Generation of Ensemble Mean Precipitation Forecasts from Convection-Allowing Ensembles

Generation of Ensemble Mean Precipitation Forecasts from Convection-Allowing Ensembles AbstractMethods for generating ensemble mean precipitation forecasts from convection-allowing model (CAM) ensembles based on a simple average of all members at each grid-point can have limited utility because of amplitude reduction and over-prediction of light precipitation areas caused by averaging complex spatial fields with strong gradients and high amplitude features. To combat these issues with the simple ensemble mean, a method known as probability matching is commonly used to replace the ensemble mean amounts with amounts sampled from the distribution of ensemble member forecasts, which results in a field that has a bias approximately equal to the average bias of the ensemble members. Thus, the probability matched mean (PM-mean, hereafter) is viewed as a better representation of the ensemble members compared to the mean, and previous studies find that it is more skillful than any of the individual members.Herein, using nearly a year of data from a CAM-based ensemble running in real-time at the National Severe Storms Laboratory, evidence is provided that the superior performance of the PM-mean is at least partially an artifact of the spatial redistribution of precipitation amounts that occur when the PM-mean is computed over a large domain. Specifically, the PM-mean enlarges big areas of heavy precipitation and shrinks or even eliminates smaller ones. An alternative approach for the PM-mean is developed that restricts the grid-points used to those within a specified radius-of-influence. The new approach has an improved spatial representation of precipitation and is found to perform more skillfully than the PM-mean at large scales when using neighborhood-based verification metrics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Generation of Ensemble Mean Precipitation Forecasts from Convection-Allowing Ensembles

Weather and Forecasting , Volume preprint (2017): 1 – Jun 29, 2017

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
D.O.I.
10.1175/WAF-D-16-0199.1
Publisher site
See Article on Publisher Site

Abstract

AbstractMethods for generating ensemble mean precipitation forecasts from convection-allowing model (CAM) ensembles based on a simple average of all members at each grid-point can have limited utility because of amplitude reduction and over-prediction of light precipitation areas caused by averaging complex spatial fields with strong gradients and high amplitude features. To combat these issues with the simple ensemble mean, a method known as probability matching is commonly used to replace the ensemble mean amounts with amounts sampled from the distribution of ensemble member forecasts, which results in a field that has a bias approximately equal to the average bias of the ensemble members. Thus, the probability matched mean (PM-mean, hereafter) is viewed as a better representation of the ensemble members compared to the mean, and previous studies find that it is more skillful than any of the individual members.Herein, using nearly a year of data from a CAM-based ensemble running in real-time at the National Severe Storms Laboratory, evidence is provided that the superior performance of the PM-mean is at least partially an artifact of the spatial redistribution of precipitation amounts that occur when the PM-mean is computed over a large domain. Specifically, the PM-mean enlarges big areas of heavy precipitation and shrinks or even eliminates smaller ones. An alternative approach for the PM-mean is developed that restricts the grid-points used to those within a specified radius-of-influence. The new approach has an improved spatial representation of precipitation and is found to perform more skillfully than the PM-mean at large scales when using neighborhood-based verification metrics.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Jun 29, 2017

References

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