许晨璐,王建捷,黄丽萍. 2017. 千米尺度分辨率下GRAPES-Meso4.0模式定量降水预报性能评估[J]. 气象学报, 75(6):851-876, doi:10.11676/qxxb2017.068
千米尺度分辨率下GRAPES-Meso4.0模式定量降水预报性能评估
Evaluation on QPF of GRAPES-Meso4.0 model at convection-permitting resolution
投稿时间:2017-03-17  修订日期:2017-06-19
DOI:10.11676/qxxb2017.068
中文关键词:  千米尺度分辨率  GRAPES-MesoV4.0  预报降水评估
英文关键词:Convection-permitting resolution  GRAPES-MesoV4.0  Evaluation of predicted precipitation
基金项目:国家重点研究发展计划973项目(2013CB430106)。
作者单位E-mail
许晨璐 中国气象科学研究院, 北京, 100081  
王建捷 国家气象中心, 北京, 100081 wangjj@cma.gov.cn 
黄丽萍 国家气象中心, 北京, 100081  
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中文摘要:
      应用覆盖中国东南部的3 km分辨率GRAPES-Meso4.0模式(GRAPES-Meso4.0_3 km)2015年夏季实时预报试验结果、同区域1600多个中国国家地面气象观测站每日08:00-08:00 BT的24 h累积降水量和逐时降水量观测资料,从降水累积量、降水频率、降水强度、日循环特征等多个角度,对千米尺度分辨率下GRAPES-Meso4.0模式的降水预报性能进行细致评估,并与同版本10 km分辨率业务模式(GRAPES-Meso4.0_10 km)同期结果在相同区域进行类同对比分析和讨论。结果表明:(1) GRAPES-Meso4.0_3 km很好地捕捉到了2015年夏季观测的日均降水量和降水频率的大小及地域分布特征,其一般性降水(中雨及以下)频率平均低于实况约3个百分点,强降水(大雨及以上量级)频率与实况近乎吻合,纠正了GRAPES-Meso4.0_10 km在这两方面存在的显著预报正偏差,均方根误差(RMSE)减小了40%-50%;(2) GRAPES-Meso4.0_3 km在降水强度预报上的优势主要表现为:对降水强度的地域分布细致特征和对短时强降水(雨强 ≥ 10 mm/h)的频数和分布等把握比较准确,但对强降水(一般性降水)的强度预报偏强(偏弱);(3) GRAPES-Meso4.0_3 km小时降水量和降水频率的日循环预报可反映出研究区域观测的双峰总体特征以及雨量和频率在日循环中的紧密联系,明显优于GRAPES-Meso4.0_10 km的表现,尽管下午(16时,北京时)峰的预报还存在偏弱现象;(4)模式分辨率提高到千米尺度和模式显式描述云和降水过程,是GRAPES-Meso4.0_3 km降水预报性能较GRAPES-Meso4.0_10 km提高的关键原因,模式初值差异也是不可忽视的影响因素。
英文摘要:
      The quantitative precipitation forecast (QPF) of GRAPES-Meso4.0 model at convection-permitting resolution is evaluated thoroughly in terms of precipitation accumulation, frequency, intensity, and diurnal cycle. Real-time QPF products of the GRAPES-Meso4.0 experimental system at 3 km horizontal resolution covering southeastern China (GRAPES-Meso4.0_3 km) are verified against the observations of 24 h (08:00-08:00 BT) accumulated precipitation and hourly precipitation from 1613 stations of the National Meteorological Surface Observation Network during the summer of 2015. The QPF products over the same period and region from the operational model of the same version but at 10 km resolution (GRAPES-Meso4.0_10 km) are also used to compare and diagnose the forecast biases. Results of this study show that: (1) GRAPES-Meso4.0_3 km perfectly captured the characteristics of the total amount and spatial distribution of observed daily mean precipitation and precipitation frequency. The mean general precipitation (moderate rain and below) frequency was about 3% lower than the observation, and the precipitation frequency of heavier ones (heavy rain and above) coincided with the observation, indicating that the high resolution model can significantly correct the positive forecasting deviation of GRAPES-Meso4.0_10 km in both types of precipitation. The root mean square error (RMSE) was reduced by 40%-50%. (2) The advantage of GRAPES-Meso4.0_3 km in precipitation intensity simulation was mainly manifested in the detailed description of spatial distribution characteristics of precipitation intensity as well as the frequency and distribution of short-time heavy rainfall (precipitation intensity≥10 mm/h). However, the predicted heavy rainfall (general precipitation) intensity was stronger (weaker) than observations. (3) Diurnal cycle of hourly precipitation and precipitation frequency predicted by GRAPES-Meso4.0_3 km could reflect the observed general bimodal characteristic in the study area and the close relationship between the diurnal cycles of precipitation amount and frequency. GRAPES-Meso4.0_3 km performed better than GRAPES-Meso4.0_10 km despite the weaker peak in the afternoon (16:00 BT). (4) The model resolution is increased to convection-permitting to explicitly describe cloud and precipitation process, which is a key reason for the improvement of QPF by GRAPES-Meso4.0_3 km compared to GRAPES-Meso4.0_10 km, and the difference in the initial field of models is also a factor that cannot be ignored.
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