positive impact of surrounding station and grid information on enhancing forecast accuracy, providing valuable insights for future research on similar issues. Furthermore, further exploration of new data features, optimization of model parameters, and expansion of the research scope are expected to further improve the accuracy of local weather and wind speed forecasts. Key Points: Using CatBoost, XGBoost, and LightGBM in a stacking ensemble model can significantly enhance wind speed forecasting accuracyThe surrounding spatiotemporal information is essential for high‐precise wind speed forecasting at sites and the gridThe accuracy of wind speed correction is lower in coastal areas than in inland areas, and flat areas are preferable to complex terrain areas [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau (QTP) has undergone substantial warming, resulting in extensive permafrost degradation and a pronounced increase in landslide frequency. However, the causal link between climate warming and permafrost landslide occurrences remains poorly understood. A comprehensive inventory of permafrost landslides along the Qinghai-Tibet Engineering Corridor (QTEC) from 2016 to 2022 was compiled through remote sensing and field verification, along with an analysis of landslide triggering factors based on data from 5 weather stations, 4 active layer thickness observation sites, and 3 precipitation stations. From 2000 to 2020, the mean annual air temperature (MAAT) showed an increase of 0.5°C per decade, while precipitation remained relatively stable. A notable peak occurred in 2016, with MAAT and mean annual surface ground temperature rising sharply by 0.59°C and 0.41°C, respectively, from the previous year. In the same year, active layer thickness across observation sites increased by an average of 18.5 cm, exceeding the average thickening rate. This substantial deepening of the active layer suggests that a portion of the underlying permafrost, potentially ice-rich near the permafrost table, thawed during the warm season. Laboratory experiments further reveal a three-stage reduction in soil strength as temperatures approach 0°C, with the most pronounced decline at −1°C. Interpretation of landslide data shows that landslide frequency in 2016 significantly increased, reaching approximately 1.3 times the historical total. This suggests that a thawed interlayer forming at the active layer-permafrost interface plays a dominant role in landslide initiation. The thawed layer acts as a weak zone, enabling the downward movement of the overlying active layer and contributing to slope instability. These findings provide robust evidence linking temperature rise to permafrost-related landslides, offering new insights into the mechanisms of temperature-induced slope instability in high-altitude regions. [ABSTRACT FROM AUTHOR]

The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis data sets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These data sets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 yr. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 |$\sim$| 0.38 |$^{\circ }$| C decade |$^{-1}$| and 0.02 |$\sim$| 0.15 mm decade |$^{-1}$|⁠ , respectively. The nighttime PWV slightly decrease during the winter with rates of |$-$| 0.01 |$\sim \, -$| 0.03 mm decade |$^{-1}$|⁠. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 data set. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations. [ABSTRACT FROM AUTHOR]

Reliable and continuous meteorological data are crucial for modeling the responses of energy systems and their components to weather and climate conditions, particularly in densely populated urban areas. However, existing long-term datasets often suffer from spatial and temporal gaps and inconsistencies, posing great challenges for detailed urban energy system modeling and cross-city comparison under realistic weather conditions. Here we introduce the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0, a 23-year (1998–2020) gap-free and quality-controlled hourly weather dataset covering 550 weather station locations across all urban areas in the contiguous United States. CHUWD-H v1.0 synthesizes hourly weather observations from stations with outputs from a physics-based solar radiation model and a reanalysis dataset through a multi-step gap filling approach. A 10-fold Monte Carlo cross-validation suggests that the accuracy of this gap filling approach surpasses that of conventional gap filling methods. Designed primarily for urban energy system modeling, CHUWD-H v1.0 should also support historical urban meteorological and climate studies, including the validation and evaluation of urban climate modeling. [ABSTRACT FROM AUTHOR]

Objective: To examine the associations between gradients of average daily temperature and sunlight hours with daily steps over a 35-week period in school-aged children and to evaluate whether there were differences by sex. Methods: We conducted a follow-up study involving 655 children (50.8% girls, mean age 10.45 ± 0.95 years) from six public primary schools in Cuenca, Spain. We measured daily steps using Xiaomi Mi Band 3 Smart Bracelets (Xiaomi Corporation, Beijing, China) from October 2022 to June 2023 (over 35 weeks). We collected the average daily temperature from the local weather station in Cuenca and the sunlight hours during the same period. We used ANCOVA models and LOESS regression to examine the associations between gradients of average daily temperature and daily hours of sunlight with daily steps. Additionally, we performed a multiple linear regression model. Results: Our findings revealed significant variations in daily steps across the 35 weeks. The relationship between environmental factors and daily steps was non-linear in both girls and boys. The optimal values for higher activity levels were an average temperature of 14 °C and 13 h of sunlight. Furthermore, a 1 °C increase in temperature was associated with an increase of 74 ± 130 steps/day, while an increase of one hour of sunlight was associated with an increase of 315 ± 237 steps/day. However, the sunlight hours may act as a moderating factor. Conclusions: Our study showed a non-linear association between average daily temperature and the sunlight hours with daily steps over a 35-week period. Appropriate strategies may be needed to promote physical activity during periods of extreme temperatures or sunlight exposure. [ABSTRACT FROM AUTHOR]

Mountain watersheds, as the critical regional water sources, are highly sensitive to climate change. It is a high demand to investigate the hydrological variations in mountain watersheds for the protection of regional ecological environments and sustainable water resources. This study aims to explore the spatiotemporal variation in the potential evapotranspiration and attribution analysis of typical watershed runoff in the Qinling Mountain Areas. Six typical watersheds were selected as the research objects. Meteorological data was daily collected from 79 meteorological stations from 1965 to 2019. Annual runoff data was from the hydrological control stations in the watersheds, together with the annual sunspot numbers, atmospheric circulation indices, and the Nino3.4 index, such as the Penman-Monteith equation. The modified MannKendall trend test was employed to analyze the spatiotemporal variations in the potential evapotranspiration (PET) across the study area using cross-wavelet transforms and the time-varying Budyko framework. PET sensitivity was assessed on various climatic factors to explore the potential drivers of PET changes. Finally, the contributions of PET were quantified for the runoff variations across different periods. The results indicated: 1) There were some significant differences in spatial distribution and trends in the multi-year average PET and climate factors from 1965 to 2019. Multi-year average PET and sunlight hours (SH) exhibited a spatial pattern of higher in the northeast and lower in the southwest. While the maximum and minimum relative humidity (RHmax and RHmin) showed the opposite trends. Additionally, the temperature generally increased on an annual scale, while the SH and wind speed (WS) decreased across a broad area. The average annual WS of all vegetation types was below 1.8 m/s. The significant reduction in the annual WS was concentrated mainly in the vegetation types, such as the broadleaved forest, cultivated vegetation, shrub, coniferous forest, and grass (P<0.05). It was also found that the differential modes were used to accurately simulate the annual variation of PET in the Qinling Mountain Area on an annual scale, with the coefficient of certainty R² (0.96) between the calculated and the actual value. 2) The annual PET and climatic factors exhibited different patterns of change across various vegetation types. Specifically, there was a significant decrease in the SH in the broadleaf forests and cultivated vegetation areas, with the spatially uneven distribution of relative humidity change trends (P<0.05). The sensitivity of annual PET to annual SH showed an overall pattern of low in the north and high in the east and west in spatial distribution, with a value range of -0.04~0.04. However, there was no spatial distribution pattern in the sensitivity of PET to RHmax. The highest sensitivity of annual PET to multiple climatic factors was found in the grassland vegetation. There was a great variation in the duration and scope of the influence of topographical factors, solar activity, and atmospheric circulation on the annual PET. The PET showed a highly significant decreasing trend with the increasing elevation and slope (P<0.001). Among them, 98.73% of meteorological stations shared a negative correlation between annual PET and the Arctic Oscillation index (AO index), and 20.25% shared a significant negative correlation (P<0.05). 3) Furthermore, the three northern foothill watersheds showed a decreasing trend in the annual runoff depth among the six typical watersheds. While two of the three southern foothill watersheds exhibited an increasing trend. There was a generally relatively small impact of PET on the runoff variation in the different typical watersheds and sliding window periods. The variation in the vegetation was the primary driving factor on the watershed runoff. The sustainable utilization of water resources in the mountain watersheds can greatly contribute to ecological protection and high-quality development in the Yellow River and Yangtze River Basins. Continuous monitoring and adaptive management were also highlighted in response to the future climates. The multiple types of data and reliable hydrological analysis were integrated to fully understand the complex interactions between climatic factors and hydrological processes in mountainous regions. [ABSTRACT FROM AUTHOR]

ABSTRACT Interpolated grids of historical climate variables are widely used in climate change impact and adaptation research. Here, we contribute monthly historical time series grids since 1901 for our data product ClimateNA, which integrates historical data and future projections to generate high‐resolution gridded data and point estimates for North America. The historical climate grids in this study are based on interpolations of monthly anomalies (change factors) with thin‐plate splines, but a novel aspect is that we rely on high‐quality 1961–1990 normal estimates from ClimateNA to serve as reference for the change factor calculations instead of the reference being derived from station data itself. This allowed us to utilise records from 66,282 climate stations for interpolations, regardless of their temporal coverage. Another aspect that deviates from standard practice is that we reduce overfitting by optimising thin‐plate splines at a 0.5° grid level instead of fitting weather station observations directly. The high‐resolution grids generated with this approach compared favourably with other time series products, such as Daymet and advanced multi‐source products, such as MSWEP, in statistical and mapped visual comparisons, and provide additional historical coverage since the beginning of the 20th century. [ABSTRACT FROM AUTHOR]

Wind downscaling is crucial for refining coarse‐scale wind estimates, improving local‐scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more efficient statistical downscaling, whereas it often overlooks inter‐variable and inter‐station spatial correlations. Addressing this, we propose TerraWind, a deep learning‐based downscaling method for complex terrain regions. TerraWind enhances accuracy by incorporating topographic factors and inter‐station linkages, capturing wind field interactions with terrain at multiple scales. Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6% $\%$ and 33.3% $\%$, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). Furthermore, TerraWind achieves an average reduction of 35.3% $\%$ in wind speed MAE and 25.6% $\%$ in wind speed RMSE compared to four deep learning models (Wind‐Topo, DeepCAMS, RCM‐emulator, and Uformer). Plain Language Summary: High computational costs pose a significant challenge in achieving high‐resolution wind grids. Consequently, it is crucial to identify a rapid and effective downscaling approach. In this study, we introduce TerraWind, a novel deep learning‐based downscaling model. We selected Eastern China as the experimental region, the data set is spanning from 16 November 2020, to 31 December 2022, with validation against ground truth meteorological station observations. The findings highlight a notable reduction in mean absolute error and root mean square error in wind speed downscaling. Notably, TerraWind exhibits exceptional performance even amidst extreme wind speed conditions, underscoring the method's robustness. Key Points: A deep learning‐based model is proposed to downscale the surface wind field on grids from kilometer resolution in complex terrain regionsThe proposed model incorporates station correlations and introduces a GNN‐based method to improve the downscaling qualityThe experiment of the model in Eastern China region results in a 24.4%–33.3% reduction in speed Root Mean Square Error compared to the baseline models [ABSTRACT FROM AUTHOR]

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]

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]

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]

Conventional spatial interpolation methods for meteorological data are usually based on linear interpolation. However, with the improvements in the temporal and spatial resolution of observational data, local neighbouring stations are susceptible to the influence of underlying surface changes and high terrain gradients. Moreover, for interpolation at a single time point, the inability to extract continuous change information effectively from adjacent times limits the interpolation performance. In this paper, an improved hybrid deep learning‐kriging method is proposed that combines a graph neural networks (GNNs) prediction model with the kriging interpolation algorithm. The GNNs considers dynamic changes over time and combines spatial and temporal information to estimate (interpolate) meteorological data at target weather stations using reanalysis data as input. The experimental results show that the hybrid method exhibits good performance in interpolating station data in complex terrain areas and under uneven surface conditions. The interpolation effectiveness of this method is markedly improved compared to that of traditional kriging methods. Moreover, when applied to station‐to‐grid interpolation, the hybrid method still provides better interpolation results than those of kriging methods. Therefore, this research provides a new method and perspective for meteorological data interpolation. [ABSTRACT FROM AUTHOR]

Studies on the long-term effects of rising temperature by climate change on mental health are limited. This study investigates the influence of temperature rise on the prevalence rate of depressive symptoms according to district type and age group in Korea. This cross-sectional study included 219,187 Korea Community Health Survey 2021 participants. Yearly average temperature and yearly average temperature difference are the main exposures of this study. Temperature difference was calculated by subtracting the historical average temperature in 1961–1990 (climate normal) from the yearly average temperature. The main outcomes are moderate depressive symptoms measured by Patient Health Questionnaire-9. Multilevel analyses were conducted to estimate the association between temperature factors and depressive symptoms. 7491 (3.4 %) participants reported moderate depressive symptoms, and 99,653 (69.9 %) participants lived in an urban district. The odds of depressive symptoms increased with 1 °C increase in temperature difference for all participants, adult participants aged 19–40, and participants who lived in same metropolitan area for 20 years or more (aOR = 1.13, CI: 1.04–1.24, aOR = 1.14, CI: 1.02–1.24, and aOR = 1.15 CI: 1.04–1.27). The association between temperature difference and depressive symptoms was consistent among urban districts participants. Due to the study's cross-sectional nature, the temporal association between regional and individual factors and depressive symptoms could not be assessed. Limited number of weather stations, especially among less populated in-land areas, may limit the accuracy of this study. Conclusion: The increase in temperature compared with historical average is associated with increased likelihood of depressive symptoms, especially for the adults aged 19–40 years old. More study on the long-term impact of climate change on mental health is needed to determine effective responses to climate change. [Display omitted] • Temperature rise over long-term average by climate change may affect mental health. • Temperature rise was associated with increased likelihood of depressive symptoms. • Early adults, urban area, and long-time residents were particularly vulnerable. [ABSTRACT FROM AUTHOR]

The Euphrates is the largest river in Iraq. The relationship between runoff and precipitation in the Euphrates River Basin has changed due to the changing climate and increasing human activities, such as increased water consumption, irrigation projects, and dam construction. This study identified the leading causes of these changes and detected abrupt changes in hydro-climatic variables. Data from 19 weather stations and two hydrological stations between 1981 and 2021 was used to examine the nature of these changes. The four sub-catchments of the river basin were studied using the sequential Mann-Kendall test analysis to identify temporal trends and abrupt changes. An annual trend test for non-parametric trends at the basin scale revealed decreased precipitation and runoff over the past 40 years; the increased temperature has potential evaporation since 1981. The stations that showed a significant decrease in annual runoff were mainly located south of the studied river and were primarily affected by human activity. Regression analysis suggests that the decline in runoff after the abrupt change in 1996 may have been primarily caused by human activity. [ABSTRACT FROM AUTHOR]

Observational data of the Earth's weather and climate at the level of ground-based weather stations are prone to gaps due to a variety of causes. These gaps can inhibit scientific research as they impede the use of numerical models for agricultural, meteorological and climatological applications as well as introducing analytic biases. In this research, different machine learning techniques are evaluated together with traditional approaches to gap filling automated weather station data. When filling gaps for a specific data stream, data from neighbouring weather stations are used in addition to reanalysis data from the European Centre for Medium-Range Weather Forecasts atmospheric reanalyses of the global climate, ERA-5 Land. A novel gap creation method is introduced that provides 100% coverage in sampling the dataset while ensuring that the sampled data are randomly distributed. Gap filling across a range of different gap lengths and target variables are compared using a range of error functions. The variables selected for modelling are mean air temperature, dew point, mean relative humidity and leaf wetness. Our results show that models perform best on gap-filling temperature and dew point with worst performance on leaf wetness. As expected, model performance decreases with increasing gap length. Comparison between machine learning and reanalysis approaches show very promising results from a number of the machine learning models. [ABSTRACT FROM AUTHOR]

Photovoltaic module temperature and solar irradiance are two essential parameters that greatly affect the performance of solar plants. The measured information concerning these parameters is needed to size as well as predict future energy production of the plant. These data are often available at hourly intervals or more from meteorological stations and are expensive to acquire depending on the number of data points needed or from websites linked to satellites. Consequently, a method that can provide data at smaller time intervals is required to capture changes in irradiance and temperature. This paper presents a simple and cost-effective datalogger that measures the irradiance, relative humidity, module, and environmental temperatures. It used a 50 W photovoltaic module as an irradiance sensor. LM35 and DHT22 sensors were used for PV module and ambient temperature measurements, respectively. An interrupt service routine function implemented with the Arduino Mega microcontroller ensured a repetitive switching sequence of parallel resistance networks and the storage of desired current and voltage coordinates every 4s from 6 a.m. to 6 p.m. The irradiance computed was based on power at the maximum point with a load-switching network and in a short-circuit condition. The entire cost of the datalogger system was 153.12 euros, and major results show that the power at maximum power point method and ambient temperature give the best estimate of the photovoltaic module temperature. Consequently, the irradiance determined by the maximum power point method with ambient temperature can be used to evaluate the performance of photovoltaic modules using the single-diode model. [ABSTRACT FROM AUTHOR]

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information. [ABSTRACT FROM AUTHOR]

The distribution patterns of five categories and 183 species of bryophytes, and six physical attributes, on 92 insular rocks in three karst districts (Pangxiegou, the weather station in Shishangsenglin and Yaolancun) with different landscape properties in the Maolan National Nature Reserve, Guizhou, China, were recorded and analysed in terms of nestedness. By using matrix temperature, NODF metrics under four null models, treeNODF and permrows null model, we evaluated the taxonomic and phylogenetic nestedness at a small scale and explored possible underlying mechanisms. We found 1) a significant taxonomic and phylogenetic nestedness among the bryophytes and their main categories on these insular rocks, not only in the whole study region but also in each of the three districts; 2) higher taxonomic nestedness for mosses than for liverworts, and higher for acrocarpous mosses than for pleurocarpous mosses, indicating that nestedness of bryophytes on insular rocks in karst regions is taxon‐dependent; 3) rock area, habitat amount and height are the three main determinants of taxonomic and phylogenetic nestedness of bryophytes, while phylogenetic nestedness overall followed the same processes as taxonomic nestedness; 4) selective extinction and habitats nestedness were the two mechanisms accounting for the small‐scale nested pattern of bryophytes on insular rocks; 5) the determinants and mechanisms of taxonomic and phylogenetic nestedness varied among bryophytes in different landscapes and ecological habitats, thus being context‐dependent. Our results indicate that 1) large rocks with rich microhabitats should be given priority for the conservation of saxicolous bryophytes; 2) the conservation of different categories of saxicolous bryophytes that have a limited congruence will require a multiple criteria approach that incorporates phylogenetic diversity differences into reserve planning; 3) the conservation strategies for bryophyte diversities in different ecosystems should be adapted to local conditions and differences among bryophyte groups. [ABSTRACT FROM AUTHOR]

Asphalt binder's performance is highly sensitive to temperature fluctuations and climatic conditions. The selection of an inappropriate asphalt binder leads to considerable damage and impacts roads' longevity. The current selection practice of asphalt binder in the United Arab Emirates (UAE) is mainly according to the penetration grading system, which does not consider local temperature conditions. The Superpave Performance Grade (PG) system considers extreme pavement temperatures to define appropriate asphalt binder PG. This study aimed at developing the Superpave asphalt binder PG map for the UAE. The Strategic Highway Research Program (SHRP) and Long-Term Pavement Performance Program (LTPP) Superpave models were used to calculate the pavement temperatures employing air temperature records collected from 20 weather stations across the UAE. The result of the Superpave PG map highlights three distinct asphalt binder grades at 98% reliability: PG 76-10, PG 70-10, and PG 64-10. The PG 76 -10 asphalt binder is the most prevalent PG, covering more than 85% of the UAE map. However, the current construction practice utilizes penetration grades 40/50 and 60/70, through both asphalt binders are equivalent to PG 64-xx. This necessitates the use of different modifier technologies to achieve the Superpave requirements of PG 76-10 and PG 70-10. [ABSTRACT FROM AUTHOR]

The difference of canopy-air temperature can indirectly monitor the variation of tea tree heat and moisture. However, the time lag effect between the tea tree canopy temperature and atmospheric temperature of tea plantation will affect the monitoring effect. In order to explore the time lag effect and the variation law between canopy temperature and atmospheric temperature, the variation characteristics of tea tree canopy temperature and atmospheric temperature of tea plantation during different tea picking seasons and different weather types were analyzed based on the monitoring data of microclimate station in tea plantation and near national meteorological station in Wuyuan from March to September in 2020. The simulated models of daily average canopy temperature and atmospheric temperature of tea plantation according to different weather types were established through the linear regression method and tested to provide data support for tea meteorological service. The results showed that: 1) the diurnal variation of tea tree canopy temperature and atmospheric temperature of tea plantation showed an obviously single-peak trend during different tea picking seasons and different weather types, while the change intensity of canopy temperature was greater than that of atmospheric temperature of tea plantation, and the peak time of canopy temperature was generally about 1h earlier than that of atmospheric temperature of tea plantation. (2) The point temperature difference of tea tree canopy and atmospheric temperature of tea plantation within 24h a day were generally ranked as spring tea>autumn tea>summer tea, sunny days>cloudy days>rainy days. In general, the canopy temperature was above or near to the atmospheric temperature of tea plantation around noon, while the canopy temperature was lower than the atmospheric temperature of tea plantation at all times in rainy days. (3) From the aspects of daily average temperature, it showed that the canopy was generally lower (1-2°C) than which in the atmosphere, but the changing trend was the same. (4) All kinds of daily average temperature prediction models were approved by 0.01 level significant test, which meaned the simulation effect were good generally. What's more, the simulation effect of the atmospheric temperature of tea plantation prediction models were better than that of canopy temperature. In addition, under different weather types, the optimal effect of the prediction models were demonstrated in rainy days, followed by sunny days, and relatively poor in cloudy days. [ABSTRACT FROM AUTHOR]

Flowers and fruits collected from the two major apple varieties ('Gala' and 'Fuji') grown in Ningxia during the bud, full-bloom, fruiting and young fruit setting stages were used as test material for the cryogenic treatments, and then the freezing rates of the ovaries under different cryogenic treatments were counted and analyzed. The Logistic equations established for the 20%, 50% and 80% freeze rates were set as the indicators of light, moderate and severe frost damage, respectively. The daily minimum temperature data collected in 19 meteorological stations from 1981 to 2022 were used to analyze the scope, frequency, intensity, and risk characteristics of disaster--causing factors of frost occurrence in Ningxia from the point of view of climatic disaster, by the means of mathematical statistics and spatial analysis. The classification of frost risk was completed by analyzing the difference in risk of late frost damage to apples in various regions of Ningxia, based on a combination of the vulnerability index and the exposure index of the affected apple. The results showed that the late frost damage to 'Gala' and 'Fuji' apple in Ningxia often occurred between 20 April and 10 May during the flowering and fruit setting period. The high-risk areas of late frost damage to 'Gala' and 'Fuji' apple were mainly located in the south-central areas close to the mountain ranges, among which Jingyuan, Xiji, and Longde were the highest risk areas, with a risk index of Hi>0.3. The suitable areas for apple cultivation were mainly in Pingluo, the southeastern part of Yinchuan, the Litong district of Wuzhong, the western part of Linwu, and parts of Zhongwei city, with an exposure index of 0.7-1.0. The high risk areas for 'Gala' were located in the western part of Lingwu, the western part of Shapotou, and the central parts of Zhongning county in the northern Ningxia, and Xiji county and Longde county in the southern Ningxia. The high risk area for Fuji was located in the western part of Lingwu, Xiji county and most part of Longde county. Unsuitable areas for 'Gala' and 'Fuji' apple were mainly located along the Helan, Luoshan, Nanhua and Liupan mountain areas. In terms of frost resistance, Gala apple is stronger than 'Fuji' apple, and the resistance of 'Gala' apple is decreasing as the phenology evolves, since the phenological phase of 'Gala' is earlier than that of 'Fuji' apple. According to the frost damage zoning maps, 'Gala' is in higher risk than 'Fuji'. The zoning results reflected the actual distribution of late frost risk for 'Gala' and 'Fuji' apple cultivated in Ningxia. The zoning map is providing a scientific baseline for the proper distribution of apply cultivation as well as for the disaster prevention and mitigation in Ningxia. [ABSTRACT FROM AUTHOR]

To explore the effect of greenhouse microclimate on the growth of protected tomatoes, two models were established: one for simulating the transverse diameter and another for the single fruit weight. These models incorporated accumulated radiation-heat and the rice clock model, utilizing data on the transverse diameter and single fruit weight of protected tomatoes, as well as concurrent meteorological data including illumination, temperature, and humidity from the greenhouse at Hefei Agricultural Meteorological Experiment Station for the years 2022 and 2023. The accuracy of both was verified using experimental data from 2022. Results indicated that both models effectively simulated the transverse diameter and single fruit weight of protected tomatoes, respectively. The transverse diameter model achieved a root mean square error (RMSE) of 1.03mm, a mean absolute error (MAE) of 0.84mm, and a mean relative error (MRE) of 7.5%. The single fruit weight model achieved an RMSE of 692.59mg, an MAE of 395.44mg, and MRE of 8.2%. Decision coefficients exceeded 0.98 for both models, suggesting they possess practical value and can provide a theoretical foundation for environmental regulation in protected tomato cultivation. [ABSTRACT FROM AUTHOR]

Snow avalanches are a major natural hazard threatening human life and infrastructure in mountainous areas. They have a sudden onset and involve the rapid transport of large masses of snow and ice down on steep slopes. Thus, it is essential for risk management activities to understand avalanche activity, frequency and triggers. In this study, the dendrogeomorphic method was used to analyse an avalanche path in the Țarcu Mountains (the Southern Carpathians) in order to reconstruct the spatio-temporal activity of past snow avalanches. The reconstruction was based on the dating of growth disturbances caused by the mechanical impact of snow avalanches on trees. A total of 186 increment cores were analysed, resulting in the identification of 374 growth disturbances, including traumatic resin ducts, reaction wood, growth suppression and scars. In a chronology spanning 101 years in Picea abies, 13 events with It between 10-20% and 6 events with It between 20-40% were reconstructed over the period 1965-2021. The frequency of snow avalanche events was calculated, resulting in an average of 17.7 years. The climatic parameters were analysed for the event years exhibiting the strongest signal. The occurrence of avalanches was associated with warmer weather and rainy days. Event year 2010 is evidenced by a tragic incident in which two individuals lost their lives in the vicinity of the Țarcu weather station. Eleven events are synchronous with those analysed in other avalanche paths, while the event year 2005 is synchronous in nine other avalanche paths. [ABSTRACT FROM AUTHOR]

Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled modeling system, the numerical weather prediction AROME model and the land-surface model SURFace EXternalisée in a stand alone mode (SURFEX-SA), in forecasting air temperatures at high resolutions ( 2.5 km to 100 m) across four Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck). The system is updated with the, according to the author's knowledge, most accurate land use and land cover input to evaluate the added value of incorporating detailed urban environmental representations. The analysis focuses on the years 2019, 2023, and 2024, examining both summer and winter seasons. SURFEX-SA demonstrates improved performance in specific scenarios, particularly during nighttime in rural and suburban areas during the warmer season. By comprehensively analyzing this prediction system with operational and citizen weather stations in a deterministic and probabilistic mode across several time periods and various skill scores, the findings of this study will enable readers to determine whether high-resolution forecasts are necessary in specific use cases. [ABSTRACT FROM AUTHOR]

This research examines the impact of various parameterization settings within the Weather Research and Forecasting (WRF) model on the accuracy of short-term weather forecasts for Poland. The study focuses on the sensitivity of key meteorological variables—namely, air temperature, wind speed, relative humidity, and atmospheric pressure—to different combinations of physical parameterization schemes. Utilizing data from the Global Forecast System (GFS) spanning 2019 to 2022, a series of model simulations were conducted with support from the Poznań Supercomputing and Networking Center (PCSS). To assess the model's performance across different weather stations, statistical metrics such as the mean absolute error (MAE) and root mean square error (RMSE) were employed. The findings indicate that the configuration labeled "p2" produced the most accurate forecasts for temperature, wind speed, and atmospheric pressure, achieving MAE values of 1.5 °C, 1.6 m/s, and 2 hPa, respectively. However, forecast inaccuracies were notably higher in mountainous regions, particularly regarding wind speed. These results underscore the importance of selecting appropriate parameterization settings tailored to regional characteristics, as different configurations can significantly impact the forecast accuracy, especially in complex terrains. This study contributes to the understanding of short-term weather forecasting models for Central Europe, offering potential pathways for improving localized forecast accuracy. [ABSTRACT FROM AUTHOR]

Using the daily precipitation data from the global precipitation measurement (GPM) satellite and meteorological stations from 2001 to 2020, the present study has analyzed the seasonal and interannual spatial-temporal variations of the precipitation over the Three-River Headwaters region. The rainfall of the Three-River Headwaters region is verified to have obvious spatial-temporal variations and is mainly concentrated in summer. Then, the empirical orthogonal function (EOF) method is performed and reveals that the summer precipitation in the Three-River Headwaters region mainly shows three patterns, e.g., the "north-south dipole pattern," "northeast--southwest diploe pattern," and "east--west dipole pattern," among which the northeast--southwest diploe pattern has a strong correlation with the mid-latitude westerlies and summer monsoon. Further analysis reveals that the northeast-southwest diploe pattern of summer precipitation is significantly related to the tripolar sea surface temperature (SST) anomalies (SSTAs) of the North Atlantic Ocean in the preceding winter and the tropical Indian Ocean SSTAs in the simultaneous summer. In the preceding winter, a wave-like pattern zonally propagating along the mid-latitude westerlies is triggered downstream by the North Atlantic tripolar SSTAs. One of the cyclones generated by the wave-like pattern coincidentally locates in Northeastern China and forms a deep northeastern low system in summer. Moreover, the warming of the tropical Indian Ocean SSTAs in summer weakens the Walker circulation, which leads to the strengthening and westward extension of the Western Pacific subtropical high (WPSH). Northerly anomalies from the deep northeastern cyclonic anomalies and southwesterly anomalies from the enhancing WPSH exactly met at the eastern Three-River Headwaters region. Hence, more water vapor and ascending motion anomalies likely appear over the east part of the Three-River Headwaters region. Opposite anomalies cover the southwestern Three-River Headwaters region and its surroundings. Then, the northeast-southwest reverse diploe pattern of the summer rainfall in the Three-River Headwaters region is directly produced. [ABSTRACT FROM AUTHOR]

Based on rain gauge data during 2008-2021 from national meteorological observation stations, this study investigated the performance of the precipitation field from the 1-km-resolution version of the China Atmospheric Realtime Analysis (CARAS) over Hubei from the perspective of climatology, multiple-time scale variations, as well as fusion accuracy and detection capability at multiple temporal scales. The results show that CARAS precipitation can reproduce the spatial distribution patterns of climatological seasonal precipitation and rainy days well over the whole of Hubei compared with observational (OBS) precipitation, albeit deviations exist between CARAS and OBS in terms of magnitude. Moreover, high correlation and consistency between CARAS and OBS can be found in multiple-time scale variations over Hubei, with correlation coefficients of interannual, seasonal, and diurnal variation generally exceeding 0.85, 0.98, and 0.95, respectively. Furthermore, CARAS has a relatively higher fusion accuracy in summer and winter, and stronger/weaker detection capability in spring/winter at a daily scale. However, the detection capability of CARAS at an hourly scale is weaker than that at a daily scale. With different precipitation intensity levels considered, CARAS daily precipitation shows relatively higher fusion accuracy in estimating moderate and heavy rain, and better detection capability in capturing no rain events. The variations of accuracy metrics and detection metrics under different precipitation intensities at an hourly scale generally resemble those at a daily scale. However, CARAS precipitation at an hourly scale shows a relatively lower fusion accuracy and weaker detection capability compared with that at a daily scale. This paper provides an insight into the characteristics of systematic deviations in CARAS precipitation over Hubei, which will benefit relevant applications of CARAS in meteorological operations over Hubei and the improvement of CARAS in the future. [ABSTRACT FROM AUTHOR]

The Tarim River Basin (TRB) faces significant ecological challenges due to global warming, making it essential to understand the changes in the climates of its sub-basins for effective management. With this aim, data from national meteorological stations, ERA5_Land, and climate indices from 1961 to 2021 were used to analyze the temperature and precipitation variations in the TRB and its sub-basins and to assess their climate sensitivity. Our results showed that (1) the annual mean temperature increased by 0.2 °C/10a and precipitation increased by 7.1 mm/10a between 1961 and 2021. Moreover, precipitation trends varied significantly among the sub-basins, with that in the Aksu River Basin increasing the most (12.9 mm/10a) and that in the Cherchen River Basin increasing the least (1.9 mm/10a). Moreover, ERA5_Land data accurately reproduced the spatiotemporal patterns of temperature (correlation 0.92) and precipitation (correlation 0.72) in the TRB. (2) Empirical Orthogonal Function analysis identified the northern sections of the Kaidu, Weigan, and Yerqiang river basins as centers of temperature sensitivity and the western part of the Kaidu and Cherchen River Basin as the center of precipitation sensitivity. (3) Global warming is closely correlated with sub-basin temperature (correlation above 0.5) but weakly correlated with precipitation (correlation 0.2~0.5). TRB temperatures were found to have a positive correlation with AMO, especially in the Hotan, Kashgar, and Aksu river basins, and a negative correlation with AO and NAO, particularly in the Keriya and Hotan river basins. Precipitation correlations between the climate indices were complex and varied across the different basins. [ABSTRACT FROM AUTHOR]

We studied the atmospheric and streamwater-quality responses to the 14 October 2023 annular solar eclipse in 7 streams in the Jemez River basin of northern New Mexico, USA. Study sites ranged from 1st-order streams to the 4th-order Jemez River. During the eclipse, across 7 weather stations, we recorded a mean decrease of 92% insolation, a 6.7°C decline in air temperature, a 16.2% increase in relative humidity, and a 1.2 m/s drop in mean wind speed. During the eclipse, we observed distinct, small-magnitude, short-duration changes in the diel cycles of stream temperatures (mean decline of 0.67°C across 7 streams), dissolved O2 (decline of 0.22 mg/L in 5 streams showing responses), and pH (decline of 0.06 in 6 streams showing responses). Streamwater turbidity and conductivity did not show consistent responses during the eclipse. We suggest that decreased insolation directly reduced water temperature and concomitantly curtailed stream periphyton photosynthesis, leading to reduced dissolved O2 production and increased dissolved CO2 concentrations (lowering pH). Declines in dissolved O2 and pH were greatest in 1st- and 2nd-order streams with high sun exposure, low gradients, and the widest arrays of aquatic vegetation (periphyton, filamentous algae, aquatic macrophytes, and emergent vegetation). Streams with morning shade from topography or tree-lined banks had responses that were smaller in magnitude. Stream basin area, discharge volume, and current velocity were not related to the magnitude of stream responses. Although the abiotic and biotic streamwater-quality responses to the eclipse were clear and measurable, the small magnitudes of the changes were well within the realm of diel variation and likely had minimal effect on the ecology of the stream ecosystem. [ABSTRACT FROM AUTHOR]

The cloud cover and meteorological parameters serve as fundamental criteria for an astronomical observatory working in optical and infrared wavelengths. In this paper, we present a systematic assessment of key meteorological parameters at the Lenghu astronomical observing site on the Tibetan Plateau. The data sets adopted includes the meteorological parameters collected at the local weather stations at the site and in the Lenghu Town, the sky brightness acquired by the Sky Quality Meters and all-sky images from a digital camera, the ERA5 reanalysis data base, and global climate monitoring data. From 2019 to 2023, the fractional observable time of photometric condition is 69.70 per cent, 74.97 per cent, 70.26 per cent, 74.27 per cent, and 65.12 per cent, respectively, which is influenced by a variety of meteorological parameters. Large-scale air–sea interactions affect the climate at Lenghu site, which in fact delivers a clue to understand the irregularity of 2023. Specifically, precipitable water vapour at Lenghu site is correlated to both the westerly wind index and the summer North Atlantic Oscillation index, the yearly average temperature of Lenghu site is observed to increase significantly during the occurrence of a strong El Niño event, and the relative humidity anomaly at Lenghu site is correlated to the Pacific Decadal Oscillation index. The decrease of fractional observing time in 2023 was due to the ongoing strong El Niño event and relevant global climate change. We underscore the substantial role of global climate change in regulating astronomical observing conditions and the necessity for long-term continuous monitoring of the astronomical meteorological parameters at Lenghu site. [ABSTRACT FROM AUTHOR]

This study introduces an improved Ski Climate Index (SCI) designed to assess skiing suitability in China by applying fuzzy logic. Using daily meteorological data from 733 weather stations for the periods 1961–1990 and 1991–2020, the study identifies significant changes in SCI distribution over time. Additionally, a coupled analysis is performed, integrating the SCI results with the distribution and spatial vitality of 389 ski resorts in China. This analysis provides a comprehensive understanding of the interplay between actual ski resources and the ongoing evolution of the skiing industry in China and three significant results:1) The snow module has a major impact on SCI distribution, while other non-snow natural elements, such as sunshine duration, wind speed, and thermal comfort, influence the overall SCI assessment less; 2) High SCI values are concentrated in Northwestern and Northeastern China, with increased ski climate resources being observed in Shaanxi-Gansu-Ningxia, Southwest Tibet, and Sichuan due to climate change and noticeable declines in the Southern regions of Northeast China.; 3) In terms of the distribution and vitality of ski resorts, the SCI also partially reflects the development of ski resorts. This skiing suitability model uses climate resources to offer valuable insights for key decision-making in resort development and operation, thereby supporting advancement of the ice-snow economy. [ABSTRACT FROM AUTHOR]

At present, computationally intensive numerical weather prediction systems based on physics equations are widely used for short-term weather forecasting. In this paper, we investigate the potential of accelerating the Weather Research and Forecasting (WRF-ARW) model using machine learning techniques. Two main approaches are considered. First, we assess the viability of complete replacing the numerical weather model with deep learning models, capable of predicting the full range forecast directly from basic initial data. Second, we consider a "super-resolution" technique involving low-resolution WRF computation and a machine learning based downscaling using coarse-grid forecast for conditioning. The process of downscaling is intrinsically an ill-posed problem. In both categories, several prominent and promising machine learning methods are evaluated and compared on real data from a variety of sources. for the Moscow region Namely, in addition to the ground truth WRF forecasts that were utilized for training, we compare the model predictions against ERA5 reanalysis and measurements from local weather stations. We show that deep learning approaches can be successfully applied to accelerate a numerical model and even produce more realistic forecasts in other aspects. As a practical outcome, this study offers empirically validated guidance for the selection and application of deep learning methods to accelerate the computation of detailed short-term atmospheric forecasts tailored to specific needs. [ABSTRACT FROM AUTHOR]

This study quantifies the impact of using an updated Land Use/Land Cover (LULC) dataset in the Weather Research and Forecasting (WRF) model on simulating temperature, relative humidity (RH), heat index (HI), wind speed (WS) and wind direction (WD) over Metro Manila (MM), Philippines. The 2015 LULC dataset from the National Mapping and Resource Information Authority (NAMRIA) of the Philippines was used to update the default LULC datasets in the WRF model. Model outputs from four simulations for April 2015 using the default USGS and MODIS LULC, and their updated counterparts (USGS_NAMRIA and MODIS_NAMRIA) were compared with data from 28 automated weather stations distributed across MM. The results show that the MODIS LULC experiment performed better in simulating the overall temperature, HI, and WS, but performed worse in simulating RH and WD, compared to the USGS LULC experiment. On the other hand, the two experiments with updated LULC have nearly similar results for all simulated variables. Both experiments show lower mean percentage biases (MPB), lower Root Mean Square Errors (RMSE), and higher Index of Agreement (IOA) with respect to the observational data in temperatures and nighttime HI, compared to their default counterparts. However, they worsen the overall RH. All simulations tended to overestimate WS, though the overall WS in the USGS_NAMRIA and daytime WS in MODIS_NAMRIA simulations improved, compared to their default counterparts. Lower MB and RMSE values are also evident in the simulated daytime WD in the updated experiments. The results of this study demonstrate potential improvements in reducing biases in temperature (0.6 to 2.4%), nighttime HI (5 to 7%), wind speed (25 to 38%) and wind direction (up to 4°) when using an updated LULC in the WRF model over MM during April 2015. This information will be useful in climate research and weather forecasting. [ABSTRACT FROM AUTHOR]