Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrare...Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrared SST offers high spatial resolution,it is limited by cloud cover.On the other hand,passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall,coastal effects,and high wind speeds.To achieve high-precision,comprehensive,and high-resolution SST data,it is essential to fuse infrared and microwave SST measurements.In this study,data from the Fengyun-3D(FY-3D)medium resolution spectral imager II(MERSI-II)SST and microwave imager(MWRI)SST were fused.Firstly,the accuracy of both MERSIII SST and MWRI SST was verified,and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST.After pretreatment and quality control of MERSI SST and MWRI SST,a Piece-Wise Regression method was employed to correct biases in MWRI SST.Subsequently,SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date.Finally,an optimal interpolation method was applied to fuse the FY-3D MERSI-II SST and MWRI SST.The results demonstrated a significant improvement in spatial coverage compared to MERSI-II SST and MWRI SST.Furthermore,the fusion SST retained true spatial distribution details and exhibited an accuracy of–0.12±0.74℃compared to OSTIA SST.This study has improved the accuracy of FY satellite fusion SST products in China.展开更多
使用Himawari-8静止卫星数据,基于CALIPSO卫星云底高度结合云雾水平均匀性特征提取海雾/低云标签,并使用全卷积神经网络与全连接条件随机场相结合的模型(Fully Convolutional Network and Conditional Random Field,FCN-CRF),提出一种...使用Himawari-8静止卫星数据,基于CALIPSO卫星云底高度结合云雾水平均匀性特征提取海雾/低云标签,并使用全卷积神经网络与全连接条件随机场相结合的模型(Fully Convolutional Network and Conditional Random Field,FCN-CRF),提出一种夜间海雾/低云卫星检测方法。经过建立与训练模型,使用CALIPSO卫星的海雾/低云观测检验FCN-CRF模型和双通道差值法的结果。FCN-CRF模型表现良好,其检出率(probability of detection,POD)为0.611,虚警率(false alarm ratio,FAR)为0.174,临界成功指数(critical success index,CSI)为0.541,Hanssen-Kuiper技能分数(Hanssen-Kuiper Skill Score,KSS)为0.436,Heidke技能分数(Heidke Skill Score,HSS)为0.577,整体优于双通道差值法。展开更多
利用2007—2016年国际卫星云气候计划(International Satellite Cloud Climatology Project,ISCCP)、云和地球辐射能量系统(Clouds and the Earth s Radiant Energy System,CERES)和中分辨率成像光谱仪(Moderate Resolution Imaging Spe...利用2007—2016年国际卫星云气候计划(International Satellite Cloud Climatology Project,ISCCP)、云和地球辐射能量系统(Clouds and the Earth s Radiant Energy System,CERES)和中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)卫星反演云产品,对比分析了不同数据反演的中国地区云系结构的宏微观特征,并采用复合评价指标定量评估了不同数据之间时间和空间上的一致性。结果表明:三套卫星数据都能较好地反演出中国地区总云量呈南高北低、东高西低、夏高冬低的分布特征,但通过比较时间技巧(Temporal Skill,S_(T))及空间技巧(Spatial Skill,S_(S))复合评价指标及其各项分量发现,与MODIS相比,CERES与ISCCP数据反演的总云量时间序列演变特征明显更为一致,且其评分均有南方优于北方,夏季优于冬季的特征;进一步分析不同高度云量的S_(T)评分发现,CERES和ISCCP两套数据在南方地区的总云量差异主要来自于低云量的绝对偏差,而北方地区的偏差则同时存在于低云和中云;对比分析MODIS和CERES反演的云滴有效半径发现,高云对应的冰相云一致性较高,而中低云相对应的液相云的偏差则有夏季高于冬季的规律。针对夏季液相和冰相云滴粒径及概率密度分析则表明,相比CERES数据,MODIS对夏季液水和冰水粒子的有效半径在不同地区均有不同程度的高估,液(冰)水谱宽则更宽(窄)。展开更多
利用2007-2009年Cloud Sat卫星云廓线雷达(nadir-pointing cloud profiling radar,CPR)资料,气象、电离层和气候卫星联合观测系统(Global Constellation Observing System for Meteorology,Ionosphere,and Climate,COSMIC)掩星资料,分...利用2007-2009年Cloud Sat卫星云廓线雷达(nadir-pointing cloud profiling radar,CPR)资料,气象、电离层和气候卫星联合观测系统(Global Constellation Observing System for Meteorology,Ionosphere,and Climate,COSMIC)掩星资料,分析了不同类型云内COSMIC掩星资料与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)和美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)分析场之间的折射率偏差特征。COSMIC与ECMWF和NCEP之间的折射率偏差分别用Nbias^(ECMWF)和Nbias^(NCEP)表示。研究发现,Nbias^(ECMWF)在积云、层积云、高积云和高层云中的最大值分别为1.2%、0.2%、0.5%和0.2%,而Nbias^(NCEP)则分别为1.8%、0.5%、0.5%和0.4%。在层积云的对流层下层Nbias^(NCEP)存在较大的负值,而Nbias^(ECMWF)为正值。折射率的正偏差随着液态水含量增加而增加。从全球分布来看,赤道辐合带云量丰富,Nbias^(NCEP)和Nbias^(ECMWF)也存在明显的正偏差,它在空间上和水汽的正偏差以及温度的负偏差高度相关。展开更多
文摘Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrared SST offers high spatial resolution,it is limited by cloud cover.On the other hand,passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall,coastal effects,and high wind speeds.To achieve high-precision,comprehensive,and high-resolution SST data,it is essential to fuse infrared and microwave SST measurements.In this study,data from the Fengyun-3D(FY-3D)medium resolution spectral imager II(MERSI-II)SST and microwave imager(MWRI)SST were fused.Firstly,the accuracy of both MERSIII SST and MWRI SST was verified,and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST.After pretreatment and quality control of MERSI SST and MWRI SST,a Piece-Wise Regression method was employed to correct biases in MWRI SST.Subsequently,SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date.Finally,an optimal interpolation method was applied to fuse the FY-3D MERSI-II SST and MWRI SST.The results demonstrated a significant improvement in spatial coverage compared to MERSI-II SST and MWRI SST.Furthermore,the fusion SST retained true spatial distribution details and exhibited an accuracy of–0.12±0.74℃compared to OSTIA SST.This study has improved the accuracy of FY satellite fusion SST products in China.
文摘利用2007—2016年国际卫星云气候计划(International Satellite Cloud Climatology Project,ISCCP)、云和地球辐射能量系统(Clouds and the Earth s Radiant Energy System,CERES)和中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)卫星反演云产品,对比分析了不同数据反演的中国地区云系结构的宏微观特征,并采用复合评价指标定量评估了不同数据之间时间和空间上的一致性。结果表明:三套卫星数据都能较好地反演出中国地区总云量呈南高北低、东高西低、夏高冬低的分布特征,但通过比较时间技巧(Temporal Skill,S_(T))及空间技巧(Spatial Skill,S_(S))复合评价指标及其各项分量发现,与MODIS相比,CERES与ISCCP数据反演的总云量时间序列演变特征明显更为一致,且其评分均有南方优于北方,夏季优于冬季的特征;进一步分析不同高度云量的S_(T)评分发现,CERES和ISCCP两套数据在南方地区的总云量差异主要来自于低云量的绝对偏差,而北方地区的偏差则同时存在于低云和中云;对比分析MODIS和CERES反演的云滴有效半径发现,高云对应的冰相云一致性较高,而中低云相对应的液相云的偏差则有夏季高于冬季的规律。针对夏季液相和冰相云滴粒径及概率密度分析则表明,相比CERES数据,MODIS对夏季液水和冰水粒子的有效半径在不同地区均有不同程度的高估,液(冰)水谱宽则更宽(窄)。
文摘利用2007-2009年Cloud Sat卫星云廓线雷达(nadir-pointing cloud profiling radar,CPR)资料,气象、电离层和气候卫星联合观测系统(Global Constellation Observing System for Meteorology,Ionosphere,and Climate,COSMIC)掩星资料,分析了不同类型云内COSMIC掩星资料与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)和美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)分析场之间的折射率偏差特征。COSMIC与ECMWF和NCEP之间的折射率偏差分别用Nbias^(ECMWF)和Nbias^(NCEP)表示。研究发现,Nbias^(ECMWF)在积云、层积云、高积云和高层云中的最大值分别为1.2%、0.2%、0.5%和0.2%,而Nbias^(NCEP)则分别为1.8%、0.5%、0.5%和0.4%。在层积云的对流层下层Nbias^(NCEP)存在较大的负值,而Nbias^(ECMWF)为正值。折射率的正偏差随着液态水含量增加而增加。从全球分布来看,赤道辐合带云量丰富,Nbias^(NCEP)和Nbias^(ECMWF)也存在明显的正偏差,它在空间上和水汽的正偏差以及温度的负偏差高度相关。
