| 降水资料同化在GRAPES-MESO模式中应用试验研究 |
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| 引用本文： | 王叶红,赖安伟,赵玉春,王明欢.降水资料同化在GRAPES-MESO模式中应用试验研究[J].大气科学,2013,37(3):645-667. |
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| 作者姓名： | 王叶红 赖安伟 赵玉春 王明欢 |
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| 作者单位： | 中国气象局武汉暴雨研究所 暴雨监测预警湖北省重点实验室,武汉 430074 |
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| 基金项目： | 公益性行业(气象)科研专项GYHY200806003、GYHY200906010、GYHY201206003,国家自然科学基金项目41075034、40975025、 41075038 |
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| 摘 要： | 利用国家气象中心中尺度业务数值预报模式GRAPES-MESO v3.0,以2010年6月1～30日为例,开展地面降水率1DVAR(one-dimensional variational assimilation)同化方案在GRAPES-3DVAR(three-dimensional variational assimilation)同化系统中的应用试验研究(ASSI试验),并以未加降水资料同化的试验为对照试验(CNTL试验),以评估全国1h加密雨量资料在模式中同化应用的效果。结果表明:1)在相对湿度背景误差和降水率观测误差范围内,1DVAR同化方案能够对湿度廓线进行有意义的调整,使分析降水向观测降水靠近；ASSI试验对初始温、压、湿、风场的修正主要为正效果；2)对2010年6月17～21日江南、华南连续性降水过程进行了分析,整体而言ASSI试验对逐日及逐时降水强度的预报普遍强于CNTL试验,与实况更加接近；3)ASSI试验对2010年6月1～30日08时起报的0～24 h模式预报的小雨、中雨、大雨、暴雨、大暴雨各个降水量级TS评分及ETS评分相比CNTL试验均有较明显提高,预报偏差也更接近于1；4)ASSI试验较CNTL试验能更好地模拟雨带的分布、雨带演变特征和降水强度的变化；5)对降水所做的典型个例和统计检验分析从不同角度说明了地面降水资料1DVAR同化方案在GRAPES-3DVAR系统中的应用改善了GRAPES-MESO v3.0的降水模拟效果。
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| 关 键 词： | 降水资料 1DVAR GRAPES-3DVAR 暴雨 |
| 收稿时间： | 2012/2/21 0:00:00 |
| 修稿时间： | 2012/11/22 0:00:00 |
Application of Precipitation Data Assimilationin the GRAPES-MESO Model |
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| WANG Yehong,LAI Anwei,ZHAO Yuchun and WANG Minghuan.Application of Precipitation Data Assimilationin the GRAPES-MESO Model[J].Chinese Journal of Atmospheric Sciences,2013,37(3):645-667. |
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| Authors: | WANG Yehong LAI Anwei ZHAO Yuchun and WANG Minghuan |
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| Institution: | Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430074 |
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| Abstract: | The mesoscale operational numerical model known as Global and Regional Assimilation and Prediction System GRAPES-MESO v3.0, from the National Meteorology Center of the China Meteorological Administration (CMA), was used to study the application of a one-dimensional variational (1DVAR) surface-precipitation assimilation scheme in the GRAPES-three-dimensional variational (3DVAR) data assimilation system (Expt ASSI) by using the experimental forecasts of the period June 1-30, 2010. The results are then compared with those of experiments without rainfall assimilation (Expt CNTL) to evaluate the application effects of assimilating 1 h intensive nationwide rainfall data into the GRAPES-3DVAR. The results are summarized in the following points: 1) 1DVAR precipitation assimilation can provide a meaningful modification for the moisture profiles by providing rainfall analysis results that are close to those determined through observation in the limits of moisture background errors and rainfall observational errors. The initial fields were obviously improved in Exp ASSI, in which the temperature, pressure, moisture, and wind values were modified to be closer to the observed values. 2) For the continuous precipitation process south of the lower reaches of the Yangtze River and in South China during June 17-21, 2010, the daily rainfall and hourly precipitation forecast of Exp ASSI was generally stronger than that of Exp CNTL and were closer to the observed values. 3) The threat score (TS) and equitable threat score (ETS) of 0-24 h rainfall forecasts at 0800 BT from Exp ASSI were better than those from Exp CNTL for rainfall levels of 1 mm, 10 mm, 25 mm, 50 mm, and 100 mm. Moreover, its forecasting bias is much closer to 1.0. The TS and ETS of 0-24 h precipitation were increased, and the forecast bias was decreased after assimilation of the 1 h accumulated precipitation in the GRAPES-3DVAR. 4) The distribution, evolution, and intensity variation of the rain region in Exp ASSI were better than those of Exp CNTL. 5) The rainfall ETS score for one month and the verification of typical heavy rain cases indicate that the assimilation of surface precipitation data in the GRAPES-3DVAR by using the 1DVAR precipitation scheme can improve the precipitation forecasts of GRAPES-MESO v3.0. |
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| Keywords: | Precipitation data 1DVAR GRAPES-3DVAR Heavy rain |
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