首页 | 本学科首页   官方微博 | 高级检索  
     检索      

GRAPES千米尺度模式在西南复杂地形区降水预报偏差与成因初探
引用本文:谢漪云,王建捷.GRAPES千米尺度模式在西南复杂地形区降水预报偏差与成因初探[J].气象学报,2021,79(5):732-749.
作者姓名:谢漪云  王建捷
作者单位:1.中国气象科学研究院,北京,100081
基金项目:中国气象局GRAPES发展专项(GRAPES-FZZX-2019)
摘    要:利用2019年夏季(6—8月)西南复杂地形区地面观测站逐时和逐日降水量观测数据,从降水量和降水频率入手,对同期GRAPES-Meso 3 km业务模式短期(36 h以内)降水预报性能,特别是在不同典型地貌区—四川盆地子区、云贵高原北部子区和南部子区、青藏高原东缘山地子区的预报偏差进行细致评估与分析。结果表明:(1)GRAPES-Meso 3 km模式能合理地刻画出西南复杂地形区夏季日降水和日内尺度降水的主要特征,以及小时降水频次-强度的基本关系。(2)在各子区,模式日降水量(频率)预报表现为清晰的正偏差,正偏差在盆地子区最显著,为观测值的1.1倍(0.3倍);日降水量正偏差主要由强降水日降水量预报偏大引起,但频率正偏差在云贵高原南、北子区与其他两个子区不同,主要是中小雨日数预报偏多的贡献;强降水(中小雨)落区预报存在明显(轻微)偏大倾向,强降水预报落区偏大频率在青藏高原东缘山地子区最高,达82.8%,在云贵高原南部子区最低,为53.6%。(3)日循环上,各时次小时降水量(频率)预报整体偏大,且主要正偏差出现在观测的夜雨峰值时段,其中海拔1200 m以下区域的降水频率正偏差从夜间峰值区延续到中午,模式偏强的日降水量预报往往表现为日内偏长的降水时长或小时降水空报。(4)诊断分析显示,模式在四川盆地区突出的夏季日降水预报正偏差是模式对流层低层在云贵高原南-东南侧偏强的西南风预报与西南地区特殊地形结合的产物。 

关 键 词:西南复杂地形区    千米尺度模式    降水量和频率    偏差特征
收稿时间:2021/3/8 0:00:00
修稿时间:2021/6/8 0:00:00

Preliminary study on the deviation and cause of precipitation prediction of GRAPES kilometer scale model in southwest complex terrain area
XIE Yiyun,WANG Jianjie.Preliminary study on the deviation and cause of precipitation prediction of GRAPES kilometer scale model in southwest complex terrain area[J].Acta Meteorologica Sinica,2021,79(5):732-749.
Authors:XIE Yiyun  WANG Jianjie
Institution:1.Chinese Academy of Meteorological Sciences,Beijing 100081,China2.National Meteorological Center,Beijing 100081,China
Abstract:The performance of the kilometer scale operational model (GRAPES-Meso 3 km) for short-term precipitation forecast over complex terrain areas of the Sichuan basin, the Yunnan-Guizhou Plateau, and the highlands of the eastern edge of Tibet Plateau in southwestern China has been carefully evaluated in term of the rainfall amount and frequency. The model forecasts are verified against daily and hourly precipitation data observed in the summer of 2019 at thousands of surface stations in the same area. Results show that: (1) The model can reasonably capture daily rainfall distribution and diurnal cycle of summer mean precipitation as well as the key relationship between hourly rainfall frequency and intensity over the complex terrains. (2) Positive forecast deviations of summer daily rainfall amount (frequency) are obvious in the whole research region, and the largest deviations are centered in Sichuan basin with the bias of 1.1 times (0.3 times) of the observed values on average. Positive deviations of daily rainfall amount forecast in the complex terrains are mainly contributed by the forecast amount of heavy rainfall (above 25 mm/d) events. Positive deviations of frequency forecast are mostly caused by light to moderate rainfall events over Yunnan-Guizhou Plateau and by heavy rainfall events over other areas. Areas of heavy rainfall from forecasts are often larger than observations, with the highest occurrence frequency of 82.8% occurring in the eastern edge of the Tibet Plateau and the lowest of 53.6% appearing in the southern Yunnan-Guizhou Plateau. (3) In terms of diurnal cycle, positive biases of hourly rainfall amount (frequency) are the main characteristics, and large deviations mostly occur around the peak time of observed rainfall in the night for different terrain heights. Large biases of hourly rainfall frequency in areas below 1200 m elevation occur from the peak time of rainfall in the night to the noon time of the next day, which indicates that the over prediction of daily rainfall amount was balanced by unrealistically long precipitation duration or by false hourly forecasts within a day. (4) Based on the diagnostics, the obvious precipitation (amount and frequency) deviations of the model forecasts over Sichuan Basin are induced by the special coupling of the over prediction of southwesterly winds in the lower troposphere at the south-southeast Yunnan-Guizhou Plateau and terrain characteristics in the Basin and surrounding area.
Keywords:Complex terrain  Kilometer scale model  Rainfall amount and frequency  Forecast deviation
点击此处可从《气象学报》浏览原始摘要信息
点击此处可从《气象学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号