GRAPES＿GFS在西南地区的预报稳定性及其误差与地形的关系
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摘要
根据地形特征,将西南地区划分为高原区、边坡区和盆地区,引入统计学"不稳定度"定量描述模式预报稳定性,对2016年6月—2017年9月全球中期天气预报(GRAPES_GFS)和欧洲中期天气预报中心(EC)在西南地区的高层形势场、主要的天气影响系统和地面要素预报性能进行了主客观检验,一定程度揭示了GRAPES_GFS和EC在西南地区的预报稳定性、地形的影响以及二模式预报性能的异同。结果显示:GRAPES_GFS高空高度场、温度场预报不稳定度分布呈北高南低型,相对湿度、风速预报不稳定度大值区在高原边缘;各要素预报不稳定度季节性周期最为显著,其位相和振幅因要素不同而有所不同;地形主要影响温度和风向预报误差值,但对相对湿度和风速预报的影响则体现在误差随时效的增长速率差异上;"漏报"是模式对西南地区天气系统的主要预报误差源,"低报"则是模式对西南地区2 m温度预报误差的最大来源;模式对西南地区降水落区预报有效率大约为50%,但强度预报通常偏低。EC与GRAPES_GFS的误差特征没有本质区别,但EC误差更小,稳定性更高。
By introducing the statistics concept of"degree of instability"and dividing Southwest China into three kinds of areas, namely, the plateau, the side slope and the basin area in consideration of the terrain complexity there, numerical model GRAPES_GFS and EC's prediction stabilities, their temporal and spatial variations, the influence of the terrain on the models' performance as well as the difference between the two models were analyzed subjectively and objectively based on their predictions over the period from July, 2016 to September, 2017. The results showed that GRAPES_GFS's prediction instability of geopotential height and temperature fields appeared higher in the north of the area, while higher instability of relative humidity and wind speed was around the Tibet Plateau to the north and east. The instability had obvious seasonal fluctuation with different phases and amplitude as different elements. Terrain impacted mainly on the prediction error value of temperature and wind direction. For both relative humidity and wind speed, its impact is more on the rate of error growth. The"lower prediction"led to a high failure rate of GRAPES_GFS in 2 m temperature prediction, and"missing"was its major error source of weather systems prediction. GRAPES_GFS predicted the location of rainfall in the Southwest China by about effective rate of 50%, although its prediction of rainfall intensity was usually lower than the observation. The two models had little essential difference in the characteristics of prediction error. Compared to GRAPES_GFS,EC had less errors and higher stability.
By introducing the statistics concept of"degree of instability"and dividing Southwest China into three kinds of areas, namely, the plateau, the side slope and the basin area in consideration of the terrain complexity there, numerical model GRAPES_GFS and EC's prediction stabilities, their temporal and spatial variations, the influence of the terrain on the models' performance as well as the difference between the two models were analyzed subjectively and objectively based on their predictions over the period from July, 2016 to September, 2017. The results showed that GRAPES_GFS's prediction instability of geopotential height and temperature fields appeared higher in the north of the area, while higher instability of relative humidity and wind speed was around the Tibet Plateau to the north and east. The instability had obvious seasonal fluctuation with different phases and amplitude as different elements. Terrain impacted mainly on the prediction error value of temperature and wind direction. For both relative humidity and wind speed, its impact is more on the rate of error growth. The"lower prediction"led to a high failure rate of GRAPES_GFS in 2 m temperature prediction, and"missing"was its major error source of weather systems prediction. GRAPES_GFS predicted the location of rainfall in the Southwest China by about effective rate of 50%, although its prediction of rainfall intensity was usually lower than the observation. The two models had little essential difference in the characteristics of prediction error. Compared to GRAPES_GFS,EC had less errors and higher stability.
引文
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[2]郑晓辉,徐国强,贾丽红,等. GRAPES_Meso区域模式及云计算方案引进及预报效果检验[J].大气科学,2016,40(5):907-919
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[12]邱静雅,李国平,郝丽萍.高原涡与西南涡相互作用引发四川暴雨的位涡诊断[J].高原气象,2015,34(6):1556-1565
[13]蒋婉君,李国平,王兴涛.基于TRMM资料的高原涡与西南涡引发强降水的对比研究[J].大气科学,2015,39(2):249-259
[14]何光碧,师锐.三次高原切变线过程演变特征及其对降水的影响[J].高原气象,2014,33(3):615-625
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[16]何光碧,高文良,屠妮妮. 2000-2007夏季青藏高原低涡切变线观测事实分析[J].高原气象,2009,28(3):549-555
[17]高正旭,王晓玲,李维京.西南低涡的统计特征及其对湖北降水的影响[J].暴雨灾害,2009,28(4):302305
[18]郑京华,董光英,梁涛等.一次西南涡东移诱发的罕见暴雨诊断分析[J].暴雨灾害,2009,28(3):229-234
[19]段玮,肖子牛,周泓.一次典型川滇切变线暴雨过程的诊断分析[J].暴雨灾害,2017,36(3):207-216
[20]李明,高维英,侯建忠,等.一次西南涡东北移对川陕大暴雨影响的分析[J].高原气象,2013,32(1):133-144
[21]孟晓文,隆霄,周国兵,等.同化常规资料对重庆地区一次大暴雨过程的数值模拟研究[J].暴雨灾害,2017, 36(4):309-318
[22]肖玉华,何光碧,陈静,等.AREM对2008年西南地区低温雨雪天气预报能力的检验[J].暴雨灾害,2009,28(2):152156.
[23]肖玉华,赵静,蒋丽娟.数值模式预报性能的地域性特点初步分析[J].暴雨灾害,2010,29(4):64-69
[24]肖玉华,康岚,徐琳娜,等.西南区域中尺度数值模式预报性能及其与天气过程关系初探[J].气象,2013,39(10):1 257-1 264
[25]赵滨,张博.邻域空间检验方法在降水评估中的应用[J].暴雨灾害,2018,37(01):1-7
[26]王在文,梁旭东,范水勇,等.数值模式降水评分对分辨率的敏感性初探[J].暴雨灾害,2016, 35(1):10-16
[27]李明,袁凯,翟红楠.一种面向对象的强降水精细化预报质量检验方法及应用[J].暴雨灾害,2017,36(1):81-85
[28]何光碧,屠妮妮,张利红.一次低涡暴雨过程发生机制及其模式预报分析[J].暴雨灾害,2014,33(3):239-246
[29]何光碧,顾清源,陈静,等. AREM模式对“05.7”四川大暴雨的敏感性试验[J].暴雨灾害,2007,26(3):199-204
[2]郑晓辉,徐国强,贾丽红,等. GRAPES_Meso区域模式及云计算方案引进及预报效果检验[J].大气科学,2016,40(5):907-919
[3]黄丽萍,陈德辉,邓连堂,等. GRAPES_Meso V4.0主要技术改进和预报效果检验[J].应用气象学报,2017,28(1):25-37
[4]陈贤. GRAPES全球系统的研发进展[G].Annual Report of CAMS,2008:36-37
[5]苏勇,沈学顺,张倩.质量守恒的订正算法在GRAPES_GFS中的应用[J].应用气象学报,2016,27(6):666-675
[6]马占山,刘奇俊,秦琰琰. GRAPES_GFS不同湿物理过程对云降水预报性能的诊断与评估[J].高原气象,2016,35(4):989-1003
[7]王金成,陆慧娟,韩威,等. GRAPES全球三维变分同化业务系统性能[J].2017,28(1):11-24
[8]沈学顺,苏勇,胡江林,等. GRAPES_GFS全球中期预报系统的研发和业务化[J].应用气象学报,2017,28(1):1-10
[9]郁淑华.夏季青藏高原低涡研究进展[J].暴雨灾害,2008,27(4):367-372
[10]李川,陈静,何光碧.青藏高原东侧陡峭地形对一次强降水天气过程的影响[J].高原气象,2006,25(3):442450.
[11]郑庆林,王必正,宋青丽.青藏高原背风坡地形对西南低涡过程影响的数值试验[J].高原气象,1997,16:225-234
[12]邱静雅,李国平,郝丽萍.高原涡与西南涡相互作用引发四川暴雨的位涡诊断[J].高原气象,2015,34(6):1556-1565
[13]蒋婉君,李国平,王兴涛.基于TRMM资料的高原涡与西南涡引发强降水的对比研究[J].大气科学,2015,39(2):249-259
[14]何光碧,师锐.三次高原切变线过程演变特征及其对降水的影响[J].高原气象,2014,33(3):615-625
[15]郁淑华,高文良,彭俊.近13年青藏高原切变线活动及其对中国降水影响的若干统计[J].高原气象,2013,32(6):1 527-1537
[16]何光碧,高文良,屠妮妮. 2000-2007夏季青藏高原低涡切变线观测事实分析[J].高原气象,2009,28(3):549-555
[17]高正旭,王晓玲,李维京.西南低涡的统计特征及其对湖北降水的影响[J].暴雨灾害,2009,28(4):302305
[18]郑京华,董光英,梁涛等.一次西南涡东移诱发的罕见暴雨诊断分析[J].暴雨灾害,2009,28(3):229-234
[19]段玮,肖子牛,周泓.一次典型川滇切变线暴雨过程的诊断分析[J].暴雨灾害,2017,36(3):207-216
[20]李明,高维英,侯建忠,等.一次西南涡东北移对川陕大暴雨影响的分析[J].高原气象,2013,32(1):133-144
[21]孟晓文,隆霄,周国兵,等.同化常规资料对重庆地区一次大暴雨过程的数值模拟研究[J].暴雨灾害,2017, 36(4):309-318
[22]肖玉华,何光碧,陈静,等.AREM对2008年西南地区低温雨雪天气预报能力的检验[J].暴雨灾害,2009,28(2):152156.
[23]肖玉华,赵静,蒋丽娟.数值模式预报性能的地域性特点初步分析[J].暴雨灾害,2010,29(4):64-69
[24]肖玉华,康岚,徐琳娜,等.西南区域中尺度数值模式预报性能及其与天气过程关系初探[J].气象,2013,39(10):1 257-1 264
[25]赵滨,张博.邻域空间检验方法在降水评估中的应用[J].暴雨灾害,2018,37(01):1-7
[26]王在文,梁旭东,范水勇,等.数值模式降水评分对分辨率的敏感性初探[J].暴雨灾害,2016, 35(1):10-16
[27]李明,袁凯,翟红楠.一种面向对象的强降水精细化预报质量检验方法及应用[J].暴雨灾害,2017,36(1):81-85
[28]何光碧,屠妮妮,张利红.一次低涡暴雨过程发生机制及其模式预报分析[J].暴雨灾害,2014,33(3):239-246
[29]何光碧,顾清源,陈静,等. AREM模式对“05.7”四川大暴雨的敏感性试验[J].暴雨灾害,2007,26(3):199-204
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