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Tropical Cyclone Rainfall Climatology, Extremes, and Flooding Potential from Remote Sensing and Reanalysis Datasets over the Continental United States
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Abstract
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002–19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV.
Significance Statement
Tropical cyclones (TCs) can produce extreme rainfall and widespread flooding. Improvements in our preparedness, mitigation, and adaptation efforts rest on a better understanding of TC rainfall and its impact. Assessment of various rainfall products and uncertainties is urgently needed for decision making and other applications. Different quantitative precipitation estimate (QPE) datasets have been developed over the past decades. This study uses 18 years of data to better understand how key properties of TC rainfall, including its contribution to extreme events and flooding potential, are represented across five widely used QPE products. Our analysis shows that satellite-based, and to a lesser extent, reanalysis-based QPEs underestimate TC rainfall properties both in terms of climatology and extreme events compared to ground-radar-gauge-based QPE. The uncertainty increases in the coastal region and is reflected in the estimated TC flooding potential.
Title: Tropical Cyclone Rainfall Climatology, Extremes, and Flooding Potential from Remote Sensing and Reanalysis Datasets over the Continental United States
Description:
Abstract
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding.
Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts.
QPEs based on different observing platforms (e.
g.
, satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared.
The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products.
We examine five datasets over an 18-yr period (2002–19).
The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5).
TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas.
Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events.
Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5.
Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region.
This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV.
Significance Statement
Tropical cyclones (TCs) can produce extreme rainfall and widespread flooding.
Improvements in our preparedness, mitigation, and adaptation efforts rest on a better understanding of TC rainfall and its impact.
Assessment of various rainfall products and uncertainties is urgently needed for decision making and other applications.
Different quantitative precipitation estimate (QPE) datasets have been developed over the past decades.
This study uses 18 years of data to better understand how key properties of TC rainfall, including its contribution to extreme events and flooding potential, are represented across five widely used QPE products.
Our analysis shows that satellite-based, and to a lesser extent, reanalysis-based QPEs underestimate TC rainfall properties both in terms of climatology and extreme events compared to ground-radar-gauge-based QPE.
The uncertainty increases in the coastal region and is reflected in the estimated TC flooding potential.
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