Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall
Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall
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基于对象的数据驱动和物理驱动极端降雨卫星估算的比较
DOI：
10.1175/jhm-d-20-0041.1
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发表时间：
2020
影响因子：
3.8
通讯作者：
Hartke, Samantha H.
中科院分区：
文献类型：
--
作者：
Li, Zhe;Wright, Daniel B.;Zhang, Sara Q.;Kirschbaum, Dalia B.;Hartke, Samantha H.
全球降水测量卫星传感器星座提供了各种降水过程的直接和间接测量。这种观测可以通过数据驱动的检索算法或通过同化到基于物理的数值天气模型中来获得空间和时间一致的网格化降水估计。我们比较了数据驱动的综合多卫星检索GPM（IMERG）和同化启用美国宇航局统一天气研究和预报（NU-WRF）模型对第四阶段参考降水的四个主要极端降雨事件在美国东南部使用基于对象的分析框架，分解成风暴对象的网格降水场。作为传统的“逐格分析”的替代方法，基于对象的方法提供了一种有前途的方法来诊断风暴的空间特性，通过空间和时间跟踪它们，并将其准确性与风暴类型和输入数据源联系起来。IMERG和NU-WRF通常捕获两个热带气旋的演变，而两个中尺度对流系统的组织性较差的空间模式对两者都构成了挑战。NU-WRF降雨率通常更准确，而IMERG更好地捕捉风暴的位置和形状。与较小的较弱风暴相比，两者在探测大的强烈风暴方面都表现出更高的技能。IMERG的准确性取决于输入的微波和红外数据源; NU-WRF似乎没有表现出这种依赖性。研究结果强调，面向对象的观点可以提供更深入的了解卫星降水的性能和卫星降水界应进一步探索“混合”的数据驱动和物理驱动的估计，以最佳利用卫星观测的潜力。
The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.