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Probabilistic high‐impact rainfall forecasts from landfalling tropical cyclones using Warn‐on‐Forecast system
Author(s) -
Yussouf Nusrat,
Jones Thomas A.,
Skinner Patrick S.
Publication year - 2020
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3779
Subject(s) - environmental science , flash flood , meteorology , probabilistic logic , flooding (psychology) , climatology , severe weather , storm , tropical cyclone , scale (ratio) , computer science , geography , cartography , geology , psychology , archaeology , artificial intelligence , psychotherapist , flood myth
Abstract Intense rainfall and flash flooding from landfalling tropical cyclones (LTC) can have devastating impacts on human life and property in coastal areas. The years 2017 and 2018 are examples of how the North Atlantic LTCs can create widespread destruction in the United States. Better preparedness is needed to mitigate the impact from the violent LTCs and can be achieved by improving the accuracy of forecasts and increased lead‐time of guidance products. However, predicting the fine‐scale details of rain bands in LTC is very challenging. This study attempts to elucidate the potential of National Severe Storms Laboratory's convective‐scale ensemble analysis and prediction system, known as the Warn‐on‐Forecast System (WoFS), in improving 0–6 hr probabilistic intense rainfall forecasts from three recent LTCs in the United States. Results indicate that the frequent 15 min assimilation cycling can accurately analyse the small‐scale details from the LTC rain bands in the WoFS analyses. The WoFS 0–6 hr ensemble forecasts initialized from those analyses represent the location, intensity and spatial distribution of intense rainfall (with the potential to cause flash flooding) as well as low‐level rotation with reasonably good accuracy. The continuous flow of the frequently updated WoFS rainfall guidance has the potential to aid operational forecasters in issuing watches, warnings, and short‐term forecast products of life‐threatening LTC with higher spatial and temporal specificity.