Abstract
A Bayesian method for estimating wind and rain in hurricanes from SeaWinds at ultra-high resolution is developed. We use a hurricane model to generate prior distributions for the wind speed, wind direction, and rain rate. The rain prior is derived from data from the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM-PR). The new method reduces the variability of the standard simultaneous wind and rain estimates while preserving meso-scale detail.
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