Research On Land Data Assimilation Improvement Of Geostationary Satellite Infrared Imager Based On Automatic Station Data

The data assimilation of the land near surface channel of the geostationary satellite infrared imager has always been a difficult area for data assimilation research.Due to the complex spatial and temporal variation of ground temperature,it is difficult for the model to accurately reproduce the spatial and temporal variation process of ground temperature,resulting in the insufficient assimilation application of the land data.In this paper,we take the Japanese Himawari-8 geostationary satellite imager data AHI(Advanced Himawari Imager)as the research object and based on clarifying the error characteristics of the ground temperature of the reanalysis data in the Chinese region,by introducing the automatic station observation data with the high spatial and temporal resolution with effective quality control in China,the ground temperature of the background field is replaced with the automatic station observation data under the condition of consistent atmospheric temperature and humidity profiles.The paper improves the accuracy of the ground temperature by replacing it with the automatic station observations,improves the simulation effect of the radiative transfer model on the AHI near-surface infrared channel data,and thus improves the assimilation effect.Finally,the paper analyzes the impact of AHI terrestrial data assimilation on the precipitation forecast level in the Haihe River basin using the improved AHI assimilation method and discusses the improved effect and practical value of high-resolution AHI data assimilation on basin-scale precipitation.The following conclusions are obtained:(1)Both ERA5 reanalysis data and NCEP GFS data can reproduce the daily variation of ground 2m temperature and ground temperature in China.However,the difference between the ground temperature observed by the automatic station and the two reanalysis data showed a significant daily variation periodicity,and the phase of this periodicity shifted significantly with the longitude band.Further analysis shows that the periodic characteristic of the error is mainly due to the systematic phase difference in the simulation of the daily variation cycle of ground temperature in China by the reanalysis data,and there is also an obvious longitude dependence of the phase difference,which proves that the phase difference is the main cause of the ground temperature error in the reanalysis data.(2)The simulation error of AHI brightness temperature can be effectively reduced by introducing station observed ground temperature,and the mean value of O-B(Observation-Background)and the standard deviation of O-B are reduced,and the improvement is more significant at the time when the ground temperature error of reanalysis data is large,which indicates that the introduction of ground temperature observations can reduce the simulation error of reanalysis data.Therefore,it is expected to improve the assimilation effect of the AHI near-surface channel land data by directly replacing the ground temperature in the background field with the automatic station observations in the assimilation process.(3)The improved AHI data assimilation method can further improve the assimilation effect of the near-surface channel in the AHI land area and enhance the precipitation forecasting level in the Haihe River basin.The analysis of the assimilation results shows that the improved AHI terrestrial data assimilation mainly improves the atmospheric temperature in the upstream high terrain area,which in turn affects the weather system development and ultimately improves the precipitation forecast in the basin.The quantitative scoring results also demonstrate that AHI assimilation can significantly improve the ETS(Equitable Threat Scores)scores for each threshold precipitation forecast from 1-15 mm.20 days of cyclic assimilation experiments demonstrate that the assimilation of AHI terrestrial observations can steadily improve the basin-scale short-term precipitation forecasts in the Haihe River basin.The results can be used as a reference for the assimilation of geostationary satellite infrared imager data in China.