Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter
Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter
复制标题
使用 1000 成员集成卡尔曼滤波器预测 2020 年 7 月九州大雨
作者：
L. Duc;T. Kawabata;Kazuo Saito;T. Oizumi
2020年7月九州暴雨的预报性能已经重新审查，旨在改善对该事件的预报。虽然日本气象厅（JMA）的确定性预报相对较好，但JMA的集合预报不知何故错过了这一事件。我们的方法是通过运行1000个成员的本地集合变换卡尔曼滤波器（LETKF 1000），从观测中提取更多的信息，并更好地量化预测的不确定性，引入流依赖同化。为了节省计算成本，在运行LETKF 1000时删除了垂直定位。定性和定量检验表明，LETKF 1000预报在确定性和概率性预报方面均优于业务预报。而不是一个技巧，以节省计算成本，去除垂直本地化的主要贡献LETKF 1000。如果去除垂直局部化，则可以用100个集合成员获得具有相似性能的预报。我们假设，运行集合卡尔曼滤波器与大约1000个集合成员是更有效的，如果垂直定位在同一时间被删除。由于本研究只研究了一种情况，为了评估当集合成员的数量在1000左右时严格去除垂直本地化的好处，将来需要考虑更大的一组情况。（引用：Duc，L.，T. Kawabata，K. Saito和T. Oizumi，2021：使用1000个集合卡尔曼滤波器预测2020年7月九州暴雨。SOLA，17，41−47，doi：10.2151/ sola.2021-007.）
Forecast performances of the July 2020 Kyushu heavy rain have been revisited with the aim of improving the forecasts for this event. While the Japan Meteorological Agency’s (JMA) deterministic forecasts were relatively good, the JMA’s ensemble forecasts somehow missed this event. Our approach is to introduce flow-dependence into assimilation by running a 1000-member local ensemble transform Kalman filter (LETKF1000) to extract more information from observations and to better quantify forecast uncertainties. To save computational costs, vertical localization is removed in running LETKF1000. Qualitative and quantitative verifications show that the LETKF1000 forecasts outperform the operational forecasts both in deterministic and probabilistic forecasts. Rather than a trick to save computational costs, removal of vertical localization is shown to be the main contribution to the outperformance of LETKF1000. If vertical localization is removed, forecasts with similar performances can be obtained with 100 ensemble members. We hypothesize that running ensemble Kalman filters with around 1000 ensemble members is more effective if vertical localization is removed at the same time. Since this study examines only one case, to assess benefit of removing vertical localization rigorously when the number of ensemble members is around 1000, a larger set of cases needs to be considered in future. (Citation: Duc, L., T. Kawabata, K. Saito, and T. Oizumi, 2021: Forecasts of the July 2020 Kyushu heavy rain using a 1000member ensemble Kalman filter. SOLA, 17, 41−47, doi:10.2151/ sola.2021-007.)
利用集合卡尔曼滤波器和海温不确定性改进热带气旋预报
DOI：
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发表时间：
2014
期刊：
影响因子：
--
作者：
Kunii;M.;T. Miyoshi;and A. Wada
通讯作者：
and A. Wada
影响因子：
1.9
作者：
Hirockawa, Yasutaka;Kato, Teruyuki;Mashiko, Wataru
通讯作者：
Mashiko, Wataru