{"title":"利用ATMS被动微波谱仪反演NPOESS降水","authors":"C. Surussavadee, D. Staelin","doi":"10.1109/LGRS.2009.2038614","DOIUrl":null,"url":null,"abstract":"This paper evaluates the ability of the United States National Polar Orbiting Environmental Sensor System (NPOESS) Advanced Technology Microwave Sounder (ATMS) to retrieve surface precipitation rates (mm/h); water path estimates for rain, snow, and graupel (mm); and peak vertical wind (convective strength, m/s). Simulated retrieval accuracies for ATMS were compared to those for its predecessor, AMSU. These retrieval algorithms employ neural networks trained using atmospheric parameters and their corresponding brightness temperatures predicted by a global ground-truth model, NCEP/MM5/TBSCAT/F(lambda), for 106 global storms. ATMS performs better than AMSU for all retrieved parameters except for snow and cloud ice, where they perform comparably. Image sharpening amplifies noise and so its benefits are restricted primarily to relatively rare isolated storms.","PeriodicalId":237798,"journal":{"name":"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"NPOESS Precipitation Retrievals using the ATMS Passive Microwave Spectrometer\",\"authors\":\"C. Surussavadee, D. Staelin\",\"doi\":\"10.1109/LGRS.2009.2038614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper evaluates the ability of the United States National Polar Orbiting Environmental Sensor System (NPOESS) Advanced Technology Microwave Sounder (ATMS) to retrieve surface precipitation rates (mm/h); water path estimates for rain, snow, and graupel (mm); and peak vertical wind (convective strength, m/s). Simulated retrieval accuracies for ATMS were compared to those for its predecessor, AMSU. These retrieval algorithms employ neural networks trained using atmospheric parameters and their corresponding brightness temperatures predicted by a global ground-truth model, NCEP/MM5/TBSCAT/F(lambda), for 106 global storms. ATMS performs better than AMSU for all retrieved parameters except for snow and cloud ice, where they perform comparably. Image sharpening amplifies noise and so its benefits are restricted primarily to relatively rare isolated storms.\",\"PeriodicalId\":237798,\"journal\":{\"name\":\"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LGRS.2009.2038614\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LGRS.2009.2038614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
NPOESS Precipitation Retrievals using the ATMS Passive Microwave Spectrometer
This paper evaluates the ability of the United States National Polar Orbiting Environmental Sensor System (NPOESS) Advanced Technology Microwave Sounder (ATMS) to retrieve surface precipitation rates (mm/h); water path estimates for rain, snow, and graupel (mm); and peak vertical wind (convective strength, m/s). Simulated retrieval accuracies for ATMS were compared to those for its predecessor, AMSU. These retrieval algorithms employ neural networks trained using atmospheric parameters and their corresponding brightness temperatures predicted by a global ground-truth model, NCEP/MM5/TBSCAT/F(lambda), for 106 global storms. ATMS performs better than AMSU for all retrieved parameters except for snow and cloud ice, where they perform comparably. Image sharpening amplifies noise and so its benefits are restricted primarily to relatively rare isolated storms.