Effects of Dust Event Days on Influenza: Evidence from Arid Environments in Lanzhou by Ling Zhang, Sheng Li, Bo Wang, Ce Liu, Li He, Xiaobing Shan, Kai Zhang, Bin Luo

“…A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters, and dust events were conducted from 2014 to 2019 in Lanzhou, China. …”
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Regional modeling of surface solar radiation, aerosol, and cloud cover spatial variability and projections over northern France and Benelux by G. Chesnoiu, I. Chiapello, N. Ferlay, P. Nabat, M. Mallet, V. Riffault

“…Our analysis relies on the National Centre for Meteorological Research–Limited Area Adaptation Dynamic International Development v6.4 (CNRM-ALADIN64) regional climate model at 12.5 km resolution, which includes an interactive aerosol scheme. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. …”
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Combining ability of CMS-lines of grain sorghum based on A1, A2, A3, A4, 9E and M-35-1A types of сytoplasmic male sterility by O. P. Kibalnik

“…The tests were conducted on the experimental field of “Rossorgo” in 2015–2016. Meteorological conditions during the studies differed in the amount of precipitation and the sum of active temperatures. …”
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Detection of Bartonella spp. in foxes' populations in Piedmont and Aosta Valley (NW Italy) coupling geospatially-based techniques by Annalisa Viani, Tommaso Orusa, Sara Divari, Stella Lovisolo, Stefania Zanet, Riccardo Orusa, Enrico Borgogno-Mondino, Enrico Bollo

“…The Tasseled Cap Wetness Index (TCW), an indicator of landscape moisture, was calculated for each meteorological season. The study found that Bartonella spp. infections in foxes were positively associated with higher TCW values (>0.7). …”
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Dynamically triggered seismicity in Japan following the 2024 M w 7.5 Noto earthquake by Like An, Bogdan Enescu, Zhigang Peng, Masatoshi Miyazawa, Hector Gonzalez-Huizar, Yoshihiro Ito

“…Our results show a relatively widespread activation of small earthquakes—none of them listed in the Japan Meteorological Agency (JMA) earthquake catalog—that were triggered by the passage of the mainshock surface waves in many regions of Japan. …”
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Unveiling the effects of post-monsoon agricultural biomass burning on aerosols, clouds, and radiation in Northwest India by Pradeep Khatri, Tadahiro Hayasaka, Prabir K. Patra, Husi Letu, Hiren Jethva, Sachiko Hayashida

“…In this study, we aim to address this research gap by analyzing fire, meteorological parameters, aerosol, cloud, and radiation data spanning nearly two decades (2002–2021), obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, Modern-Era Retrospective Analysis Research and Applications, Version 2 (MERRA-2), and the Fifth Generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5). …”
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Evaluation and Adjustment of Precipitable Water Vapor Products from FY-4A Using Radiosonde and GNSS Data from China by Xiangping Chen, Yifei Yang, Wen Liu, Changzeng Tang, Congcong Ling, Liangke Huang, Shaofeng Xie, Lilong Liu

“…The geostationary meteorological satellite Fengyun-4A (FY-4A) has rapidly advanced, generating abundant high spatiotemporal resolution atmospheric precipitable water vapor (PWV) products. …”
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Simulation Effect Evaluation of CMIP6 Models on Climatic Elements in Huai River Basin by WU Chen, WANG Jingcai, SHAO Junbo, LI Xiangyu, WANG Wenyue

“…The general climate model (CMIP6) is the main means of large-scale simulation and prediction of future climate changes,but the simulation quality of its model data and applicability in different research regions need to be evaluated.Based on the measured data of 19 meteorological stations in the upper and middle reaches of the Huai River Basin from 1960 to 2014,this paper systematically evaluates the simulated climate data of 19 CMIP6 models using five precision indicators.The indicators include equidistant cumulative distribution function method,mean,dispersion coefficient (C_v),Pearson correlation coefficient (PCC),standard deviation (STD),and root-mean-square deviation (RMSE).Finally,the following conclusions are drawn:① Based on the simulation data of 19 CMIP6 models corrected for bias,the top two climate models for precipitation data simulation are preliminarily selected,including ACCESS-ESM1-5 and CMCC-ESM2.The top five climate models for simulating temperature data contain ACCESS-ESM1-5 and CMCC-ESM2;② The historical period data and measured historical climate data of the selected CMIP6 models are evaluated from two aspects of annual change trend and spatial distribution.Then,the CMIP6 model suitable for the upper and middle reaches of the Huai River Basin is verified and selected;③ Based on analyzing the influence of atmospheric circulation factors on climate,AO has a good correlation with the climate data of the upper and middle reaches of the studied basin,and the correlation between the model and atmospheric circulation data is close to the actual situation.The comprehensive evaluation shows that the most suitable models for simulating the upper and middle reaches of the Huai River Basin are ACCESS-ESM1-5 and CMCC-ESM2.The systematic evaluation of CMIP6 model data simulating the upper and middle reaches of the Huai River Basin provides theoretical and technical references for the rational selection and utilization of CMIP6 datasets for the research on climate change factors in future scenarios of the basin.…”
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Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4) by J. Feng, C. Zhao, C. Zhao, C. Zhao, C. Zhao, C. Zhao, C. Zhao, Q. Du, Z. Yang, C. Jin

“…The modification also leads to changes in meteorological fields due to alterations in radiative feedback effects of aerosols. …”
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Temporal and Spatial Evolution Characteristics of Extreme Climate Events in Grand Shangri-La Region from 1961 to 2019 by WU Yang, JIN Hanyu, CHENG Qingping

“…The Grand Shangri-La region connects the Qinghai-Tibet Plateau,the Yunnan-Guizhou Plateau,and the Hengduan Mountains and is of great geographical significance.Five rivers flow through the region,and the climate is vulnerable.Therefore,it is necessary to explore extreme climate changes and their response mechanism,so as to assess regional climate risks and achieve disaster warnings.Based on the daily temperature and precipitation data of 56 meteorological stations from 1961 to 2019,this paper analyzes changes in extreme climate indexes and their correlation with large-scale circulation index in the region by using Mann-Kendall,Sen's slope estimation,Pettitt test,and Pearson correlation analysis.The results show that:① The extreme warm index and the extreme precipitation intensity index (RX1day,R95p,R99p,and SDII) increase significantly,while the cold index (CSDI、FD、ID、TN10p、TX10p) and the number of continuous humid days decrease greatly.In terms of different seasons,the warming amplitude of most of the extreme temperature indexes in winter is higher than that in summer,and the precipitation intensity increases gradually in summer and autumn but decreases slightly in winter.② Spatially,the diurnal temperature range in the north of the Qinghai-Tibet Plateau is higher than that in the south,and the frequency of extremely high-temperature rises,with the high temperature appearing mostly in arid valleys in the south and east of the Qinghai-Tibet Plateau.The precipitation intensity is high in the south and west of the Hengduan Mountains,and persistent precipitation is strong in the Yalong River basin and the upper reaches of the Jinsha River in the north.③ Extreme climate indexes and south China sea summer monsoon index (SCSMI) are significantly correlated in the same year.In addition,there is a one-year response lag between the extreme precipitation index and Arctic oscillation (AO),North Atlantic oscillation (NAO),and Pacific decadal oscillation (PDO).The above analysis shows that the Grand Shangri-La region generally shows warming and humid climate characteristics.The frequency of heavy precipitation in flood season and warm winter events increases and is closely related to large-scale circulation.…”
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Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period by Jeong‐Heon Kim, Young‐Sil Kwak, YongHa Kim, Su‐In Moon, Se‐Heon Jeong, JongYeon Yun

“…We collected 131 days of geomagnetic storm cases from January 1, 2009 to December 31, 2019, provided by the Japan Meteorological Agency's Kakioka Magnetic Observatory, and obtained the interplanetary magnetic field Bz, Dst, Kp, and AE indices related to the geomagnetic storm corresponding to each storm date from the OMNI database. …”
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Estimation of the Uncertainty due to Each Step of Simulating the Photovoltaic Conversion under Real Operating Conditions by Anne Migan Dubois, Jordi Badosa, Vincent Bourdin, Moira I. Torres Aguilar, Yvan Bonnassieux

“…If the local measurements are not available, we can use the closest meteorological station’s records (13 for our study), and the error becomes 12.1%. …”
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Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach. by Rodrigue Govan, Romane Scherrer, Baptiste Fougeron, Christine Laporte-Magoni, Roman Thibeaux, Pierre Genthon, Philippe Fournier-Viger, Cyrille Goarant, Nazha Selmaoui-Folcher

“…The analysis encompasses a broad spectrum of variables, including meteorological, topographic, and socio-demographic factors. …”
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Modelling climate change and aridity for climate impact studies in semi-arid regions: The case of Giba basin, northern Ethiopia by Atsbha Brhane Gebru, Tesfamichael Gebreyohannes, Gebrerufael Hailu Kahsay

“…Historical data (1961–2019) from seven meteorological stations and global grided data were used for future climate projections (2020–2100) under the three emission scenarios (RCPs 2.6, 4.5, and 8.5) for the three-time horizons (2040s, 2060s, and 2080s). …”
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Machine learning-driven solar irradiance prediction: advancing renewable energy in Rajasthan by Aayushi Tandon, Amit Awasthi, Kanhu Charan Pattnayak, Aditya Tandon, Tanupriya Choudhury, Ketan Kotecha

“…Using data from MERRA-2, researchers analysed solar irradiance and meteorological parameters to develop accurate prediction models. …”
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Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. …”
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Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming by K. Nordling, K. Nordling, N. L. S. Fahrenbach, B. H. Samset

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Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving by B. M. Swiger, M. W. Liemohn, N. Y. Ganushkina, S. V. Dubyagin

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Variation Diagnosis of Maximum Precipitation in Jiangxi Province in 60 Years by WU Shaofei, WANG Qi, HUANG Binbin, JU Xiang, XU Changbao, HE Miao

“…Hourly precipitation data of 91 meteorological stations in Jiangxi Province during 1954—2012 were selected to analyze the variation of maximum precipitation in consecutive 1 h,3 h,6 h,12 h and 24 h using hydrologic variation diagnostic system (HDMS) .Firstly,in the detailed diagnosis,linear trend,Kendall and Spearman methods were used for trend test,and M-K,cumulative anomaly,ordered clustering,sliding F,sliding rank sum and other test methods were adopted for jump diagnosis.Secondly,the efficiency coefficient R² was employed to determine the final diagnosis results and conduct the spatio-temporal analysis.Finally,the spatial characteristics of the occurrence frequency of maximum precipitation greater than or equal to 16 mm,30 mm and 50 mm were analyzed.The results show the followings:① The spatial distribution characteristics of maximum precipitation in Max1 h,Max3 h,Max6 h,Max12 h and Max24 h were similar in Jiangxi Province.With the increase in duration,most stations with variations witnessed a jumping increase.Those with significant variations were more in the northern plain than in the southern mountain area,namely that the spatial distribution was dense in the north and sparse in the south.② The degree of precipitation series variation was positively and negatively correlated with elevation in the southern mountain area and the northern plain area,respectively.Studies have shown that atmospheric circulation indexes AO (Arctic oscillation),NAO (North Atlantic oscillation) and PNA (Pacific-North American oscillation) had negative,positive and negative correlations with precipitation at stations in the study area,respectively.Therefore,the atmospheric circulation indexes have correlations with the change of precipitation series in Jiangxi Province,which are one of the causes of the maximum precipitation variation in the period.③ The frequency of regional rainstorms gradually decreased with the increase in duration,and rainstorm events in the northern plain area were less than those in other mountain areas due to the influence of topographic factors.Through the analysis of hourly precipitation data of many years in Jiangxi Province,this paper is expected to provide some reference for short-term rainstorm forecast and flood warning.…”
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