摘要:为了把反映天气形势变化的背景误差协方差引入到变分分析系统中来提高分析质量，本文在GRAPES区域三维变分框架的基础上通过扩展控制变量方法实现动态与静态背景误差协方差耦合，建立混合三维变分分析系统（GRAPES Hybrid-3DVar）。通过控制变量扰动产生的集合样本进行单点观测分析试验验证Hybrid-3DVar及其局地化方案的合理性，并针对台风苏迪罗进行实际观测资料同化和数值预报试验，结果表明：用集合样本描述的背景误差协方差是随着天气流型变化的，动力场和质量场的离散度在台风中心处最大，因而混合同化的分析增量包含更多细微结构和中小尺度信息；其分析和24 h内预报要素质量优于3DVar，24 h内降水强度和落区预报也更准确，混合同化分析改善了3DVar分析的降水空报问题;同时混合同化分析的24 h内台风路径预报也最接近实况，台风强度预报在48 h之内都比3DVar更接近观测。
关键词:Hybrid-3DVar/
3DVar/
GRAPES/
局地化/
台风苏迪罗
Abstract:To improve the analysis quality by incorporating the flow-dependent ensemble covariance into the variational data assimilation system, the new GRAPES (global/regional assimilation and prediction system) hybrid-3Dvar system was built. The new system is based on the GRAPES regional 3DVar system, which uses the statistic covariance, and was built by augmenting the state vectors with another set of control variables preconditioned upon the ensemble dynamic covariance. The new hybrid-3DVar system and the localization method were verified through a single-observation assimilation experiment with ensemble samples produced by the 3D-Var’s control variable perturbation method. The real observation assimilation and forecast experiment for Typhoon Soudelor yielded the following conclusions: (1) The background covariance, which is represented by ensemble samples, is flow-dependent, and the root mean square spread in the ensemble of momentum field and mass field is largest near the typhoon center. (2) The analysis increments of the new hybrid-3DVar have a more detailed structure and more medium- and small-scale information. (3) The analysis and 24 h prediction qualities of model variables in the new hybrid-3DVar are significantly improved compared with the 3DVar system, and the precipitation position predictions are more accurate. (4) The 24 h forecast track of Typhoon Soudelor is closer to the observational one, and the 48 h-predicted intensity also approaches the real observation.
Key words:Hybrid-3DVar/
3DVar/
GRAPES/
Localization/
Typhoon Soudelor
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