In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar o...In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.展开更多
层状云降水中,0℃层融化效应会引起雷达反射率因子局部增大,若不进行订正,则会高估雷达估测的降水。本文提出一种基于新一代天气雷达反射率因子垂直廓线的0℃层亮带自动识别与订正算法,以减小因亮带造成的降水高估。本研究首先对降水类...层状云降水中,0℃层融化效应会引起雷达反射率因子局部增大,若不进行订正,则会高估雷达估测的降水。本文提出一种基于新一代天气雷达反射率因子垂直廓线的0℃层亮带自动识别与订正算法,以减小因亮带造成的降水高估。本研究首先对降水类型进行分类,在SHY95的基础上增加了垂直方向的反射率因子三维特征,避免亮带的反射率因子高值区被误识别为对流云区;其次,在层状云区识别出一个可能的亮带影响区,在其中查找亮带,采用旋转坐标系法精确的识别亮带的顶、底高度;最后,利用最小二乘法拟合亮带上、下层的斜率,平滑垂直廓线(VPR,Vertical Profile of Reflectivity)的显著突出部分。将该方法应用于北京地区2010—2011年10次包含亮带的降水过程,得到的亮带订正后的均方根误差ERMS、平均绝对误差ERMA、平均相对误差BRM值较初值均有显著减小(分别减小1.538 mm,0.417和0.468)。结果表明,该方法能够有效地识别与订正亮带,使得定量测量降水精度有所提高。展开更多
Bangkok is located in a low land area,and floods frequently occur from rainfall,river discharge,and tides.High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models.The ...Bangkok is located in a low land area,and floods frequently occur from rainfall,river discharge,and tides.High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models.The main objective of this study is to establish a method that improves the accuracy of precipitation estimates by merging rainfall from three sources:an infrared channel from the Himawari-8 satellite,rain gauges,and ground-based radar observations.This study applied cloud classification and bias correction using rain gauges to discriminate these errors.The bias factors were interpolated using the ordinary kriging(OK)method to fill in the areas of estimated rainfall where no rain gauge was available.The results show that bias correction improved the accuracy of radar and Himawari-8 rainfall estimates before their combination.The merged algorithm was then adopted to produce hourly merged rainfall products(GSR).Compared to the initial estimated product,the GSR is significantly more accurate.The merging algorithm increases the spatial resolution and quality of rainfall estimates and is simple to use.Furthermore,these findings not only reveal the potential and limitations of the merged algorithm but also provide useful information for future retrieval algorithm enhancement.展开更多
基金National Key Research and Development Program of China(2017YFC1404700,2018YFC1506905)Open Research Program of the State Key Laboratory of Severe Weather(2018LASW-B09,2018LASW-B08)+7 种基金Science and Technology Planning Project of Guangdong Province,China(2019B020208016,2018B020207012,2017B020244002)National Natural Science Foundation of China(41375038)Special Scientific Research Fund of Meteorological Public Welfare Profession of China(GHY201506006)2017-2019Meteorological Forecasting Key Technology Development Special Grant(YBGJXM(2017)02-05)Guangdong Science&Technology Plan Project(2015A020217008)Zhejiang Province Major Science and Technology Special Project(2017C03035)Scientific and Technological Research Projects of Guangdong Meteorological Service(GRMC2018M10)Natural Science Foundation of Guangdong Province(2018A030313218)
文摘In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.
文摘层状云降水中,0℃层融化效应会引起雷达反射率因子局部增大,若不进行订正,则会高估雷达估测的降水。本文提出一种基于新一代天气雷达反射率因子垂直廓线的0℃层亮带自动识别与订正算法,以减小因亮带造成的降水高估。本研究首先对降水类型进行分类,在SHY95的基础上增加了垂直方向的反射率因子三维特征,避免亮带的反射率因子高值区被误识别为对流云区;其次,在层状云区识别出一个可能的亮带影响区,在其中查找亮带,采用旋转坐标系法精确的识别亮带的顶、底高度;最后,利用最小二乘法拟合亮带上、下层的斜率,平滑垂直廓线(VPR,Vertical Profile of Reflectivity)的显著突出部分。将该方法应用于北京地区2010—2011年10次包含亮带的降水过程,得到的亮带订正后的均方根误差ERMS、平均绝对误差ERMA、平均相对误差BRM值较初值均有显著减小(分别减小1.538 mm,0.417和0.468)。结果表明,该方法能够有效地识别与订正亮带,使得定量测量降水精度有所提高。
基金Srinakharinwirot University,Thailand,grant number[035/2563]Thailand Science Research and Innovation(TSRI)Basic Research Fund:Fiscal year 2022 under project number[FRB650048/0164].
文摘Bangkok is located in a low land area,and floods frequently occur from rainfall,river discharge,and tides.High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models.The main objective of this study is to establish a method that improves the accuracy of precipitation estimates by merging rainfall from three sources:an infrared channel from the Himawari-8 satellite,rain gauges,and ground-based radar observations.This study applied cloud classification and bias correction using rain gauges to discriminate these errors.The bias factors were interpolated using the ordinary kriging(OK)method to fill in the areas of estimated rainfall where no rain gauge was available.The results show that bias correction improved the accuracy of radar and Himawari-8 rainfall estimates before their combination.The merged algorithm was then adopted to produce hourly merged rainfall products(GSR).Compared to the initial estimated product,the GSR is significantly more accurate.The merging algorithm increases the spatial resolution and quality of rainfall estimates and is simple to use.Furthermore,these findings not only reveal the potential and limitations of the merged algorithm but also provide useful information for future retrieval algorithm enhancement.
