Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferome...Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferometric infrared sounder(GIIRS)of FengYun-4 A(FY-4 A)observation and simulated brightness temperature based on background field,the brightness temperature bias correction of GIIRS channel is carried out based on random forest(RF)and extreme gradient boosting(XGBoost)machine learning.Based on the case data of Typhoon"Haishen",the correction effect of machine learning method is compared with Harris and Kelly’s"off-line"method,and the importance of different predictors to the bias correction is further discussed.The experimental results show that the systematic bias is effectively corrected,and the following conclusions are obtained:the correction effect is improved by adding geographic information(longitude and latitude)into the predictors;under the given combination of predictors,the correction effect of XGBoost is the best,followed by random forest,and finally offline method,but the three methods can correct the bias effectively;compared with long wave data of FY-4 A/GIIRS,machine learning may be more feasible for medium wave data bias correction.展开更多
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 ca...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.展开更多
Spatial characteristic is an important indicator of remote sensor performance,and space-borne infrared hyperspectral sounder is the frontier of atmospheric vertical sounding technology.In this paper,the formation mech...Spatial characteristic is an important indicator of remote sensor performance,and space-borne infrared hyperspectral sounder is the frontier of atmospheric vertical sounding technology.In this paper,the formation mechanism of the vertical spatial characteristics involved in the space-borne infrared hyperspectral sounding data are analyzed in detail,which shows that the vertical spatial characteristics of sounding data depends not only on the spectral channels and their waveband coverage,but also the specific atmospheric parameter and its specific variation interested.The indicators of vertical spatial characteristics are defined and their mathematical models are established based on the mechanism analyses.These models are applied to the vertical spatial characteristic evaluation of atmospheric temperature sounding for FY-4A GIIRS,which is the first space-borne infrared hyperspectral atmospheric sounder in geostationary orbit.It is concluded that FY-4A GIIRS can sound the vertical temperature distribution in whole troposphere and lower stratosphere with height<35 km.This study can provide basic information to support the improvement of infrared hyperspectral sounder and the trace of vertical spatial characteristics of atmospheric inversion products.展开更多
基金Supported by the National Natural Science Foundation of China(41805080)Special Project for Innovation and Development of Anhui Meteorological Bureau(CXB202101)Central Asian Fund for Atmospheric Science Research(CAAS202003)。
文摘Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferometric infrared sounder(GIIRS)of FengYun-4 A(FY-4 A)observation and simulated brightness temperature based on background field,the brightness temperature bias correction of GIIRS channel is carried out based on random forest(RF)and extreme gradient boosting(XGBoost)machine learning.Based on the case data of Typhoon"Haishen",the correction effect of machine learning method is compared with Harris and Kelly’s"off-line"method,and the importance of different predictors to the bias correction is further discussed.The experimental results show that the systematic bias is effectively corrected,and the following conclusions are obtained:the correction effect is improved by adding geographic information(longitude and latitude)into the predictors;under the given combination of predictors,the correction effect of XGBoost is the best,followed by random forest,and finally offline method,but the three methods can correct the bias effectively;compared with long wave data of FY-4 A/GIIRS,machine learning may be more feasible for medium wave data bias correction.
基金Supported by the National Natural Science Foundation of China(42175014)National Key Research and Development Program of China(2021YFB3900400).
文摘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.
文摘Spatial characteristic is an important indicator of remote sensor performance,and space-borne infrared hyperspectral sounder is the frontier of atmospheric vertical sounding technology.In this paper,the formation mechanism of the vertical spatial characteristics involved in the space-borne infrared hyperspectral sounding data are analyzed in detail,which shows that the vertical spatial characteristics of sounding data depends not only on the spectral channels and their waveband coverage,but also the specific atmospheric parameter and its specific variation interested.The indicators of vertical spatial characteristics are defined and their mathematical models are established based on the mechanism analyses.These models are applied to the vertical spatial characteristic evaluation of atmospheric temperature sounding for FY-4A GIIRS,which is the first space-borne infrared hyperspectral atmospheric sounder in geostationary orbit.It is concluded that FY-4A GIIRS can sound the vertical temperature distribution in whole troposphere and lower stratosphere with height<35 km.This study can provide basic information to support the improvement of infrared hyperspectral sounder and the trace of vertical spatial characteristics of atmospheric inversion products.
