In order to assess the potential impact of climate change on agriculture and develop scientific adaptation strategies, the variability characteristics and risk on agriculture of regional high temperature, drought and their compound events in Hubei Province were identified and analyzed based on daily temperature, precipitation and other observations from 76 national meteorological stations during 1994-2023. The analysis employed classification standards for regional high temperature process and monitoring and assessment methods for drought process. The results show that regional high temperature events occurred an average of 4.3 times per year, with an overall increasing trend and 61.2% of severe and strong events occurred in July and August. Regional drought events occurred an average of 1.5 times per year, showing a deceasing trend before 2010 and increasing trend thereafter, with slightly higher frequencies in winter and spring than in summer and autumn. Regional compound high temperature and drought events mainly occurred from June to August, with an significant increase in frequency after 2010. The spatial distribution of intensity and agricultural risk for regional high temperature and drought events was generally similar. High intensity and high risk areas for high temperature events were mainly located in eastern Hubei, while low intensity areas were in the southwest. For drought events, high intensity and high risk areas were mainly located in central-eastern Hubei, decreasing towards surroundings regions. The agricultural risk of compound high temperature and drought events showed a decreasing trend from east to west. The most widely distributed risk levels for regional high temperature, drought and their compound events were classified as high-risk, moderate-risk and extreme high-risk areas, accounting for 37.6%, 53.8% and 46.6% of Hubei Province’s total area, respectively. In the background of global warming and increasing frequency of extreme weather events, the probability and risk of regional extreme high temperature, drought and their compound events are expected to rise in eastern Hubei Province.

Accurate forecast of dense fog (visibility less than or equal to 500 meters) is of great significance for ensuring people's safety and reducing economic losses. Based on the ground observation data of 31 national meteorological stations, environmental monitoring station data of southern Henan, and ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) from 2019 to 2021, the spatial distribution and physical characteristics of dense fog in this area were analyzed, and 30 dense fog forecast factors were selected. The visibility classification forecast (VCF) model is trained based on the LightGBM (Light Gradient Boosting Machine) machine learning method. By inputting the forecast field data by the ECMWF model at 08:00 every day and the PM_2.5 concentration monitoring at 08:00, the 3-hour graded visibility forecast products of the national stations in southern Henan are obtained. Through the prediction test of 17 dense fog days in southern Henan from January to March 2022, it was shown that the scores of the VCF model were generally better than the visibility forecast directly output by ECMWF model. The dense fog forecast product generated based on the VCF model for the period from 20:00 to 20:00 in southern Henan can provide important reference for forecasting.

The air water vapor content and annual precipitation in China decrease from southeast coast to northwest inland, and there is a good spatial similarity between them. By studying the fitting relationship between them, it is possible to find a good statistical law and a breakthrough in the study of influence of air water vapor content on annual average precipitation. Based on this spatial similarity, the average precipitation (P) and air water vapor content (W) of 121 sounding stations from 1971 to 2000 in China were studied and the fitting formula between them was found, namely P=44.385 (W-2.66), with a highly positive correlation between the two, R²=0.8293. Further, the study on monthly average precipitation and air water vapor content over the years found a high positive correlation between the two, and the above results passed the review and verification. Air water vapor content W multiplied by the study area is the stock of air water vapor, namely the liquid volume converted from air water vapor. There are many factors affecting annual average precipitation. The minor influencing factors were ignored and the main influencing factor was found in the fitting formula. It is the stock of air water vapor, and its quantitative parameter is the air water vapor content W. The study also found that when the air water vapor content is equal to or more than 14 mm, the annual average precipitation of all stations would be equal to or more than 400 mm. The annual average water vapor content equal to or more than 14 mm is a sufficient and unnecessary condition for the average annual precipitation equal to or more than 400 mm.

Based on the gale observation data at Pudong and Nanhui national weather stations and twelve automatic weather stations and surface roughness data along the rail transit line 16 of Shanghai, the temporal and spatial characteristics of wind speed, wind direction and gust factor were studied on the rail transit line 16 and surroundings, the driving risks of train under dominant wind directions were discussed, and the suggestions of wind measurement equipment layout on elevated section of the rail transit line 16 were given. The results show that it was necessary to carry out more dense observation of wind speed and wind direction, especially pay a close attention to severe convection weathers in summer. During the influence of typhoon processes on Shanghai, the differences of wind speed between adjacent automatic weather stations near Luoshanlu station and Zhoupudong-Xinchang, Yeshengdongwuyuan-Huinandong, Shuyuan-Lingangdadao sections of the rail transit line 16 were quite obvious, and the influences of surface roughness on wind speed at Longyanglu-Luoshanlu, Yeshengdongwuyuan-Huinandong sections and Dishuihu station were greater, while the wind directions of Longyanglu-Luoshanlu, Zhoupudong-Xinchang and Yeshengdongwuyuan-Huinandong sections were quite different. It is suggested that the section of Longyanglu-Luoshanlu and Zhoupudong-Xinchang along the rail transit line 16 should be added an automatic weather station, respectively.