Understanding Model-based Probable Maximum Precipitation Estimation as a Function of Location and Season from Atmospheric Reanalysis

Understanding Model-based Probable Maximum Precipitation Estimation as a Function of Location and... AbstractExtreme precipitation events bring huge societal and economic loss around the world every year, and they have undergone spatially heterogeneous changes in the past half-century. They are fundamental to probable maximum precipitation (PMP) estimation in engineering practice, making it important to understand how extreme storm magnitudes are related to key meteorological conditions. However, there is currently a lack of information that can potentially inform the engineering profession on the controlling factors for PMP estimation. In this study, we present a statistical analysis of the relationship between extreme 3-day precipitation and atmospheric instability, moisture availability and large-scale convergence over the Contiguous US (CONUS). The analysis is conducted using the North America Regional Reanalysis (NARR) and ECMWF ERA-Interim reanalysis data, and a high-resolution regional climate simulation. While extreme 3-day precipitation events across the CONUS are mostly related to vertical velocity and moisture availability, those in the southwestern US mountain regions are also controlled by atmospheric instability. Vertical velocity and relative humidity have domain-wide impacts, while no significant relationship is found between extreme precipitation and air temperature. Such patterns are stable over different seasons and extreme precipitation events of various durations between 1 and 3 days. Our analyses can directly help in configuring the numerical models for PMP estimation at a given location for a given storm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Understanding Model-based Probable Maximum Precipitation Estimation as a Function of Location and Season from Atmospheric Reanalysis

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
D.O.I.
10.1175/JHM-D-17-0170.1
Publisher site
See Article on Publisher Site

Abstract

AbstractExtreme precipitation events bring huge societal and economic loss around the world every year, and they have undergone spatially heterogeneous changes in the past half-century. They are fundamental to probable maximum precipitation (PMP) estimation in engineering practice, making it important to understand how extreme storm magnitudes are related to key meteorological conditions. However, there is currently a lack of information that can potentially inform the engineering profession on the controlling factors for PMP estimation. In this study, we present a statistical analysis of the relationship between extreme 3-day precipitation and atmospheric instability, moisture availability and large-scale convergence over the Contiguous US (CONUS). The analysis is conducted using the North America Regional Reanalysis (NARR) and ECMWF ERA-Interim reanalysis data, and a high-resolution regional climate simulation. While extreme 3-day precipitation events across the CONUS are mostly related to vertical velocity and moisture availability, those in the southwestern US mountain regions are also controlled by atmospheric instability. Vertical velocity and relative humidity have domain-wide impacts, while no significant relationship is found between extreme precipitation and air temperature. Such patterns are stable over different seasons and extreme precipitation events of various durations between 1 and 3 days. Our analyses can directly help in configuring the numerical models for PMP estimation at a given location for a given storm.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Feb 1, 2018

References

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