Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model

Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model
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DOI:
10.3390/rs14092118
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发表时间:
2022-04
期刊:
Remote. Sens.
影响因子:
--
通讯作者:
Z. Pu;Ying Wang;Xin Li;C. Ruf;Li Bi;A. Mehra
Z. Pu;Ying Wang;Xin Li;C. Ruf;Li Bi;A. Mehra
中科院分区:
其他
文献类型:
--
作者:
Z. Pu;Ying Wang;Xin Li;C. Ruf;Li Bi;A. Mehra

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本研究探讨了同化来自美国宇航局气旋全球导航卫星系统(CYGNSS)的海洋表面风的影响,提高短期数值模拟和预测的登陆飓风使用NCEP业务飓风天气研究和预报(HWRF)模型。利用HWRF和基于网格点统计插值(GSI)的混合三维集合变分(3DEnVar)数据同化系统进行了一系列同化试验。CYGNSS数据对飓风预报的影响进行了比较与先进的散射计(ASCAT)风产品已经被同化到HWRF预报系统中的一系列同化实验。评估了不同版本的CYGNSS数据(V2.1与V3.0)对飓风预报的影响。结果表明,CYGNSS海面风场可以改善飓风路径和强度、非对称风结构和降水的数值模拟和预报。CYGNSS对飓风预报的影响与ASCAT卫星数据产品的实际使用具有可比性和互补性。不同版本的CYGNSS数据的相对影响对最佳稀疏距离的依赖性是显而易见的。
This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident.