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Uncertainty analysis using the WRF maximum likelihood ensemble filter system and comparison with dropwindsonde observations in Typhoon Sinlaku (2008)

机译:使用WRF最大似然集合滤波器系统进行不确定性分析,并与台风“新乐库”(2008年)中的顺风探空仪观测结果进行比较

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摘要

In this study, the maximum likelihood ensemble filter (MLEF) is applied to a tropical cyclone case to identify the uncertainty areas in the context of targeting observations, using the WRF model. Typhoon Sinlaku (2008), from which dropwindsonde data are collected through THORPEX Pacific Asian Regional Campaign (TPARC), is selected for the case study. For the uncertainty analysis, a measurement called the deep layer mean (DLM) wind variance is employed. With assimilation of conventional rawinsonde data, the MLEF-WRF system demonstrated stable data assimilation performance over multiple data assimilation cycles and produced high uncertainties mostly in data-void areas, for the given tropical cyclone case. Dropwindsonde deployment through T-PARC turned out to occur inside or near the weak uncertainty areas that are identified through the MLEF-WRF system. The uncertainty analysis using the MLEF method can provide a guide for identifying more effective targeting observation areas.
机译:在这项研究中,将最大似然集合滤波器(MLEF)应用于热带气旋案例,以使用WRF模型在目标观测的背景下识别不确定区域。案例研究选择了台风Sinlaku(2008),通过THORPEX亚太地区战役(TPARC)收集了暴风雪数据。对于不确定性分析,采用了一种称为深层平均风(DLM)风方差的测量方法。对于常规的原始信噪比数据,MLEF-WRF系统在多个数据同化周期内显示出稳定的数据同化性能,对于给定的热带气旋情况,主要在数据缺乏的地区产生了高度不确定性。通过T-PARC进行的落风探空机部署最终发生在通过MLEF-WRF系统确定的弱不确定性区域内或附近。使用MLEF方法进行的不确定性分析可以为确定更有效的目标观察区域提供指导。

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