A Validation of the Multivariate and Minimum Residual Method for Cloud Retrieval Using Radiance from Multiple Satellites  

作　　者：XU Dongmei Thomas AULIGNé Xiang-Yu HUANG 

机构地区：[1]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science & Technology [2]National Center for Atmospheric Research,Boulder, Colorado 80307, USA

出　　处：《Advances in Atmospheric Sciences》2015年第3期349-362,共14页大气科学进展（英文版）

基　　金：sponsored by the 973 Program (Grant No. 2013CB430102);the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD);and the Air Force Weather Agency;support from Craig S. SCHWARTZ, Allegrino Americo SAMUEL, and Gael DESCOMBES are greatly appreciated;sponsored by the National Science Foundation;the National Science Foundation

摘　　要：The Multivariate and Minimum Residual （MMR） cloud detection and retrieval algorithm,previously developed and tested on simulated observations and Advanced Infrared Sounder radiance,was explored and validated using various radiances from multiple sensors.For validation,the cloud retrievals were compared to independent cloud products from CloudSat,MODIS （Moderate Resolution Imaging Spectroradiometer）,and GOES （Geostationary Operational Environmental Satellites）.We found good spatial agreement within a single instrument,although the cloud fraction on each pixel was estimated independently.The retrieved cloud properties showed good agreement using radiances from multiple satellites,especially for the vertically integrated cloud mask.The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds.The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations.For observations with fewer channels,the MMR solution was an ＂overly smoothed＂ estimation of the true vertical profile,starting from a uniform clear guess.Additionally,the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator,discriminating between numerical weather prediction （NWP） error and cloud effects.The retrieval scheme was also found to be robust when different radiative transfer models were used.The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.The Multivariate and Minimum Residual （MMR） cloud detection and retrieval algorithm,previously developed and tested on simulated observations and Advanced Infrared Sounder radiance,was explored and validated using various radiances from multiple sensors.For validation,the cloud retrievals were compared to independent cloud products from CloudSat,MODIS （Moderate Resolution Imaging Spectroradiometer）,and GOES （Geostationary Operational Environmental Satellites）.We found good spatial agreement within a single instrument,although the cloud fraction on each pixel was estimated independently.The retrieved cloud properties showed good agreement using radiances from multiple satellites,especially for the vertically integrated cloud mask.The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds.The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations.For observations with fewer channels,the MMR solution was an ＂overly smoothed＂ estimation of the true vertical profile,starting from a uniform clear guess.Additionally,the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator,discriminating between numerical weather prediction （NWP） error and cloud effects.The retrieval scheme was also found to be robust when different radiative transfer models were used.The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.

关 键 词：cloud retrieval RADIANCE cloud fraction observation operator 

分 类 号：P412.27[天文地球—大气科学及气象学]