基于WRF的高分辨率重庆2021年夏季气候降尺度预测试验  

WRF-based High-resolution Downscaling Climate Forecast Experiments of the Summer 2021 in Chongqing

作　　者：沈龙 闫会平 应武杉 唐红玉[2] 罗京佳 SHEN Long;YAN Huiping;YING Wushan;TANG Hongyu;LUO Jingjia(Institute for Climate and Application Research(ICAR),Nanjing University of Information Science and Technology,Nanjing 210044,China;Chongqing Climate Centre,Chongqing 401147,China)

机构地区：[1]南京信息工程大学气候与应用前沿研究院,南京210044 [2]重庆市气候中心,重庆401147

出　　处：《高原山地气象研究》2024年第4期63-73,共11页Plateau and Mountain Meteorology Research

基　　金：国家自然科学基金项目(42030605)。

摘　　要：本文利用WRF模式对全球气候预测系统1.0版(NUIST-CFS1.0)于3月1日起报的重庆2021年夏季(6—8月)预测结果进行高分辨率(5 km×5 km)动力降尺度预测研究,并对比分析了2种陆面过程(Noah和RUC)参数化方案对降尺度预测结果的影响。结果表明:WRF动力降尺度可以改善NUIST-CFS1.0对重庆夏季2 m气温和降水的预测结果。随着区域模式水平分辨率的提升,WRF动力降尺度可以预测出更多的2 m气温空间分布细节,但对降水预测的改善十分有限。从陆面方案的影响来看,RUC方案预测的降水量较Noah方案更加接近观测,Noah方案预测的潜热通量较小而感热通量较大,有利于改善NUIST-CFS1.0对重庆夏季2 m气温低估的偏差。从降水的预测效果来看,WRF动力降尺度对不同量级降水概率预测均优于NUIST-CFS1.0的结果,不仅能捕捉到更多的重庆夏季大雨(>25 mm·d^(-1))事件,从而减小NUIST-CFS1.0的降水负偏差,还能更加合理地捕捉到重庆“夜雨”的特征。相较于NUIST-CFS1.0,WRF动力降尺度甚至在重庆地区低空环流的日变化以及水汽输送特征方面有较好的表现,这与WRF对降水的预测效果相对应。The WRF model is used to conduct the high-resolution(5 km×5 km)dynamical downscaling prediction experiments for the climate in Chongqing in the summer(June-August)of 2021 based on the forecast of Global Climate Prediction System 1.0(NUIST-CFS1.0)initialized from March 1,2021.The effects of two land surface parameterization schemes(Noah and RUC)on the downscaling seasonal prediction results are compared and analyzed.The results show that WRF dynamical downscaling can improve the predictions of 2-meter air temperature and precipitation in Chongqing during summer.With the increased horizontal resolution,WRF dynamic downscal-ing can provide more detailed spatial distribution of 2-meter air temperature,but the improvement in precipitation prediction remains lim-ited.From the impact of the land surface scheme,the RUC scheme demonstrates better agreement with observations in precipitation pre-diction compared to the Noah scheme.However,the Noah scheme predicts a relatively small latent heat flux and a relatively large sens-ible heat flux,which contributes to alleviating the underestimation bias of 2-meter air temperature in the original NUIST-CFS1.0 forecast.From the prediction effect of precipitation,WRF dynamic downscaling is superior to NUIST-CFS1.0 in predicting the probability of pre-cipitation of different magnitudes.It can not only capture more heavy rain events(>25 mmday^(-1))in Chongqing in summer,thus reducing the negative deviation of precipitation in NUIST-CFS1.0,but also capture the characteristics of night rain in Chongqing more reasonably.Further analysis demonstrates that WRF dynamical downscaling exhibits superior performance in capturing the diurnal cycle characterist-ics of low-level circulation and water vapor transport over Chongqing compared with NUIST-CFS1.0,which corresponds to the prediction effect of WRF on precipitation.

关 键 词：WRF模式 动力降尺度 陆面过程参数化方案 区域气候预测 

分 类 号：P456.7[天文地球—大气科学及气象学]