Details

Autor(en) / Beteiligte

• Ma, Zaizhong
• Liu, Bin
• Mehra, Avichal
• Abdolali, Ali
• van der Westhuysen, Andre
• Moghimi, Saeed
• Vinogradov, Sergey
• Zhang, Zhan
• Zhu, Lin
• Wu, Keqin
• Shrestha, Roshan
• Kumar, Anil
• Tallapragada, Vijay
• Kurkowski, Nicole

Titel

Investigating the Impact of High-Resolution Land–Sea Masks on Hurricane Forecasts in HWRF

Ist Teil von

• Atmosphere, 2020-09, Vol.11 (9), p.888

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Realistic wind information is critical for accurate forecasts of landfalling hurricanes. In order to provide more realistic near-surface wind forecasts of hurricanes over coastal regions, high-resolution land–sea masks are considered. As a leading hurricane modeling system, the National Centers for Environmental Prediction (NCEP) Hurricane Weather Research Forecast (HWRF) system has been widely used in both operational and research environments for studying hurricanes in different basins. In this study, high-resolution land–sea mask datasets are introduced to the nested domain of HWRF, for the first time, as an attempt to improve hurricane wind forecasts. Four destructive North Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018), which brought historic wind damage and storm surge along the Eastern Seaboard of the United States and Northeastern Gulf Coast, were selected to demonstrate the methodology of extending the capability to HWRF, due to the introduction of the high-resolution land–sea masks into the nested domains for the first time. A preliminary assessment of the numerical experiments with HWRF shows that the introduction of high-resolution land–sea masks into the nested domains produce significantly more accurate definitions of coastlines, land-use, and soil types. Furthermore, the high-resolution land–sea mask not only improves the quality of simulated wind information along the coast, but also improves the hurricane track, intensity, and storm-size predictions.