The Microwave Radiation Imager(MWRI),boarded on the FY-3 series satellites:FY-3B,FY-3C,and FY-3D,is the first satellite-based microwave radiometer in China,commencing passive microwave brightness temperature data acqu...The Microwave Radiation Imager(MWRI),boarded on the FY-3 series satellites:FY-3B,FY-3C,and FY-3D,is the first satellite-based microwave radiometer in China,commencing passive microwave brightness temperature data acquisition since 2010.The Advanced Microwave Scanning Radiometer 2(AMSR2) boarded on the Global Change Observation Mission 1st-Water(GCOM-W1),has been operational since 2012.Despite the FY-3 series satellites are equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2,disparities in observation times and satellite platforms result in inconsistencies in the data obtained by different satellites,which further impacting the consistency of retrieved geophysical parameters.To improve the consistency of brightness temperatures from FY-3B,FY-3C,FY-3D/MWRI,and GCOM-W1/AMSR2,cross-calibrations were conducted among brightness temperatures at ten-channel from above four platforms.The consistency of derived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed.The results show that the correlation coefficients of brightness temperatures at all channels between sensors exceed0.98.After cross-calibration,the RMSEs and biases of brightness temperatures at all frequencies and snow depth in China derived from them reduce to varying degrees.The consistencies in both brightness temperatures and snow depth of FY-3B/MWRI,FY-3D/MWRI,and AMSR2 are higher than those of FY-3C and others.These findings advocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI,FY-3D/MWRI,and AMSR2,which share similar satellite overpass time,to derived a long-term snow depth dataset.展开更多
在WRFDA-3DVar(Weather Research and Forecasting model’s 3-dimensional variational data assimilation)的框架下,添加了新的探测器AMSR2(Advanced Microwave Scanning Radiometer 2)微波辐射率资料的同化模块,实现了AMSR2辐射率资...在WRFDA-3DVar(Weather Research and Forecasting model’s 3-dimensional variational data assimilation)的框架下,添加了新的探测器AMSR2(Advanced Microwave Scanning Radiometer 2)微波辐射率资料的同化模块,实现了AMSR2辐射率资料在中小尺度同化系统中的有效使用。台风"山神"(Son-Tinh)直接同化AMSR2资料的个例试验结果表明,AMSR2资料可以很好的探测出台风的形态,并且与没有同化该资料的控制试验相比,同化AMSR2辐射率资料可以有效提高模式分析场的质量,进一步提高了台风中心气压,最大风速和台风路径的预报。展开更多
遥感反演土壤水分(SM)产品越来越多地应用于农业、气象、水文等研究,而微波土壤水分数据产品的区域适用性分析是其合理使用的必要前提。使用MERRA-2(Modern Era Retrospective-analysis for Research and Applications,Version 2)模拟...遥感反演土壤水分(SM)产品越来越多地应用于农业、气象、水文等研究,而微波土壤水分数据产品的区域适用性分析是其合理使用的必要前提。使用MERRA-2(Modern Era Retrospective-analysis for Research and Applications,Version 2)模拟土壤水分为参考数据,运用传统统计方法(原始数据相关性、距平相关性、偏差以及无偏均方根差)和TC(Triple-Collocation)不确定性误差模型分析的方法,对亚洲区域2012年7月～2016年7月两种被动微波土壤水分SMOS-L3-SM(Soil Moisture and Ocean Salinity,L3)和AMSR2-LPRM-SM(The Advanced Microwave Scanning Radiometer 2,Land Parameter Retrieval Model Product)进行对比评估。结果表明:①空间上SMOS-L3较AMSR2-LPRM数据与参考数据MERRA-2土壤水分的相关性较好,表现为SMOS-L3-SM具有较好的空间连续性,且在亚洲大多数地区有较小的无偏均方根差;②湿季条件下遥感土壤水分与参考值的相关性比干季条件下的相关性更好,且干季出现高纬地区(约>55°)缺失值较多的情况;③两遥感土壤水分的TC误差呈现相似的分布,区域TC平均误差两者均为0.076 m^3/m^3。总之,SMOS-L3-SM和AMSR2-LPRM-SM在空间相关性及TC误差评价方面都具有合理性,为遥感土壤水分在农业、气象、水文等方面的应用提供参考。展开更多
本项工作在黑河流域中上游区域内,利用地下4cm深度的地面实测土壤水分数据验证了2012年7月至2014年12月期间AMSR2的两种算法产品——日本宇航局标准算法土壤水分产品(JAXA产品)和阿姆斯特丹自由大学联合美国宇航局开发的陆表参数反演模...本项工作在黑河流域中上游区域内,利用地下4cm深度的地面实测土壤水分数据验证了2012年7月至2014年12月期间AMSR2的两种算法产品——日本宇航局标准算法土壤水分产品(JAXA产品)和阿姆斯特丹自由大学联合美国宇航局开发的陆表参数反演模型算法土壤水分产品(LPRM产品)。验证结果显示:与地面实测数据相比,所有验证像元上两种土壤水分产品的均方根误差RMSE(Root Mean Square Error)普遍超过了0.1m^3/m^3。JAXA产品动态变化范围较小,升轨产品的总体精度略高于降轨,相比地面实测数据均存在明显的低估,在冻季与实测数据比较接近。LPRM产品动态范围较大,降轨产品在冻季不可用,在未冻季升轨产品精度高于降轨,相比地面实测数据有高估的倾向。同时,还进一步讨论并分析了两种算法对土壤温度和植被的不同处理方式对土壤水产品精度的可能影响,指出了算法可能的改进方向。展开更多
基金supported by the National Natural Science Foun-dation of China(42125604,42171143)Innovative Development Project of China Meteorological Administration(CXFZ 2022J039).
文摘The Microwave Radiation Imager(MWRI),boarded on the FY-3 series satellites:FY-3B,FY-3C,and FY-3D,is the first satellite-based microwave radiometer in China,commencing passive microwave brightness temperature data acquisition since 2010.The Advanced Microwave Scanning Radiometer 2(AMSR2) boarded on the Global Change Observation Mission 1st-Water(GCOM-W1),has been operational since 2012.Despite the FY-3 series satellites are equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2,disparities in observation times and satellite platforms result in inconsistencies in the data obtained by different satellites,which further impacting the consistency of retrieved geophysical parameters.To improve the consistency of brightness temperatures from FY-3B,FY-3C,FY-3D/MWRI,and GCOM-W1/AMSR2,cross-calibrations were conducted among brightness temperatures at ten-channel from above four platforms.The consistency of derived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed.The results show that the correlation coefficients of brightness temperatures at all channels between sensors exceed0.98.After cross-calibration,the RMSEs and biases of brightness temperatures at all frequencies and snow depth in China derived from them reduce to varying degrees.The consistencies in both brightness temperatures and snow depth of FY-3B/MWRI,FY-3D/MWRI,and AMSR2 are higher than those of FY-3C and others.These findings advocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI,FY-3D/MWRI,and AMSR2,which share similar satellite overpass time,to derived a long-term snow depth dataset.
文摘在WRFDA-3DVar(Weather Research and Forecasting model’s 3-dimensional variational data assimilation)的框架下,添加了新的探测器AMSR2(Advanced Microwave Scanning Radiometer 2)微波辐射率资料的同化模块,实现了AMSR2辐射率资料在中小尺度同化系统中的有效使用。台风"山神"(Son-Tinh)直接同化AMSR2资料的个例试验结果表明,AMSR2资料可以很好的探测出台风的形态,并且与没有同化该资料的控制试验相比,同化AMSR2辐射率资料可以有效提高模式分析场的质量,进一步提高了台风中心气压,最大风速和台风路径的预报。
文摘遥感反演土壤水分(SM)产品越来越多地应用于农业、气象、水文等研究,而微波土壤水分数据产品的区域适用性分析是其合理使用的必要前提。使用MERRA-2(Modern Era Retrospective-analysis for Research and Applications,Version 2)模拟土壤水分为参考数据,运用传统统计方法(原始数据相关性、距平相关性、偏差以及无偏均方根差)和TC(Triple-Collocation)不确定性误差模型分析的方法,对亚洲区域2012年7月～2016年7月两种被动微波土壤水分SMOS-L3-SM(Soil Moisture and Ocean Salinity,L3)和AMSR2-LPRM-SM(The Advanced Microwave Scanning Radiometer 2,Land Parameter Retrieval Model Product)进行对比评估。结果表明:①空间上SMOS-L3较AMSR2-LPRM数据与参考数据MERRA-2土壤水分的相关性较好,表现为SMOS-L3-SM具有较好的空间连续性,且在亚洲大多数地区有较小的无偏均方根差;②湿季条件下遥感土壤水分与参考值的相关性比干季条件下的相关性更好,且干季出现高纬地区(约>55°)缺失值较多的情况;③两遥感土壤水分的TC误差呈现相似的分布,区域TC平均误差两者均为0.076 m^3/m^3。总之,SMOS-L3-SM和AMSR2-LPRM-SM在空间相关性及TC误差评价方面都具有合理性,为遥感土壤水分在农业、气象、水文等方面的应用提供参考。
文摘本项工作在黑河流域中上游区域内,利用地下4cm深度的地面实测土壤水分数据验证了2012年7月至2014年12月期间AMSR2的两种算法产品——日本宇航局标准算法土壤水分产品(JAXA产品)和阿姆斯特丹自由大学联合美国宇航局开发的陆表参数反演模型算法土壤水分产品(LPRM产品)。验证结果显示:与地面实测数据相比,所有验证像元上两种土壤水分产品的均方根误差RMSE(Root Mean Square Error)普遍超过了0.1m^3/m^3。JAXA产品动态变化范围较小,升轨产品的总体精度略高于降轨,相比地面实测数据均存在明显的低估,在冻季与实测数据比较接近。LPRM产品动态范围较大,降轨产品在冻季不可用,在未冻季升轨产品精度高于降轨,相比地面实测数据有高估的倾向。同时,还进一步讨论并分析了两种算法对土壤温度和植被的不同处理方式对土壤水产品精度的可能影响,指出了算法可能的改进方向。
