Examination of Analysis and Forecast Errors of High-Resolution Assimilation, Bias Removal, and Digital Filter Initialization with an Ensemble Kalman Filter

Abstract

Abstract Mesoscale atmospheric data assimilation is becoming an integral part of numerical weather prediction. Modern computational resources now allow assimilation and subsequent forecasting experiments ranging from resolutions of tens of kilometers over regional domains to smaller grids that employ storm-scale assimilation. To assess the value of the high-resolution capabilities involved with assimilation and forecasting at different scales, analyses and forecasts must be carefully evaluated to understand 1) whether analysis benefits gained at finer scales persist into the forecast relative to downscaled runs begun from lower-resolution analyses, 2) how the positive analysis effects of bias removal evolve into the forecast, and 3) how digital filter initialization affects analyses and forecasts. This study applies a 36- and 4-km ensemble Kalman filter over 112 assimilation cycles to address these important issues, which could all be relevant to a variety of short-term, high-resolution, real-time forecasting applications. It is found that with regard to surface wind and temperature, analysis improvements gained at higher resolution persist throughout the 12-h forecast window relative to downscaled, high-resolution forecasts begun from analyses on the coarser grid. Aloft, however, no forecast improvements were found with the high-resolution analysis/forecast runs. Surface wind and temperature bias removal, while clearly improving surface analyses, degraded surface forecasts and showed little forecast influence aloft. Digital filter initialization degraded temperature analyses with or without bias removal, degraded wind analyses when bias removal was used, but improved wind analyses when bias removal was absent. No forecast improvements were found with digital filter initialization. The consequences of these results with regard to operational assimilation/forecasting systems on nested grids are discussed.