GRAPES模式预报误差订正的变分方法研究

何邓新; 周志敏; 康兆萍; 徐桂荣

doi:10.3969/j.issn.1004-9045.2020.04.009

GRAPES模式预报误差订正的变分方法研究

Variational method study to correct the forecast error of GRAPES model

摘要

摘要: 利用变分方法建立预报场和预报倾向场这一预报场组合与模式预报非系统性误差之间的映射关系，来估计GRAPES（Global/Regional Assimilation and PrEdiction System）模式的非系统性误差，从而对预报做出修正。采用两种不同的历史样本建立这一映射关系，其中，利用相同时刻历史样本建立映射关系的方法称为DEM方法；通过相似面积比选取“相似样本”来建立上述映射关系的方法称为SEM方法。以FNL分析资料作为评判预报误差的依据，根据2002—2010年7月GRAPES模式500 hPa高度场48 h预报的回报资料，利用两种不同的方案进行非系统性误差的估计及预报订正试验。对279个检验样本的试验结果表明：SEM方法和DEM方法都对非系统性误差有一定的估算能力，二者估算的非系统性误差空间分布和量级与模式非系统性误差较一致，SEM方法的修订效果略优于DEM方法，但并不明显。对预报做出系统性误差和非系统性误差两步订正后，DEM方法和SEM方法的订正有效率分别为98.566%和100%，可明显提高预报的准确性。

Abstract: In this study, forecast error is devived into two components, i.e., systematic forecast error and non-systematic forecast error. After the correction of systematic forecast error, variational method is used to estimate and correct the non-systematic forecast error by building the mapping relationship between the state variable and non-systematic forecast error. The mapping relationship can be built by two schemes. One is by using the historical samples of the same period (DEM), and the other is by using "similarity samples"(SEM). The hind-cast data of 48 h forecast of 500 hPa height field generated by GRAPES model in July from 2002 to 2010 is used to test the effectiveness of the above two methods. The analysis data from NCEP-FNL is used as validation data to assess the result of the methods. The result shows that the correction efficiency is 98.566% for DEM and 100% for SEM. Both the DEM method and SEM method has certain capability to esti-mate the non-systematic forecast error.

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