A weather pattern based approach to evaluate Antarctic Mesoscale Prediction System (AMPS) forecasts: Part 1. Synoptic weather patterns

A critical element in improving weather prediction models used for weather forecasting is a careful evaluation of the model forecasts, so that model errors can be identified and corrected. Typical model evaluation strategies evaluate the model over large periods of times (months, seasons, years, etc) or for single case study events of storms or other events of interest. Here we will discuss a new method of evaluating weather prediction models that assesses the model skill over long periods of time but subsets this time period into similar weather patterns, allowing us to determine if certain model errors occur under certain weather regimes and not others. The method of self-organizing maps is used to create a synoptic climatology of the weather patterns that occur in the Ross Sea sector of Antarctica. Using these weather patterns the model forecasts, from the Antarctic Mesoscale Prediction System (AMPS), that correspond to each weather pattern are identified and model errors are then determined for each weather pattern. In part 1 of this presentation we will discuss how the self-organizing map algorithm is applied to the AMPS output to create a synoptic climatology for the Ross Sea sector. Several methods of describing differences and errors in the model forecast synoptic weather patterns will be shown, including comparison of WRF and MM5 versions of AMPS, changes in synoptic climatology as a function of forecast duration, and comparison of forecast weather patterns with the model analysis weather patterns. The results of this type of analysis provide a different perspective on model performance and are useful for both the model developers and operational weather forecasters.