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An Adaptive Approach For The Calculation of Ensemble Gridpoint Probabilities


Traditional ensemble probabilities are computed based on the number of members that exceed a threshold at a given point divided by the total number of members. This approach has been employed for many years in coarse-resolution models. However, convection-permitting ensembles of less than ~20 members are generally underdispersive, and spatial displacement at the gridpoint scale is often large. These issues have motivated the development of spatial filtering and neighborhood postprocessing methods, such as fractional coverage and neighborhood maximum value, which address this spatial uncertainty. Two different fractional coverage approaches for the generation of gridpoint probabilities were evaluated. The first method expands the traditional point probability calculation to cover a 100-km radius around a given point. The second method applies the idea that a uniform radius is not appropriate when there is strong agreement between members. In such cases, the traditional fractional coverage approach can reduce the probabilities for these potentially well-handled events. Therefore, a variable radius approach has been developed based upon ensemble agreement scale similarity criteria. In this method, the radius size ranges from 10 km for member forecasts that are in good agreement (e.g., lake-effect snow, orographic precipitation, very short-term forecasts, etc.) to 100 km when the members are more dissimilar. Results from the application of this adaptive technique for the calculation of point probabilities for precipitation forecasts are presented based upon several months of objective verification and subjective feedback from the 2017 Flash Flood and Intense Rainfall Experiment.

Article / Publication Data
Available Metadata
Accepted On
June 15, 2018
Fiscal Year
Peer Reviewed
Publication Name
Weather and Forecasting
Published On
August 01, 2018
Publisher Name
American Meteorological Society
Print Volume
Print Number
Page Range
Submitted On
February 28, 2018


Authors who have authored or contributed to this publication.