L波段雷达探空高度评估及其质量标识
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摘要
以美国NCEP FNL分析数据和我国GRAPES_GFS预报数据为背景场,对2016年7月1日-2017年6月30日北京探空站的L波段秒级探空位势高度(探空高度)从观测余差、平均偏差、标准偏差、概率密度分布、峰度系数、偏度系数、相关系数和均方根误差等参数进行误差分析。根据分析结果对探空高度进行质量标识,并根据质量标识的结果再次求解参数,评估质量标识效果。结果表明:探空高度质量较好,无论是基于NCEP FNL还是GRAPES_GFS.探空高度的误差基本在±5 dagpm以内。探测高层的观测余差平均偏差和标准偏差表明基于GRAPES_GFS的评估优于NCEP FNL的评估。单一个例选取的可疑点和错误点阈值具有误差特征、自适应的特点。
Using analysis data of NCEP FNL and forecast data of GRAPES_GFS as background fields, the error analysis of geopotential height(sounding height) data of Beijing sounding station are obtained from observation residuals, average deviations, standard deviation, probability density distributions, kurtosis coefficients, skewness coefficients, correlation coefficient and root mean square error. According to assessment results, data quality is marked, and parameters are solved according to results of the quality mark. Test results show whether based on NCEP FNL or GRAPES_GFS, the error of the sounding height is basically within ±5 dagpm, and the absolute value of observation residuals increases with the decrease of air pressure. Observation residuals below 100 hPa is basically within ±3 dagpm. Observation residuals are mostly negative at the top of 100 hPa. The average deviation, standard deviation, probability density distribution,kurtosis coefficient, skewness coefficient, correlation coefficient and root mean square error are analyzed and evaluated from characteristics and distribution characteristics of the seasonal error, all of which show that the quality of data at height of detection potential is good, and each parameter is close to their optimal state. However, at the high level(10-30 hPa), the average deviation and standard deviation show obviously that the evaluation result of GRAPES_GFS is better than that of NCEP FNL, and the other parameters are basically the same and the difference is small. The average deviations plus standard deviation of two times is selected as the suspicious threshold value of the potential height at a single moment, and average deviation plus standard deviation is selected as the error threshold of the potential height. This choice is not only meaningful in mathematical statistics, but also shows that the threshold value is based on the background field error feature and self-adaptive threshold value, which can help to find out the true error point for correction.
Using analysis data of NCEP FNL and forecast data of GRAPES_GFS as background fields, the error analysis of geopotential height(sounding height) data of Beijing sounding station are obtained from observation residuals, average deviations, standard deviation, probability density distributions, kurtosis coefficients, skewness coefficients, correlation coefficient and root mean square error. According to assessment results, data quality is marked, and parameters are solved according to results of the quality mark. Test results show whether based on NCEP FNL or GRAPES_GFS, the error of the sounding height is basically within ±5 dagpm, and the absolute value of observation residuals increases with the decrease of air pressure. Observation residuals below 100 hPa is basically within ±3 dagpm. Observation residuals are mostly negative at the top of 100 hPa. The average deviation, standard deviation, probability density distribution,kurtosis coefficient, skewness coefficient, correlation coefficient and root mean square error are analyzed and evaluated from characteristics and distribution characteristics of the seasonal error, all of which show that the quality of data at height of detection potential is good, and each parameter is close to their optimal state. However, at the high level(10-30 hPa), the average deviation and standard deviation show obviously that the evaluation result of GRAPES_GFS is better than that of NCEP FNL, and the other parameters are basically the same and the difference is small. The average deviations plus standard deviation of two times is selected as the suspicious threshold value of the potential height at a single moment, and average deviation plus standard deviation is selected as the error threshold of the potential height. This choice is not only meaningful in mathematical statistics, but also shows that the threshold value is based on the background field error feature and self-adaptive threshold value, which can help to find out the true error point for correction.
引文
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[7]刘超,花丛,张恒德,等.L波段探空雷达秒数据在污染天气边界层分析中的应用.气象,2017,43(5):591-597.
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[10]胡梦玲,游庆龙,林厚博.青藏高原地区多套位势高度和风场再分析资料的对比分析.冰川冻土,2015,37(5):1229-1244.
[11]蔡兆男,王永,Liu Xiong,等.利用探空资料验证COME卫星奥氧数据.应用气象学报,2009,20(3):337-345.
[12]彭艳秋,王卫国,刘煜,等.利用不同资料研究我国大陆上空柱水汽含量.应用气象学报,2012,23(1):59-68.
[13]吴蕾,陈洪滨,康雪.风廓线雷达与L波段雷达探空测风对比分析.气象科技,2014,42(2):225-230.
[14]赵静,曹晓钟,代桃高,等.毫米波云雷达与探空测云数据对比分析.气象,2017,43(1):101-107.
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[17]杜明斌,杨引明,丁金.COSMIC反演精度和有关特性的检验.应用气象学报,2009,29(5):586-593.
[18]王洪,曹云昌,肖稳安.COSMIC掩里数据与L波段探空数据的对比分析.气象,2010,36(9):14-20.
[19]徐桂荣,乐新安,张文刚,等.COSMIC掩星资料反演青藏高原大气廓线与探空观测的对比分析.暴雨灾害,2016,35(4):315-325.
[20]马颍,姚雯,黄炳勋.59型与L波段探空仪温度和位势高度记录对比.应用气象学报,2010,21(2),214-220.
[21]赵世军,苏小勇,高太长. RS92探空仪温压湿测量性能分析.气象科技,2012,40(1):31-34.
[22]陶士伟,张跃堂,陈卫红,等.全球观测资料质量监视评估.气象,2006,32(6):53-58.
[23]马颖,姚雯,黄炳勋.用初估场对比中芬探空仪温度和位势高度记录.应用气象学报,2011,22(3):336-345.
[24]姚雯,马颖,王战,等.用数值预报场间接对比新疆两种型号探空系统.应用气象学报,2012,23(2):159-166.
[25]陈哲,杨溯,刘靓珂.1979-2012年中国探空相对湿度资料的非均一性检验与订正.气象,2015,41(11):1374-1382.
[26]苏志侠,吕世华,罗四维.美国NCEP/NCAR全球再分析资料及其初步分析.高原气象,1999,18(2):209-218.
[27]徐影,丁一汇,赵宗慈.美国NCEP/NCAR近50年全球再分析资料在我国气候变化研究中可信度的初步分析.应用气象学报,2001,12(3):337-347.
[28]秦育婧,王盘兴,管兆勇,等.两种再分析资料的Hadley环流比较.科学通报,2006,51(12):1469-1473.
[29]赵天保,符淙斌.几种再分析地表气温资料在中国区域的适用性评估.高原气象,2009,28(3):594-606.
[30]田笑,智协飞,徐海明.NCEP和JRA再分析资料与探空资料的位势高度比较分析.干旱气象,2013,31(2):254-262.
[31]佟铃,彭新东,范广州,等.GRAPES全球模式的误差评估和订正.大气科学,2017,41(2):333-344.
[2]王英,熊安元.L波段探空仪器换型对高空湿度资料的影响.应用气象学报,2015,26(1):76-86.
[3] Dai A G,Wang J H,Thorne P W,et al. A new approach to homogenize daily radiosonde humidity data. J Climate,2011,24(4):965-991.
[4]李伟,张春晖,孟昭林,等.L波段气象探测网运行监控系统设计.应用气象学报,2010,21(1):115-120.
[5]李伟,赵培涛,郭启云,等.国产GPS探空仪国际比对试验结果.应用气象学报,2011,22(4):453-462.
[6]郭启云,李伟.L波段雷达探空系统气压测量值与气压反算气压值的误差分析.气象水文海洋仪器,2013,3(1):9-13.
[7]刘超,花丛,张恒德,等.L波段探空雷达秒数据在污染天气边界层分析中的应用.气象,2017,43(5):591-597.
[8]李刚,谭言科,李崇银,等.近30年北半球冬季臭氧总量分布特征及其与平流层温度的关系.地球物理学报,2015,58(5):1475-1491.
[9]周顺武,张人禾.青藏高原地区上空NCEP/NCAR再分析温度和位势高度资料与观测资料的比较分析.气候与环境研究,2009.14(2):284-292.
[10]胡梦玲,游庆龙,林厚博.青藏高原地区多套位势高度和风场再分析资料的对比分析.冰川冻土,2015,37(5):1229-1244.
[11]蔡兆男,王永,Liu Xiong,等.利用探空资料验证COME卫星奥氧数据.应用气象学报,2009,20(3):337-345.
[12]彭艳秋,王卫国,刘煜,等.利用不同资料研究我国大陆上空柱水汽含量.应用气象学报,2012,23(1):59-68.
[13]吴蕾,陈洪滨,康雪.风廓线雷达与L波段雷达探空测风对比分析.气象科技,2014,42(2):225-230.
[14]赵静,曹晓钟,代桃高,等.毫米波云雷达与探空测云数据对比分析.气象,2017,43(1):101-107.
[15]朱元竞,李万彪,陈勇.GMS-5估计可降水量的研究.应用气象学报,1998,9(1):8-14.
[16]孙学金,赵世军,余鹏.GPS掩星切点水平漂移规律的数值研究.应用气象学报,2004,15(2):174-180.
[17]杜明斌,杨引明,丁金.COSMIC反演精度和有关特性的检验.应用气象学报,2009,29(5):586-593.
[18]王洪,曹云昌,肖稳安.COSMIC掩里数据与L波段探空数据的对比分析.气象,2010,36(9):14-20.
[19]徐桂荣,乐新安,张文刚,等.COSMIC掩星资料反演青藏高原大气廓线与探空观测的对比分析.暴雨灾害,2016,35(4):315-325.
[20]马颍,姚雯,黄炳勋.59型与L波段探空仪温度和位势高度记录对比.应用气象学报,2010,21(2),214-220.
[21]赵世军,苏小勇,高太长. RS92探空仪温压湿测量性能分析.气象科技,2012,40(1):31-34.
[22]陶士伟,张跃堂,陈卫红,等.全球观测资料质量监视评估.气象,2006,32(6):53-58.
[23]马颖,姚雯,黄炳勋.用初估场对比中芬探空仪温度和位势高度记录.应用气象学报,2011,22(3):336-345.
[24]姚雯,马颖,王战,等.用数值预报场间接对比新疆两种型号探空系统.应用气象学报,2012,23(2):159-166.
[25]陈哲,杨溯,刘靓珂.1979-2012年中国探空相对湿度资料的非均一性检验与订正.气象,2015,41(11):1374-1382.
[26]苏志侠,吕世华,罗四维.美国NCEP/NCAR全球再分析资料及其初步分析.高原气象,1999,18(2):209-218.
[27]徐影,丁一汇,赵宗慈.美国NCEP/NCAR近50年全球再分析资料在我国气候变化研究中可信度的初步分析.应用气象学报,2001,12(3):337-347.
[28]秦育婧,王盘兴,管兆勇,等.两种再分析资料的Hadley环流比较.科学通报,2006,51(12):1469-1473.
[29]赵天保,符淙斌.几种再分析地表气温资料在中国区域的适用性评估.高原气象,2009,28(3):594-606.
[30]田笑,智协飞,徐海明.NCEP和JRA再分析资料与探空资料的位势高度比较分析.干旱气象,2013,31(2):254-262.
[31]佟铃,彭新东,范广州,等.GRAPES全球模式的误差评估和订正.大气科学,2017,41(2):333-344.