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Title: Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation

Journal Article · · Geophysical Research Letters

The intensity and spatial extent of tropical cyclone precipitation (TCP) often shapes the risk posed by landfalling storms. Here we provide a comprehensive climatology of landfalling TCP characteristics as a function of tropical cyclone strength, using daily precipitation station data and Atlantic U.S. landfalling tropical cyclone tracks from 1900 to 2017. We analyze the intensity and spatial extent of ≥1 mm/day TCP (Z1) and ≥50 mm/day TCP (Z50) over land. We show that the highest median intensity and largest median spatial extent of Z1 and Z50 occur for major hurricanes that have weakened to tropical storms, indicating greater flood risk despite weaker wind speeds. We also find some signs of TCP change in recent decades. In particular, for major hurricanes that have weakened to tropical storms, Z50 intensity has significantly increased, indicating possible increases in flood risk to coastal communities in more recent years.

Research Organization:
Georgia Institute of Technology, Atlanta, GA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; National Oceanic and Atmospheric Administration (NOAA)
Grant/Contract Number:
SC0019418
OSTI ID:
1803810
Journal Information:
Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 23 Vol. 46; ISSN 0094-8276
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English

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Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation dataset January 2019
Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation dataset January 2019
Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation dataset January 2019
Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation dataset January 2019