Mesoscale data assimilation for a local severe rainfall event with the NHM-LETKF system
Mesoscale data assimilation for a local severe rainfall event with the NHM-LETKF system
复制标题
NHM-LETKF系统对局地强降雨事件的中尺度数据同化
DOI：
10.1175/waf-d-13-00032.1
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
M
中科院分区：
文献类型：
--
作者：
Kunii;M
本研究旨在通过数据同化和集合预报方法，使用当地集合变换卡尔曼滤波器（LETKF）与日本气象厅的非静力模式（NHM）实施，以提高当地恶劣天气事件的预测。新开发的NHM-LETKF包含一个自适应膨胀方案和一个空间协方差定位方案与物理距离，它允许一个单向嵌套分析，其中一个更精细的分辨率LETKF进行使用外部模型的输出。这些新的功能增强了潜在的LETKF对流尺度的事件。将NHM-LETKF应用于2012年日本的一次局部强降雨事件。模式初步估计与分析结果的均方根误差比较表明，该系统对观测资料的同化是适当的。分析合奏传播表明，一个显着增加的时间周围的暴雨发生，这意味着环境领域的不确定性增加。用LETKF分析初始化的预报成功地捕捉到了强烈的暴雨，表明该系统可以有效地用于当地的恶劣天气事件。集合预报的概率预报的调查表明，这可能成为一个可靠的数据来源，在未来的决策。还测试了单向嵌套数据同化方案。结果表明，同化与更高分辨率的模式改善了当地恶劣天气条件的降水预报。
This study seeks to improve forecasts of local severe weather events through data assimilation and ensemble forecasting approaches using the local ensemble transform Kalman filter (LETKF) implemented with the Japan Meteorological Agency’s nonhydrostatic model (NHM). The newly developed NHM–LETKF contains an adaptive inflation scheme and a spatial covariance localization scheme with physical distance, and it permits a one-way nested analysis in which a finer-resolution LETKF is conducted by using the output of an outer model. These new features enhance the potential of the LETKF for convective-scale events. The NHM–LETKF was applied to a local severe rainfall event in Japan during 2012. Comparison of the root-mean-square errors between the model first guess and analysis showed that the system assimilated observations appropriately. Analysis ensemble spreads indicated a significant increase around the time torrential rainfall occurred, implying an increase in the uncertainty of environmental fields. Forecasts initialized with LETKF analyses successfully captured intense rainfalls, suggesting that the system could work effectively for local severe weather events. Investigation of probabilistic forecasts by ensemble forecasting indicated that this could become a reliable data source for decision making in the future. A one-way nested data assimilation scheme was also tested. The results demonstrated that assimilation with a finer-resolution model improved the precipitation forecasting of local severe weather conditions.