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This content will become publicly available on May 1, 2023

Title: Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model
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.
Authors:
; ; ; ; ;
Award ID(s):
2004658
Publication Date:
NSF-PAR ID:
10336161
Journal Name:
Remote Sensing
Volume:
14
Issue:
9
Page Range or eLocation-ID:
2118
ISSN:
2072-4292
Sponsoring Org:
National Science Foundation
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