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Evaluation of Heavy Precipitation Forecasts Using Composite-Based Methods: A Distributions-Oriented Approach

Evaluation of Heavy Precipitation Forecasts Using Composite-Based Methods: A... Numerical forecasts of heavy warm-season precipitation events are verified using simple composite collection techniques. Various sampling methods and statistical measures are employed to evaluate the general characteristics of the precipitation forecasts. High natural variability is investigated in terms of its effects on the relevance of the resultant statistics. Natural variability decreases the ability of a verification scheme to discriminate between systematic and random error. The effects of natural variability can be mitigated by compositing multiple events with similar properties. However, considerable sample variance is inevitable because of the extreme diversity of mesoscale precipitation structures. The results indicate that forecasts of heavy precipitation were often correct in that heavy precipitation was observed relatively close to the predicted area. However, many heavy events were missed due in part to the poor prediction of convection. Targeted composites of the missed events indicate that a large percentage of the poor forecasts were dominated by convectively parameterized precipitation. Further results indicate that a systematic northward bias in the predicted precipitation maxima is related to the deficits in the prediction of subsynoptically forced convection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Evaluation of Heavy Precipitation Forecasts Using Composite-Based Methods: A Distributions-Oriented Approach

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References (19)

Publisher
American Meteorological Society
Copyright
Copyright © 2004 American Meteorological Society
ISSN
1520-0493
DOI
10.1175/MWR2975.1
Publisher site
See Article on Publisher Site

Abstract

Numerical forecasts of heavy warm-season precipitation events are verified using simple composite collection techniques. Various sampling methods and statistical measures are employed to evaluate the general characteristics of the precipitation forecasts. High natural variability is investigated in terms of its effects on the relevance of the resultant statistics. Natural variability decreases the ability of a verification scheme to discriminate between systematic and random error. The effects of natural variability can be mitigated by compositing multiple events with similar properties. However, considerable sample variance is inevitable because of the extreme diversity of mesoscale precipitation structures. The results indicate that forecasts of heavy precipitation were often correct in that heavy precipitation was observed relatively close to the predicted area. However, many heavy events were missed due in part to the poor prediction of convection. Targeted composites of the missed events indicate that a large percentage of the poor forecasts were dominated by convectively parameterized precipitation. Further results indicate that a systematic northward bias in the predicted precipitation maxima is related to the deficits in the prediction of subsynoptically forced convection.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Oct 14, 2004

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