| GRAPES全球模式的误差评估和订正 |
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| 引用本文： | 佟铃,彭新东,范广洲,常俊.GRAPES全球模式的误差评估和订正[J].大气科学,2017,41(2):333-344. |
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| 作者姓名： | 佟铃 彭新东 范广洲 常俊 |
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| 作者单位： | 1.成都信息工程大学大气科学学院高原大气与环境四川省重点实验室, 成都 610225 |
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| 基金项目： | 国家重点基础研究发展计划（973计划）项目2012CB417204，国家自然科学基金项目41175095、41575103，十二五科技支撑项目2012BAC22B01 |
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| 摘 要： | 以欧洲中期预报中心的ERA-interim再分析资料为参考，对GRAPES全球模式的数值预报结果误差进行了评估，并运用基于历史资料的模式距平积分订正（ANO）方法，对数值预报结果进行了订正试验，检验了ANO方法对GRAPES模式全球中期天气预报的订正改进效果。对1984~2014逐年7月15~24日10天的预报结果订正前后对比分析表明，ANO方法对不同区域位势高度、温度等要素预报订正效果明显，31个个例200 hPa位势高度一周预报距平相关系数平均提高0.05、均方根误差减少12 gpm。其它各层误差订正也显示类似结果，验证了ANO方法对提高GRAPES全球模式10天数值天气预报技巧的有效性，并与MOS（Model Output Statistics）方法对比，更便利、更经济，具有更好的可操作性以及业务预报应用能力。
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| 关 键 词： | 数值天气预报 GRAPES全球模式 误差订正 模式距平积分订正 (ANO) 方法 历史资料 |
| 收稿时间： | 2016/1/13 0:00:00 |
Error Evaluation and Correction for GRAPES Global Forecasts |
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| TONG Ling,PENG Xindong,FAN Guangzhou and CHANG Jun.Error Evaluation and Correction for GRAPES Global Forecasts[J].Chinese Journal of Atmospheric Sciences,2017,41(2):333-344. |
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| Authors: | TONG Ling PENG Xindong FAN Guangzhou and CHANG Jun |
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| Institution: | 1.Key Laboratory of Sichuan Province of Plateau Atmosphere and Environment & Institute of Atmosphere Science, Chengdu University of Information Technology, Chengdu 6102252.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 1000813.Sichuan Branch, China Meteorological Administration Training Centre, Chengdu 610027 |
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| Abstract: | Using the ERA-interim reanalysis data of ECMWF as the reference, numerical errors of the GRAPES global model are evaluated first. A numerical correction of the systematic model errors is then performed based on the historical observation data by using the Anomaly Numerical-correction with Observations (ANO) method. The effect of the ANO on the global forecast is tested by several case studies, and significant improvements on the global forecasting quality are confirmed. The ANO application in numerical results from 15 to 24 July during 1984-2014 shows significant positive effects in the circulation forecasts compared to those without the ANO application. For example, the potential height and temperature forecasts have been improved in various regions. Analysis of the geopotential height forecasts at 200 hPa shows that the anomalous correlation coefficient (ACC) increases by 0.05 and the root mean square error (RMSE) decreases by 12 gpm on average for all the 31 cases. Similar results can be found at other levels. The above results verify the validity of the ANO method in the improvement of 10-day numerical weather forecasting skill of the GRAPE global model. Compared with the MOS method, the ANO method is more efficient and maneuverable for application in the operational forecast. |
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| Keywords: | Numerical weather prediction GRAPES global model Error correction ANO (Anomaly Numerical-correction with Observations) method Historical data |
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