天气雷达对中小尺度灾害性天气具有较强的监测预警能力,对研究中小尺度对流系统的云雨结构、理解降水内部的热力学和动力学过程有很大的帮助。单站点地基雷达受到诸如电磁波衰减、地物干扰等影响,在探测上存在一些限制。为了扩大天气雷...天气雷达对中小尺度灾害性天气具有较强的监测预警能力,对研究中小尺度对流系统的云雨结构、理解降水内部的热力学和动力学过程有很大的帮助。单站点地基雷达受到诸如电磁波衰减、地物干扰等影响,在探测上存在一些限制。为了扩大天气雷达探测区域,需要采用多部天气雷达组网联合探测。然而雷达组网的各雷达之间没有进行统一标定,影响雷达网资料一致性、组网拼图,以及使雷达资料在数值模式同化的应用中受到限制。本文以TRMM(Tropical Rainfall Measuring Mission)卫星搭载的经过精确标定的测雨雷达PR(Precipitation Radar)数据产品作为标准参照源,订正地基雷达GR(Ground-based Radar)的反射率因子偏差。为了减小PR与GR之间观测值对比的不一致性,利用最佳配对数据对比法(ABCD, Available Best Comparable Dataset法),对2008年1月至2014年9月间,江苏省六部地基雷达(南京、常州、连云港、南通、徐州、盐城)的反射率因子值进行订正。最后对方法的应用范围、存在的问题及未来展望进行了讨论。展开更多
Numerical simulations of two heavy rainfall cases in the Changjiang-Huaihe River basin are performed with TRMM/PR (precipitation radar) data incorporated into the PSU/NCAR meso scale model MM5. The mixing ratio of rai...Numerical simulations of two heavy rainfall cases in the Changjiang-Huaihe River basin are performed with TRMM/PR (precipitation radar) data incorporated into the PSU/NCAR meso scale model MM5. The mixing ratio of rainwater q <SUB>r</SUB> is obtained from the R −q <SUB>r</SUB> relation (R is the rainfall rate), and the mixing ratio of water vapor q <SUB>v</SUB> in the model is replaced by q <SUP>1</SUP>′<SUB>v</SUB> = q <SUB>v</SUB>+q <SUB>r</SUB>. Then, TRMM/PR data are used to modify humidity analysis obtained from conventional radiosonde data, and sensitivity experiments (STE) are performed and compared to control experiments (CTL). Results show that both the heavy rainfall distribution and its maximum amounts from STE are improved compared with those from CTL.展开更多
Precipitation radar data derived from the Tropical Rainfall Measuring Mission (TRMM) satellite are used to study precipitation characteristics in 1998 over East Asia (10?38癗, 100C-145癊), especially over mid-latitude...Precipitation radar data derived from the Tropical Rainfall Measuring Mission (TRMM) satellite are used to study precipitation characteristics in 1998 over East Asia (10?38癗, 100C-145癊), especially over mid-latitude land (continental land) and ocean (East China Sea and South China Sea). Results are compared with precipitations in the tropics. Yearly statistics show dominant stratiform rain events over East Asia (about 83.7% by area fraction) contributing to 50% of the total precipitation. Deep convective rains contribute 48% to the total precipitation with a 13.7% area fraction. The statistics also show the unimportance of warm convective rain in East Asia, contributing 1.5% to the total precipitation with a 2.7% area fraction. On a seasonal scale, the results indicate that the rainfall ratio of stratiform rain to deep convective rain is proportional to their rainfall pixel ratio. Seasonal precipitation patterns compare well between Global Precipitation Climatology Project rainfall and TRMM PR measurements except in summer. Studies indicate a clear opposite shift of rainfall amount and events between deep convective and stratiform rains in the meridional in East Asia, which corresponds to the alternative activities of summer monsoon and winter monsoon in the region. The vertical structures of precipitation also exhibit strong seasonal variability in precipitation Contoured Rainrate by Altitude Diagrams (CRADs) and mean profiles in the mid-latitudes of East Asia. However, these structures in the South China Sea are of a tropical type except in winter. The analysis of CRADs reveals a wide range of surface rainfall rates for most deep convective rains, especially in the continental land, and light rain rate for most stratiform rains in East Asia, regardless of over land or ocean.展开更多
In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） ...In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） are compared in temporal and spatial scales in order to comprehend tropical rainfall climatologically. Reasons for the rainfall differences derived from both datasets are discussed. Results show that GPCP and TRMM PR datasets present similar distribution patterns over the Tropics but with some differences in amplitude and location. Generally, the average difference over the ocean of about 0.5 mm d^-1 is larger than that of about 0.1 mm d^-1 over land. Results also show that GPCP tends to underestimate the monthly precipitation over the land region with sparse rain gauges in contrast to regions with a higher density of rain gauge stations. A Probability Distribution Function （PDF） analysis indicates that the GPCP rain rate at its maximum PDF is generally consistent with the TRMM PR rain rate as the latter is less than 8 mm d^-1. When the TRMM PR rain rate is greater than 8 mm d^-1, the GPCP rain rate at its maximum PDF is less by at least 1 mm d^-1 compared to TRMM PR estimates. Results also show an absolute bias of less than 1 mm d^-1 between the two datasets when the rain rate is less than 10 mm d^-1. A large relative bias of the two datasets occurs at weak and heavy rain rates.展开更多
The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relative...The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.展开更多
文摘天气雷达对中小尺度灾害性天气具有较强的监测预警能力,对研究中小尺度对流系统的云雨结构、理解降水内部的热力学和动力学过程有很大的帮助。单站点地基雷达受到诸如电磁波衰减、地物干扰等影响,在探测上存在一些限制。为了扩大天气雷达探测区域,需要采用多部天气雷达组网联合探测。然而雷达组网的各雷达之间没有进行统一标定,影响雷达网资料一致性、组网拼图,以及使雷达资料在数值模式同化的应用中受到限制。本文以TRMM(Tropical Rainfall Measuring Mission)卫星搭载的经过精确标定的测雨雷达PR(Precipitation Radar)数据产品作为标准参照源,订正地基雷达GR(Ground-based Radar)的反射率因子偏差。为了减小PR与GR之间观测值对比的不一致性,利用最佳配对数据对比法(ABCD, Available Best Comparable Dataset法),对2008年1月至2014年9月间,江苏省六部地基雷达(南京、常州、连云港、南通、徐州、盐城)的反射率因子值进行订正。最后对方法的应用范围、存在的问题及未来展望进行了讨论。
基金This research was supported by the National Natural Science Foundation of China under Grant No.49794030.
文摘Numerical simulations of two heavy rainfall cases in the Changjiang-Huaihe River basin are performed with TRMM/PR (precipitation radar) data incorporated into the PSU/NCAR meso scale model MM5. The mixing ratio of rainwater q <SUB>r</SUB> is obtained from the R −q <SUB>r</SUB> relation (R is the rainfall rate), and the mixing ratio of water vapor q <SUB>v</SUB> in the model is replaced by q <SUP>1</SUP>′<SUB>v</SUB> = q <SUB>v</SUB>+q <SUB>r</SUB>. Then, TRMM/PR data are used to modify humidity analysis obtained from conventional radiosonde data, and sensitivity experiments (STE) are performed and compared to control experiments (CTL). Results show that both the heavy rainfall distribution and its maximum amounts from STE are improved compared with those from CTL.
文摘Precipitation radar data derived from the Tropical Rainfall Measuring Mission (TRMM) satellite are used to study precipitation characteristics in 1998 over East Asia (10?38癗, 100C-145癊), especially over mid-latitude land (continental land) and ocean (East China Sea and South China Sea). Results are compared with precipitations in the tropics. Yearly statistics show dominant stratiform rain events over East Asia (about 83.7% by area fraction) contributing to 50% of the total precipitation. Deep convective rains contribute 48% to the total precipitation with a 13.7% area fraction. The statistics also show the unimportance of warm convective rain in East Asia, contributing 1.5% to the total precipitation with a 2.7% area fraction. On a seasonal scale, the results indicate that the rainfall ratio of stratiform rain to deep convective rain is proportional to their rainfall pixel ratio. Seasonal precipitation patterns compare well between Global Precipitation Climatology Project rainfall and TRMM PR measurements except in summer. Studies indicate a clear opposite shift of rainfall amount and events between deep convective and stratiform rains in the meridional in East Asia, which corresponds to the alternative activities of summer monsoon and winter monsoon in the region. The vertical structures of precipitation also exhibit strong seasonal variability in precipitation Contoured Rainrate by Altitude Diagrams (CRADs) and mean profiles in the mid-latitudes of East Asia. However, these structures in the South China Sea are of a tropical type except in winter. The analysis of CRADs reveals a wide range of surface rainfall rates for most deep convective rains, especially in the continental land, and light rain rate for most stratiform rains in East Asia, regardless of over land or ocean.
基金NKBRDPC Grant No.2004CB418304NSFCGrant Nos.40175015 , 40375018 +1 种基金 NSFC grant of the Joint Research Fund for Overseas Chinese Young Scholars(No.40428006)EORC/JAXA(No.206).
文摘In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） are compared in temporal and spatial scales in order to comprehend tropical rainfall climatologically. Reasons for the rainfall differences derived from both datasets are discussed. Results show that GPCP and TRMM PR datasets present similar distribution patterns over the Tropics but with some differences in amplitude and location. Generally, the average difference over the ocean of about 0.5 mm d^-1 is larger than that of about 0.1 mm d^-1 over land. Results also show that GPCP tends to underestimate the monthly precipitation over the land region with sparse rain gauges in contrast to regions with a higher density of rain gauge stations. A Probability Distribution Function （PDF） analysis indicates that the GPCP rain rate at its maximum PDF is generally consistent with the TRMM PR rain rate as the latter is less than 8 mm d^-1. When the TRMM PR rain rate is greater than 8 mm d^-1, the GPCP rain rate at its maximum PDF is less by at least 1 mm d^-1 compared to TRMM PR estimates. Results also show an absolute bias of less than 1 mm d^-1 between the two datasets when the rain rate is less than 10 mm d^-1. A large relative bias of the two datasets occurs at weak and heavy rain rates.
基金supported by funding from the Natural Science Foundation of Jiangsu Province (Grant No. BK20171457)the 2013 Special Fund for Meteorological Scientific Research in the Public Interest (Grant No. GYHY201306078)+1 种基金the National Natural Science Foundation of China (Grant No. 41301399)Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.
