Time-Expanded Sampling for Ensemble-Based Data Assimilation Applied to Conventional and Satellite Observations

Abstract

he time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy?s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model?s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.