国家级格点实况分析产品在江苏地区的适用性评估分析
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摘要
利用国家级格点实况分析资料与地面气象站实况数据,采用误差分析、技巧评分等方法评估了2017年7月至2018年6月逐时的格点实况产品在江苏地区的地面2 m气温、2 m相对湿度、10 m风和降水要素的一致性和准确性,同时采用MODE检验方法对格点降水产品空间分布偏差进行了分析。结果表明:2 m气温格点实况与自动站观测基本一致,平均绝对误差在0.5~0.8℃,均方根误差在0.8℃左右,其中日最高气温误差较小。格点实况和自动站2 m相对湿度之间的平均绝对误差在5%左右,均方根误差在6%~7%,表现出较高的准确性和稳定性。格点实况10 m风向准确率达到70%左右,而风速准确率仅为56%,与气象站点观测相比有明显差异。格点降水产品的全年有无降水准确率为90%~98%,对于晴雨检验存在带来较大影响的可能。格点实况产品对小雨级别降水的准确率最高,随着降水量级增大,格点实况降水场相比站点观测存在较多的降水漏报,因此,对于降水分量级检验还不适合用格点实况场来替代气象站点观测。设计了一种基于空间形态的降水准确率评分方法对降水空间落区进行检验,格点实况降水场的空间形态准确率评分在0.9左右,较准确地反映了实际降水空间分布。因而,格点实况数据在江苏平原地区都有较高的精度,误差在可接受的范围内,基本可以代替自动站观测作为预报和模式检验的真实实况场,但也存在以下几个方面的问题:(1)格点2 m气温、2 m相对湿度产品在江苏的丘陵地带误差较大,降水产品在海岛气象站准确性较低;(2)格点降水产品一定程度地弱化了大雨以上量级降水强度;(3)格点实况风速产品误差较大,与业务服务需求有一定差距。
Based on the National Gridded Real-time Observation Datasets covering Jiangsu Province released by the National Meteorological Information Centre of CMA and observations of automatic stations,the consistency and accuracy of hourly 2 m temperature,2 m relative humidity,10 m wind and precipitation from July 2017 to June 2018 were evaluated in details by the error statistical analysis,skill score and other methods.The MODE method was applied to reveal the spatial deviation of precipitation between gridded products and observed rainfall records.The results indicate that the mean absolute error of 2 m temperature is between 0.5 and 0.8℃,the root mean square error is around 0.8℃ and the 2 m maximum temperature exhibits a better accuracy than the minimum temperature.The root mean square error range of 2 m relative humidity is 6%-7%,which means that the gridded 2 m temperature and 2 m relative humidity data are well consistent with observation.The accuracy of gridded 10 m wind direction is about 70% while that of wind speed is only 56%,showing a big difference from observation.The verification results of precipitation show that the gridded data with accuracy 90%-98% performs well in forecasting rain or norain.Nonetheless,it may still have a great impact on the precipitation frequency evaluation.TS score of light rain is higher than those in other classes,but it declines sharply when rainfall magnitude increases.Moderate rain or above has a relatively higher probability of detection,which means that the precipitation event is less detected than observed.Therefore,for the quantitative precipitation verification,it is not suitable to replace the observation data by the gridded data.Further study on 24 h accumulated rainfall bias between gridded data and observation indicates that the spatial structure of precipitation can be well described by gridded data.The spatial scores of precipitation designed in this article is above 0.9,which reflects the spatial distribution of actual precipitation.Generally,the gridded data in Jiangsu Plain Region can basically replace the automatic stations as the real-time meteorological field for forecast and model verification.However,there are still some problems as follows:(1)the 2 m temperature and 2 m relative humidity have large errors in the hilly areas of Jiangsu Province,and precipitation product from island stations has a lower accuracy;(2)the intensity of precipitation above heavy rain is weakened by the gridded data;(3)the wind speed value is lower than the observation,leaving a gap with the requirement of forecasting operation.
Based on the National Gridded Real-time Observation Datasets covering Jiangsu Province released by the National Meteorological Information Centre of CMA and observations of automatic stations,the consistency and accuracy of hourly 2 m temperature,2 m relative humidity,10 m wind and precipitation from July 2017 to June 2018 were evaluated in details by the error statistical analysis,skill score and other methods.The MODE method was applied to reveal the spatial deviation of precipitation between gridded products and observed rainfall records.The results indicate that the mean absolute error of 2 m temperature is between 0.5 and 0.8℃,the root mean square error is around 0.8℃ and the 2 m maximum temperature exhibits a better accuracy than the minimum temperature.The root mean square error range of 2 m relative humidity is 6%-7%,which means that the gridded 2 m temperature and 2 m relative humidity data are well consistent with observation.The accuracy of gridded 10 m wind direction is about 70% while that of wind speed is only 56%,showing a big difference from observation.The verification results of precipitation show that the gridded data with accuracy 90%-98% performs well in forecasting rain or norain.Nonetheless,it may still have a great impact on the precipitation frequency evaluation.TS score of light rain is higher than those in other classes,but it declines sharply when rainfall magnitude increases.Moderate rain or above has a relatively higher probability of detection,which means that the precipitation event is less detected than observed.Therefore,for the quantitative precipitation verification,it is not suitable to replace the observation data by the gridded data.Further study on 24 h accumulated rainfall bias between gridded data and observation indicates that the spatial structure of precipitation can be well described by gridded data.The spatial scores of precipitation designed in this article is above 0.9,which reflects the spatial distribution of actual precipitation.Generally,the gridded data in Jiangsu Plain Region can basically replace the automatic stations as the real-time meteorological field for forecast and model verification.However,there are still some problems as follows:(1)the 2 m temperature and 2 m relative humidity have large errors in the hilly areas of Jiangsu Province,and precipitation product from island stations has a lower accuracy;(2)the intensity of precipitation above heavy rain is weakened by the gridded data;(3)the wind speed value is lower than the observation,leaving a gap with the requirement of forecasting operation.
引文
陈笑,赵东,何晓凤,等,2018.基于MODE对模式预报强风风场的检验分析[J].气象,44(8):1009-1019.Chen X,Zhao D,He X F,et al,2018.Evaluation and analysis of model forecast performance of high wind based on MODE method[J].Meteor Mon,44(8):1009-1019(in Chinese).
符娇兰,宗志平,代刊,等,2014.一种定量降水预报误差检验技术及其应用[J].气象,40(7):796-805.Fu J L,Zong Z P,Dai K,et al,2014.Application of a verification method on bias analysis of quantitative precipitation forecasts[J].Meteor Mon,40(7):796-805(in Chinese).
宫宇,代刊,徐珺,等,2018.GRAPES-GFS模式暴雨预报天气学检验特征[J].气象,44(9):1148-1159.Gong Y,Dai K,Xu J,et al,2018.Synoptic verification characteristics of operational GRAPES-GFS model heavy rain event forecast[J].Meteor Mon,44(9):1148-1159(in Chinese).
谷军霞,2017.多源融合实况分析产品在智能网格预报业务中的应用[R].郑州:第34届中国气象学会年会.Gu J X,2017.Application of multi-source fusion real-time analysis products in gridded weather forecast and service[R].Zhengzhou:The 34th Annual Meeting of Chinese Meteorological Society(in Chinese).
李佳,陈葆德,徐同,2016.目标检验方法在高分辨率数值天气预报检验中的应用[J].沙漠与绿洲气象,10(5):1-9.Li J,Chen B D,Xu T,2016.Application of the object-based verification method for high resolution NWP[J].Desert Oasis Meteor,10(5):1-9(in Chinese).
刘凑华,牛若芸,2013.基于目标的降水检验方法及应用[J].气象,39(6):681-690.Liu C H,Niu R Y,2013.Object-based precipitation verification method and its application[J].Meteor Mon, 39(6):681-690(in Chinese).
马雷鸣,鲍旭炜,2017.数值天气预报模式物理过程参数化方案的研究进展[J].地球科学进展,32(7):679-687.Ma L M,Bao X W,2017.Research progress on physical parameterization schemes innumerical weather prediction models[J].Adv Earth Sci,32(7):679-687(in Chinese).
潘旸,谷军霞,徐宾,等,2018a.多源降水数据融合研究及应用进展[J].气象科技进展,8(1):143-152.Pan Y,Gu J X,Xu B,et al,2018a.Advances in multi-source precipitation merging research[J].Adv Meteor Sci Technol,8(1):143-152(in Chinese).
潘旸,谷军霞,宇婧婧,等,2018b.中国区域高分辨率多源降水观测产品的融合方法试验[J].气象学报,76(5):755-766.Pan Y,Gu J X,Yu J J,et al,2018b.Test of merging methods for multisource observed precipitation products at high resolution over China[J].Acta Meteor Sin,76(5):755-766(in Chinese).
师春香,姜立鹏,朱智,等,2018.基于CLDAS2.0驱动数据的中国区域土壤湿度模拟与评估[J].江苏农业科学,46(4):231-236.Shi C X,Jiang L P,Zhu Z,et al,2018.Simulation and assessment of China's regional soil moisture based on CLDAS2.0 forcing data[J].Jiangsu Agric Sci,46(4):231-236(in Chinese).
唐文苑,周庆亮,刘鑫华,等,2017.国家级强对流天气分类预报检验分析[J].气象,43(1):67-76.Tang W Y,Zhou Q L,Liu X H,et al,2017.Anlyisis on verification of national severe convective weather categorical forecasts[J].Meteor Mon,43(1):67-76(in Chinese).
熊秋芬,2011.GRAPES_Meso模式的降水格点检验和站点检验分析[J].气象,37(2):185-193.Xiong Q F,2011.Verification of GRAPES_Meso precipitation forecasts based on fine-mash and station datasets[J].Meteor Mon,37(2):185-193(in Chinese).
尤凤春,王国荣,郭锐,等,2011.MODE方法在降水预报检验中的应用分析[J].气象,37(12):1498-1503.You F C,Wang G R,Guo R,et al,2011.The application analysis of MODE method to the rainfall forecast test[J].Meteor Mon,37(12):1498-1503(in Chinese).
宇婧婧,沈艳,潘旸,等,2015.中国区域逐日融合降水数据集与国际降水产品的对比评估[J].气象学报,73(2):394-410.Yu J J,Shen Y,Pan Y,et al,2015.Comparative assessment between the daily merged precipitation dataset over China and the world's popular counterparts[J].Acta Meteor Sin,73(2):394-410(in Chinese).
赵煜飞,朱江,2015.近50年中国降水格点日值数据集精度及评估[J].高原气象,34(1):50-58.Zhao Y F,Zhu J,2015.Assessing quality of grid daily precipitation datasets in China in recent 50years[J].Plateau Meteor,34(1):50-58(in Chinese).
Chen F,Manning K W,LeMone M A,et al,2007.Description and evaluation of the characteristics of the NCAR High-resolution land data assimilation system[J].J Appl Meteor Climatol,46(6):694-713.
Davis C,Brown B,Bullock R,2006a.Object-based verification of precipitation forecasts.PartⅠ:methodology and application to mesoscale rain areas[J].Mon Wea Rev,134(7):1772-1784.
Davis C,Brown B,Bullock R,2006b.Object-based verification of precipitation forecasts.PartⅡ:application to convective rain systems[J].Mon Wea Rev,134(7):1785-1795.
Davis C A,Brown B G,Bullock B,et al,2009.The method for objectbased diagnostic evaluation(MODE)applied to numerical forecasts from the 2005 NSSL/SPC Spring Program[J].Wea Forecasting,24(5):1252-1267.
Ebert E E,McBride J L,2000.Verification of precipitation in weather systems:determination of systematic errors[J].J Hydrol, 239(1/2/3/4):179-202.
Gilleland E,Ahijevych D A,Brown B G,et al,2010.Verifying forecasts spatially[J].Bull Amer Meteor Soc,91(10):1365-1376.
Jacobs C M J,Moors E J,Ter Maat H W,et al,2010.Evaluation of European Land Data Assimilation System(ELDAS)products using in situ observations[J].Tellus A,60(5):1023-1037.
符娇兰,宗志平,代刊,等,2014.一种定量降水预报误差检验技术及其应用[J].气象,40(7):796-805.Fu J L,Zong Z P,Dai K,et al,2014.Application of a verification method on bias analysis of quantitative precipitation forecasts[J].Meteor Mon,40(7):796-805(in Chinese).
宫宇,代刊,徐珺,等,2018.GRAPES-GFS模式暴雨预报天气学检验特征[J].气象,44(9):1148-1159.Gong Y,Dai K,Xu J,et al,2018.Synoptic verification characteristics of operational GRAPES-GFS model heavy rain event forecast[J].Meteor Mon,44(9):1148-1159(in Chinese).
谷军霞,2017.多源融合实况分析产品在智能网格预报业务中的应用[R].郑州:第34届中国气象学会年会.Gu J X,2017.Application of multi-source fusion real-time analysis products in gridded weather forecast and service[R].Zhengzhou:The 34th Annual Meeting of Chinese Meteorological Society(in Chinese).
李佳,陈葆德,徐同,2016.目标检验方法在高分辨率数值天气预报检验中的应用[J].沙漠与绿洲气象,10(5):1-9.Li J,Chen B D,Xu T,2016.Application of the object-based verification method for high resolution NWP[J].Desert Oasis Meteor,10(5):1-9(in Chinese).
刘凑华,牛若芸,2013.基于目标的降水检验方法及应用[J].气象,39(6):681-690.Liu C H,Niu R Y,2013.Object-based precipitation verification method and its application[J].Meteor Mon, 39(6):681-690(in Chinese).
马雷鸣,鲍旭炜,2017.数值天气预报模式物理过程参数化方案的研究进展[J].地球科学进展,32(7):679-687.Ma L M,Bao X W,2017.Research progress on physical parameterization schemes innumerical weather prediction models[J].Adv Earth Sci,32(7):679-687(in Chinese).
潘旸,谷军霞,徐宾,等,2018a.多源降水数据融合研究及应用进展[J].气象科技进展,8(1):143-152.Pan Y,Gu J X,Xu B,et al,2018a.Advances in multi-source precipitation merging research[J].Adv Meteor Sci Technol,8(1):143-152(in Chinese).
潘旸,谷军霞,宇婧婧,等,2018b.中国区域高分辨率多源降水观测产品的融合方法试验[J].气象学报,76(5):755-766.Pan Y,Gu J X,Yu J J,et al,2018b.Test of merging methods for multisource observed precipitation products at high resolution over China[J].Acta Meteor Sin,76(5):755-766(in Chinese).
师春香,姜立鹏,朱智,等,2018.基于CLDAS2.0驱动数据的中国区域土壤湿度模拟与评估[J].江苏农业科学,46(4):231-236.Shi C X,Jiang L P,Zhu Z,et al,2018.Simulation and assessment of China's regional soil moisture based on CLDAS2.0 forcing data[J].Jiangsu Agric Sci,46(4):231-236(in Chinese).
唐文苑,周庆亮,刘鑫华,等,2017.国家级强对流天气分类预报检验分析[J].气象,43(1):67-76.Tang W Y,Zhou Q L,Liu X H,et al,2017.Anlyisis on verification of national severe convective weather categorical forecasts[J].Meteor Mon,43(1):67-76(in Chinese).
熊秋芬,2011.GRAPES_Meso模式的降水格点检验和站点检验分析[J].气象,37(2):185-193.Xiong Q F,2011.Verification of GRAPES_Meso precipitation forecasts based on fine-mash and station datasets[J].Meteor Mon,37(2):185-193(in Chinese).
尤凤春,王国荣,郭锐,等,2011.MODE方法在降水预报检验中的应用分析[J].气象,37(12):1498-1503.You F C,Wang G R,Guo R,et al,2011.The application analysis of MODE method to the rainfall forecast test[J].Meteor Mon,37(12):1498-1503(in Chinese).
宇婧婧,沈艳,潘旸,等,2015.中国区域逐日融合降水数据集与国际降水产品的对比评估[J].气象学报,73(2):394-410.Yu J J,Shen Y,Pan Y,et al,2015.Comparative assessment between the daily merged precipitation dataset over China and the world's popular counterparts[J].Acta Meteor Sin,73(2):394-410(in Chinese).
赵煜飞,朱江,2015.近50年中国降水格点日值数据集精度及评估[J].高原气象,34(1):50-58.Zhao Y F,Zhu J,2015.Assessing quality of grid daily precipitation datasets in China in recent 50years[J].Plateau Meteor,34(1):50-58(in Chinese).
Chen F,Manning K W,LeMone M A,et al,2007.Description and evaluation of the characteristics of the NCAR High-resolution land data assimilation system[J].J Appl Meteor Climatol,46(6):694-713.
Davis C,Brown B,Bullock R,2006a.Object-based verification of precipitation forecasts.PartⅠ:methodology and application to mesoscale rain areas[J].Mon Wea Rev,134(7):1772-1784.
Davis C,Brown B,Bullock R,2006b.Object-based verification of precipitation forecasts.PartⅡ:application to convective rain systems[J].Mon Wea Rev,134(7):1785-1795.
Davis C A,Brown B G,Bullock B,et al,2009.The method for objectbased diagnostic evaluation(MODE)applied to numerical forecasts from the 2005 NSSL/SPC Spring Program[J].Wea Forecasting,24(5):1252-1267.
Ebert E E,McBride J L,2000.Verification of precipitation in weather systems:determination of systematic errors[J].J Hydrol, 239(1/2/3/4):179-202.
Gilleland E,Ahijevych D A,Brown B G,et al,2010.Verifying forecasts spatially[J].Bull Amer Meteor Soc,91(10):1365-1376.
Jacobs C M J,Moors E J,Ter Maat H W,et al,2010.Evaluation of European Land Data Assimilation System(ELDAS)products using in situ observations[J].Tellus A,60(5):1023-1037.