In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In...In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In this paper,synchronous measurements of an airborne millimeter-wave radar and a hot-wire probe in stratus cloud are used to compare the LWC retrievals of the oceanic and continental particle parameter scheme with diameter less than 50μm and the particle parameter scheme with diameter less than 500μm and 1500μm(scheme 1,scheme 2,scheme 3,and scheme4,respectively).The results show that the particle parameter scheme needs to be selected according to the reflectivity factor when using the physical iterative method to retrieve the LWC and LWP.When the reflectivity factor is less than-30 d BZ,the retrieval error of scheme 1 is the minimum.When the reflectivity factor is greater than-30 d BZ,the retrieval error of scheme 4 is the minimum.Based on the reflectance factor value,the LWP retrievals of scheme 4 are closer to the measurements,the average relative bias is 5.2%,and the minimum relative bias is 4.4%.Compared with other schemes,scheme 4 seems to be more useful for the LWC and LWP retrieval of stratus cloud in China.展开更多
Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave(GW)analysis.To accurately and efficiently derive the t...Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave(GW)analysis.To accurately and efficiently derive the three-dimensional structure of the stratospheric GWs from the single-field-of-view(SFOV)Atmospheric Infra Red Sounder(AIRS)observations,this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach(ANN).The simulation experiments show that the retrieval bias is less than 0.5 K,and the root mean square error(RMSE)ranges from 1.8 to 4 K.Moreover,the retrieval results from 20 granules of the AIRS observations with the trained neural network(AIRS_SFOV)and the corresponding operational AIRS products(AIRS_L2)as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies(CIMSS)(AIRS_DR)are compared respectively with ECMWF T799 data.The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR.Furthermore,the analysis of the typical GW events induced by the mountain Andes and the typhoon"Soulik"using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.展开更多
基金National Natural Science Foundation of China(41575031,41175089)China Postdoctoral Science Foundation(2015M580124)Key Laboratory of Geo-Information Engineering(S18701)
文摘In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In this paper,synchronous measurements of an airborne millimeter-wave radar and a hot-wire probe in stratus cloud are used to compare the LWC retrievals of the oceanic and continental particle parameter scheme with diameter less than 50μm and the particle parameter scheme with diameter less than 500μm and 1500μm(scheme 1,scheme 2,scheme 3,and scheme4,respectively).The results show that the particle parameter scheme needs to be selected according to the reflectivity factor when using the physical iterative method to retrieve the LWC and LWP.When the reflectivity factor is less than-30 d BZ,the retrieval error of scheme 1 is the minimum.When the reflectivity factor is greater than-30 d BZ,the retrieval error of scheme 4 is the minimum.Based on the reflectance factor value,the LWP retrievals of scheme 4 are closer to the measurements,the average relative bias is 5.2%,and the minimum relative bias is 4.4%.Compared with other schemes,scheme 4 seems to be more useful for the LWC and LWP retrieval of stratus cloud in China.
基金National Natural Science Foundation of China(41575031,41375024)Postdoctoral Science Foundation of China(2015M580124)Meteorology Research Special Funds for Public Welfare Projects(GYHY201406011)。
文摘Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave(GW)analysis.To accurately and efficiently derive the three-dimensional structure of the stratospheric GWs from the single-field-of-view(SFOV)Atmospheric Infra Red Sounder(AIRS)observations,this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach(ANN).The simulation experiments show that the retrieval bias is less than 0.5 K,and the root mean square error(RMSE)ranges from 1.8 to 4 K.Moreover,the retrieval results from 20 granules of the AIRS observations with the trained neural network(AIRS_SFOV)and the corresponding operational AIRS products(AIRS_L2)as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies(CIMSS)(AIRS_DR)are compared respectively with ECMWF T799 data.The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR.Furthermore,the analysis of the typical GW events induced by the mountain Andes and the typhoon"Soulik"using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.
