An Object-based Method for Quantifying Errors in Meteorolgical Models
C. A. Davis, B. G. Brown, R. Bullock, M. Chapman, K. Manning, and A. Takacs
National Center for Atmospheric Research
P.O. Box 3000
Boulder, CO 80307 USA
Measures-based characterizations of errors in forecasts fail to provide useful information as increasingly complex spatial structures become evident in numerical weather forecasts. The perceived utility of such forecasts often lies in their ability to predict localized and episodic events. Subtle forecast timing and location errors yield low skill scores by traditional measures because the phenomena of interest (e.g. precipitation, turbulence, icing) contain large spatial gradients. Yet, the occurrence of such features in forecasts can provide forecasters with important clues to the possible occurrence of important weather events. An alternative verification strategy is to decompose numerical forecasts into objects whose position and attributes can be objectively compared between models and observations. We describe a recently developed method for defining rain areas for the purpose of verifying precipitation produced by numerical weather prediction models. Objects are defined in both forecasts and observations based on a convolution (smoothing) and thresholding procedure. Nearby objects are merged according to a rule set involving separation distance and orientation. Objects in the two datasets are matched and a statistical analysis of matched pairs is performed. In addition, the raw rainfall values within each object are retained and the distribution of intensities is analyzed as another object attribute. Extension of this method to a wide variety of environmental forecast verification problems is also discussed. This approach allows us to more appropriately assess the spatial accuracy of these types of forecasts than previously used methodologies.
In: McRoberts, R. et al. (eds). Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.