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The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program

The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the... The authors use a procedure called the method for object-based diagnostic evaluation, commonly referred to as MODE, to compare forecasts made from two models representing separate cores of the Weather Research and Forecasting (WRF) model during the 2005 National Severe Storms Laboratory and Storm Prediction Center Spring Program. Both models, the Advanced Research WRF (ARW) and the Nonhydrostatic Mesoscale Model (NMM), were run without a traditional cumulus parameterization scheme on horizontal grid lengths of 4 km (ARW) and 4.5 km (NMM). MODE was used to evaluate 1-h rainfall accumulation from 24-h forecasts valid at 0000 UTC on 32 days between 24 April and 4 June 2005. The primary variable used for evaluation was a ““total interest”” derived from a fuzzy-logic algorithm that compared several attributes of forecast and observed rain features such as separation distance and spatial orientation. The maximum value of the total interest obtained by comparing an object in one field with all objects in the comparison field was retained as the quality of matching for that object. The median of the distribution of all such maximum-interest values was selected as a metric of the overall forecast quality. Results from the 32 cases suggest that, overall, the configuration of the ARW model used during the 2005 Spring Program performed slightly better than the configuration of the NMM model. The primary manifestation of the differing levels of performance was fewer false alarms, forecast rain areas with no observed counterpart, in the ARW. However, it was noted that the performance varied considerably from day to day, with most days featuring indistinguishable performance. Thus, a small number of poor NMM forecasts produced the overall difference between the two models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program

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Publisher
American Meteorological Society
Copyright
Copyright © 2008 American Meteorological Society
ISSN
1520-0434
DOI
10.1175/2009WAF2222241.1
Publisher site
See Article on Publisher Site

Abstract

The authors use a procedure called the method for object-based diagnostic evaluation, commonly referred to as MODE, to compare forecasts made from two models representing separate cores of the Weather Research and Forecasting (WRF) model during the 2005 National Severe Storms Laboratory and Storm Prediction Center Spring Program. Both models, the Advanced Research WRF (ARW) and the Nonhydrostatic Mesoscale Model (NMM), were run without a traditional cumulus parameterization scheme on horizontal grid lengths of 4 km (ARW) and 4.5 km (NMM). MODE was used to evaluate 1-h rainfall accumulation from 24-h forecasts valid at 0000 UTC on 32 days between 24 April and 4 June 2005. The primary variable used for evaluation was a ““total interest”” derived from a fuzzy-logic algorithm that compared several attributes of forecast and observed rain features such as separation distance and spatial orientation. The maximum value of the total interest obtained by comparing an object in one field with all objects in the comparison field was retained as the quality of matching for that object. The median of the distribution of all such maximum-interest values was selected as a metric of the overall forecast quality. Results from the 32 cases suggest that, overall, the configuration of the ARW model used during the 2005 Spring Program performed slightly better than the configuration of the NMM model. The primary manifestation of the differing levels of performance was fewer false alarms, forecast rain areas with no observed counterpart, in the ARW. However, it was noted that the performance varied considerably from day to day, with most days featuring indistinguishable performance. Thus, a small number of poor NMM forecasts produced the overall difference between the two models.

Journal

Weather and Forecasting – American Meteorological Society

Published: Dec 8, 2008

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