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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Assessment of a multidimensional satellite rainfall error model for ensemble generation of satellite rainfall data
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Assessment of a multidimensional satellite rainfall error model for ensemble generation of satellite rainfall data

机译：评估卫星降雨数据整体生成的多维卫星降雨误差模型

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This letter presents preliminary insights from the pursuit of the following scientific query: "How realistic is ensemble generation of satellite rainfall data by a multidimensional satellite rainfall error model?" The authors first evaluated the scale-dependent multidimensional error structure for two satellite rainfall algorithms developed at the NASA Goddard Space Flight Center, namely: 1) the infrared (IR) estimates known as the 3B41RT product and 2) the combined passive microwave (PMW) and IR estimates known as the 3B42RT product. Ground radar (WSR-88D) rainfall fields from the Southern Plains of the U.S. were used as reference. Next, by reversing the definition of reference and corrupted rain fields produced by a multidimensional satellite rainfall error model (SREM2D, developed by Hossain and Anagnostou), the authors derived the inverse multidimensional error structure of WSR-88D rainfall fields with respect to the satellite rainfall estimation algorithms. SREM2D was then applied on actual satellite rainfall data with the pertinent inverse error parameters to generate an ensemble of most likely realizations of the reference WSR-88D rainfall fields. The simulated ensemble was then compared with that derived from a simpler (bidimensional) inverse error modeling approach. The accuracy of the SREM2D rainfall ensemble was observed to be higher than the simpler error-modeling scheme for the 3B41RT product. No tangible improvement was observed for the 3B42RT product, which is attributed to the heterogeneous nature of 3B42RT data statistics that was not accounted for in the inverse SREM2D approach. The overall conclusion is that a multidimensional error modeling approach such as SREM2D has the potential to generate realistic ensembles of satellite rainfall fields, which should be considered as an improvement over the more widely used simpler error-modeling scheme. A combined use of the multidimensional error model with a sequential error correction scheme could therefore potentially improve the diagnosis of satellite rainfall-based predictability of the global water and energy cycle.
机译：这封信提出了以下科学查询的初步见解：“通过多维卫星降雨误差模型综合生成卫星降雨数据的现实程度如何？”作者首先评估了NASA戈达德太空飞行中心开发的两种卫星降雨算法的尺度相关多维误差结构，即：1）红外（IR）估计为3B41RT产品，以及2）组合无源微波（PMW）和IR估算值称为3B42RT产品。美国南部平原的地面雷达（WSR-88D）降雨场被用作参考。接下来，通过颠倒由多维卫星降雨误差模型（由Hossain和Anagnostou开发的SREM2D）产生的参考雨场和损坏的雨场的定义，作者推导了WSR-88D降雨场相对于卫星降水的逆多维误差结构。估计算法。然后将SREM2D与相关的反误差参数一起应用于实际的卫星降雨数据，以生成参考WSR-88D降雨场最可能实现的集合。然后将模拟的集合与从更简单的（二维）逆误差建模方法得出的集合进行比较。观察到SREM2D降雨系综的准确性高于3B41RT产品的简单误差建模方案。没有观察到3B42RT产品的明显改善，这归因于3B42RT数据统计数据的异质性，而SREM2D逆方法没有考虑到这一点。总的结论是，多维误差建模方法（例如SREM2D）有可能生成逼真的卫星降雨场集合，应将其视为对更广泛使用的简单误差建模方案的改进。因此，多维误差模型与顺序误差校正方案的组合使用可以潜在地改善对基于卫星降雨量的全球水和能源循环的可预测性的诊断。

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