132 Precipitation Evaluation of the Real-Time Basin-Scale HWRF in 2017

Tuesday, 17 April 2018
Champions DEFGH (Sawgrass Marriott)
Mu-Chieh Ko, NOAA/AOML/HRD, Miami, FL; and F. D. Marks Jr., G. J. Alaka Jr., and S. G. Gopalakrishnan
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Handout (1.2 MB)

Precipitation is a critical consequence of a landfalling tropical cyclone (TC) that is often overshadowed by the wind threat. However, heavy rainfall associated with TCs can induce flooding, resulting in large losses to life, property, and the economy. In the 2017 Atlantic hurricane season, three major hurricanes made landfall in the U.S. and were associated with significant precipitation impacts: 1) Hurricane Harvey delivered record-breaking rainfall totals to Texas and Louisiana; 2) Hurricane Irma brought heavy precipitation to Florida, the forecast for which was complicated by track uncertainty; and 3) Hurricane Maria generated extreme precipitation in Puerto Rico, resulting in severe flooding and debris flows. With support from the Hurricane Forecast Improvement Project (HFIP), the NOAA/AOML/Hurricane Research Division (HRD) developed and maintained an experimental version of the Hurricane Weather Research and Forecasting (HWRF) Model, called “basin-scale HWRF” (HWRF-B), that produced one of the best track forecasts in 2017. Due to the high dependence of precipitation on TC track, HWRF-B was leveraged as a research tool to evaluate precipitation in Hurricanes Harvey, Irma, and Maria. HWRF-B precipitation was compared with observational data from NCEP hourly national multi-sensor (Stage IV) and the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation analyses to evaluate the accuracy of these model forecasts in terms of rainfall distribution, peak intensity, and precipitation structure. Probabilistic precipitation guidance will be developed using an ensemble of HWRF-B precipitation forecasts. Furthermore, this evaluation will assess the operational capability of HWRF-B precipitation forecasts, provide a strategy for future precipitation evaluation, and evaluate innovative probabilistic precipitation guidance.
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