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

AbstractMesoscale 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 forecast...

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