FY-4A/GIIRS Temperature Validation in Winter and Application to Cold Wave Monitoring

In order to improve the operational application ability of the Fengyun-4A(FY-4A)new sounding dataset,in this paper,validation of the FY-4A Geosynchronous Interferometric Infrared Sounder(FY-4A/GIIRS)temperature was carried out using the balloon sounding temperature from meteorological sounding stations.More than 350,000 samples were obtained through time–space matching,and the results show that the FY-4A/GIIRS temperature mean bias(MB)is 0.07°C,the mean absolute error(MAE)is 1.80°C,the root-mean-square error(RMSE)is 2.546°C,and the correlation coefficient(RR)is 0.95.The FY-4A/GIIRS temperature error is relatively larger in the upper and lower troposphere,and relatively smaller in the middle troposphere;that is,the temperature at 500 hPa is better than that at 850 hPa.The temporal variation is smaller in the upper and middle troposphere than in the lower troposphere.The reconstruction of missing data of FY-4A/GIIRS temperature in cloudy areas is also carried out and the results are evaluated.The spatial distribution of reconstructed FY-4A/GIIRS temperature and the fifth generation ECMWF reanalysis(ERA5)data is consistent and completely retains the minimum temperature center with high precision of FY-4A/GIIRS.There are more detailed characteristics of intensity and position at the cold center than that of the reanalysis data.Therefore,an operational satellite retrieval temperature product with time–space continuity and high accuracy is formed.The reconstructed FY-4A/GIIRS temperature is used to monitor a strong cold wave event in November 2021.The results show that the product effectively monitors the movement and intensity of cold air activities,and it also has good indication for the phase transition of rain and snow triggered by cold wave.

Global Rainstorm Disaster Risk Monitoring Based on Satellite Remote Sensing

Methods of rainstorm disaster risk monitoring(RDRM)based on retrieved satellite rainfall data are studied.Due to significant regional differences,the global rainstorm disasters are not only affected by geography(such as topography and surface properties),but also by climate events.It is necessary to study rainstorm disaster-causing factors,hazard-formative environments,and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide.According to a global flood disaster dataset for the last 20 years,the top four flood disaster causes(accounting for 96.8%in total)related to rainstorms,from most to least influential,are heavy rain(accounting for 61.6%),brief torrential rain(16.7%),monsoonal rain(9.4%),and tropical cyclone/storm rain(9.1%).A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years.Taking the 7-day accumulated rainfall,3-and 12-h maximum rainfall,24-h rainfall,rainstorm threshold,and others as the main parameters,a rainstorm intensity index is constructed.Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data,population data,and economic data.Finally,a satellite remote sensing RDRM model is developed,incorporating the above three factors(rainstorm intensity index,hazard-formative environment factor,and hazard-affected body factor).The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.