GPS PWV Assimilation with the JMA Nonhydrostatic 4DVAR and Cloud Resolving Ensemble Forecast for the 2008 August Tokyo Metropolitan Area Local Heavy Rainfalls
GPS PWV Assimilation with the JMA Nonhydrostatic 4DVAR and Cloud Resolving Ensemble Forecast for the 2008 August Tokyo Metropolitan Area Local Heavy Rainfalls
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DOI：
10.1007/978-3-319-43415-5_17
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Kazuo Saito;Y. Shoji;S. Origuchi;L. Duc
中科院分区：
文献类型：
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作者：
Kazuo Saito;Y. Shoji;S. Origuchi;L. Duc
2008年8月5日，东京都内多处发生了零星的局部暴雨，5名排水工人因水位突然上升而丧生。日本气象厅（JMA）的业务中尺度模式未能预测当地发生的大暴雨，这是由不稳定的大气条件下发展的深对流单体造成的，没有强烈的天气/地形强迫。利用JMA的中尺度业务分析和JMA全球一周集合预报系统的扰动作为初始条件，通过初始和侧边界扰动，进行了水平分辨率为10 km的11人中尺度集合预报，但对20 mm/3 h以上的强降水几乎没有预报。以10 km集合的6 h预报为初始和边界条件，进行了水平分辨率为2 km的降尺度集合预报试验。在一些集合成员中预测了分散的强降雨，但它们的位置和分布是不够的。总可降水量（PWV ）由GNSS观测 地球观测网络系统（GEONET） 日本地理空间信息管理局（Geospatial Information Authority of Japan）的Meso-4DVAR分析结果表明，JMA中尺度分析低估了东京地区的水汽含量。为了修正初始条件，利用日本气象厅的非静力四维变分同化方案进行了再分析资料同化试验 （JNOVA） 其中，从GEONET的PWV数据同化2.5天，3小时的数据同化周期。2公里的降尺度合奏运行从JNoVA分析正确地预测了分散的地方暴雨的地区。威胁分数和ROC区域技能分数表明，即使在集合预报中，初始条件的准确性对于数值预报小尺度对流性降雨也是至关重要的。分数技巧分数表明云分辨集合预报的价值，这种非强迫对流雨的情况。
On 5th August 2008, scattering local heavy rainfalls occurred at various places over the Tokyo metropolitan area, and five drainage workers were claimed by an abrupt increase of water level. The Japan Meteorological Agency (JMA) operational mesoscale model of the day failed to predict occurrence of the local heavy rainfalls, which were brought about by deep convective cells developed on the unstable atmospheric condition without strong synoptic/orographic forcings. A 11-member mesoscale ensemble prediction with a horizontal resolution of 10 km was conducted using the operational mesoscale analysis of JMA and perturbations of the JMA global one-week ensemble prediction system as the initial condition and the initial and lateral boundary perturbations, but the intense rains exceeding 20 mm/3 h were hardly predicted. A downscaling ensemble forecast experiment with a horizontal resolution of 2 km was conducted using the 6 h forecast of the 10 km ensemble as the initial and boundary conditions . Scattered intense rains were predicted in some ensemble members, but their locations and distribution were insufficient. The total precipitatable water vapor (PWV ) observed by the GNSS Earth Observation Network System (GEONET) of Geospatial Information Authority of Japan showed that the JMA mesoscale analysis given by the hydrostatic Meso-4DVAR underestimated water vapor over the Tokyo metropolitan area. To modify the initial condition, a reanalysis data assimilation experiment was conducted with the JMA’s nonhydrostatic 4DVAR (JNoVA) , where PWV data from GEONET were assimilated 2.5 days with 3-h data assimilation cycles. The 2 km downscale ensemble run from the JNoVA analysis properly predicted the areas of scattering local heavy rains. Threat scores and ROC area skill scores suggest that even in the ensemble prediction, accuracy of initial condition is critical to numerically predict small scale convective rains. Fractions skill scores indicated the value of the cloud resolving ensemble forecast for such the unforced convective rain case.