祁连山中段夏季降雨特征研究可为地形云人工增雨提供天气背景依据支撑。基于2015—2017年地面观测资料和FY-2G卫星红外黑体亮度温度(Temperature of Black Body, TBB)逐时资料,分析祁连山中段夏季降雨与对流特征及降雨日变化与局地环流...祁连山中段夏季降雨特征研究可为地形云人工增雨提供天气背景依据支撑。基于2015—2017年地面观测资料和FY-2G卫星红外黑体亮度温度(Temperature of Black Body, TBB)逐时资料,分析祁连山中段夏季降雨与对流特征及降雨日变化与局地环流的关系。结果表明:祁连山中段海拔低于3.5 km的气象站降雨量随海拔呈线性递增变化,东部(99.2°E以东)降雨量随海拔的变化较西部剧烈。位于山谷的野牛沟和祁连站山谷风环流和降雨日变化较青海湖畔的刚察站明显,两站降雨最大值和次峰值分别在傍晚和清晨,分别对应该地区两类主要地形云(积雨云和层积云)高频时段,平均雨强分别为2.0~2.3、1.0~1.3 mm·h^(-1)。与河西走廊张掖站相比,在雨强小于1.5 mm·h^(-1)和大于等于1.5 mm·h^(-1)条件下,祁连、野牛沟和刚察站的TBB概率分布峰值从-22~-12℃转为-32~-22℃;在使用TBB<-32℃的降雨云识别阈值时,祁连山地区降雨云的覆盖率低于河西走廊地区;TBB<-22℃阈值更适宜祁连山地区弱对流降雨云的识别。研究区内深对流与浅对流的高值区分别呈现南北向、西北—东南向分布,基于TBB资料的浅对流频率日变化等可反映该地区降雨日变化的部分特征。展开更多
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
Sea surface temperature(SST)is a crucial physical parameter in meteorology and oceanography.This study demonstrates that the influence of earth incidence angle(EIA)on the SST retrieved from the microwave radiation ima...Sea surface temperature(SST)is a crucial physical parameter in meteorology and oceanography.This study demonstrates that the influence of earth incidence angle(EIA)on the SST retrieved from the microwave radiation imager(MWRI)onboard FengYun-3(FY-3)meteorological satellites should not be ignored.Compared with algorithms that do not consider the influence of EIA in the regression,those that integrate the EIA into the regression can enhance the accuracy of SST retrievals.Subsequently,based on the recalibrated Level 1B data from the FY-3/MWRI,a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression.The reprocessed SST data,including FY-3B/MWRI SST during 2010-2019,FY-3C/MWRI SST during 2013-2019,and FY-3D/MWRI SST during 2018-2020,were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis(OSTIA).The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset.Compared with the Copernicus Climate Change Service V2.0 SST,the absolute deviation of the reprocessed SST,with a quality flag of 50,was less than 1.5℃.The root mean square errors of the FY-3/MWRI orbital,daily,and monthly SSTs,with a quality flag of 50,were approximately 0.82℃,0.69℃,and 0.37℃,respectively.The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current.Overall,this reprocessed SST product is recommended for El Niño and La Niña events monitoring.展开更多
The onset,evolution,and propagation processes of convective cells can be reflected by the organizational morphology of mesoscale convective systems(MCSs),which are key factors in determining the potential for heavy pr...The onset,evolution,and propagation processes of convective cells can be reflected by the organizational morphology of mesoscale convective systems(MCSs),which are key factors in determining the potential for heavy precipitation.This paper proposed a method for objectively classifying and segmenting MCSs using geosynchronous satellite observations.Validation of the product relative to the classification in radar composite reflectivity imagery indicates that the algorithm offers skill for discriminating between convective and stratiform areas and matched 65%of convective area identifications in radar imagery with a false alarm rate of 39%and an accuracy of 94%.A quantitative evaluation of the similarity between the structures of 50 MCSs randomly obtained from satellite and radar observations shows that the similarity was as high as 60%.For further testing,the organizational modes of the MCS that caused the heavy precipitation in Northwest China on August 21,2016(hereinafter known as the“0821”rainstorm)were identified.It was found that the MCS,accompanied by the“0821”rainstorm,successively exhibited modes of the isolated cell,squall line with parallel stratiform(PS)rain,and non-linear system during its life cycle.Among them,the PS mode might have played a key role in causing this flooding.These findings are in line with previous studies.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
文摘祁连山中段夏季降雨特征研究可为地形云人工增雨提供天气背景依据支撑。基于2015—2017年地面观测资料和FY-2G卫星红外黑体亮度温度(Temperature of Black Body, TBB)逐时资料,分析祁连山中段夏季降雨与对流特征及降雨日变化与局地环流的关系。结果表明:祁连山中段海拔低于3.5 km的气象站降雨量随海拔呈线性递增变化,东部(99.2°E以东)降雨量随海拔的变化较西部剧烈。位于山谷的野牛沟和祁连站山谷风环流和降雨日变化较青海湖畔的刚察站明显,两站降雨最大值和次峰值分别在傍晚和清晨,分别对应该地区两类主要地形云(积雨云和层积云)高频时段,平均雨强分别为2.0~2.3、1.0~1.3 mm·h^(-1)。与河西走廊张掖站相比,在雨强小于1.5 mm·h^(-1)和大于等于1.5 mm·h^(-1)条件下,祁连、野牛沟和刚察站的TBB概率分布峰值从-22~-12℃转为-32~-22℃;在使用TBB<-32℃的降雨云识别阈值时,祁连山地区降雨云的覆盖率低于河西走廊地区;TBB<-22℃阈值更适宜祁连山地区弱对流降雨云的识别。研究区内深对流与浅对流的高值区分别呈现南北向、西北—东南向分布,基于TBB资料的浅对流频率日变化等可反映该地区降雨日变化的部分特征。
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.
基金National Natural Science Foundation of China(42330602)Youth Innovation Team for“FengYun Satellite Remote Sensing Product Verification”(CMA2023QN12)。
文摘Sea surface temperature(SST)is a crucial physical parameter in meteorology and oceanography.This study demonstrates that the influence of earth incidence angle(EIA)on the SST retrieved from the microwave radiation imager(MWRI)onboard FengYun-3(FY-3)meteorological satellites should not be ignored.Compared with algorithms that do not consider the influence of EIA in the regression,those that integrate the EIA into the regression can enhance the accuracy of SST retrievals.Subsequently,based on the recalibrated Level 1B data from the FY-3/MWRI,a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression.The reprocessed SST data,including FY-3B/MWRI SST during 2010-2019,FY-3C/MWRI SST during 2013-2019,and FY-3D/MWRI SST during 2018-2020,were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis(OSTIA).The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset.Compared with the Copernicus Climate Change Service V2.0 SST,the absolute deviation of the reprocessed SST,with a quality flag of 50,was less than 1.5℃.The root mean square errors of the FY-3/MWRI orbital,daily,and monthly SSTs,with a quality flag of 50,were approximately 0.82℃,0.69℃,and 0.37℃,respectively.The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current.Overall,this reprocessed SST product is recommended for El Niño and La Niña events monitoring.
基金National Natural Science Foundation of China(41965001)。
文摘The onset,evolution,and propagation processes of convective cells can be reflected by the organizational morphology of mesoscale convective systems(MCSs),which are key factors in determining the potential for heavy precipitation.This paper proposed a method for objectively classifying and segmenting MCSs using geosynchronous satellite observations.Validation of the product relative to the classification in radar composite reflectivity imagery indicates that the algorithm offers skill for discriminating between convective and stratiform areas and matched 65%of convective area identifications in radar imagery with a false alarm rate of 39%and an accuracy of 94%.A quantitative evaluation of the similarity between the structures of 50 MCSs randomly obtained from satellite and radar observations shows that the similarity was as high as 60%.For further testing,the organizational modes of the MCS that caused the heavy precipitation in Northwest China on August 21,2016(hereinafter known as the“0821”rainstorm)were identified.It was found that the MCS,accompanied by the“0821”rainstorm,successively exhibited modes of the isolated cell,squall line with parallel stratiform(PS)rain,and non-linear system during its life cycle.Among them,the PS mode might have played a key role in causing this flooding.These findings are in line with previous studies.
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)。
