An FSO-based Optimization Framework for Improved Observation Performance: Theoretical Formulation and Experiments with NAVDAS-AR/NAVGEM

Abstract

Abstract The forecast sensitivity to observations (FSO) is embedded into a new optimization framework for improving the observation performance in atmospheric data assimilation. Key ingredients are introduced as follows: the innovation-weight parametrization of the analysis equation, the FSO-based evaluation of the forecast error gradient to parameters, a line search approach to optimization, and an efficient mechanism for step length specification. This methodology is tested in preliminary numerical experiments with the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) and the U.S. Navy’s Global Environmental Model (NAVGEM) at a T425L60 resolution. The experimental setup relies on a verification state produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the analysis and short-range forecast errors. Parameter tuning is implemented in a training stage valid for April 1-14 of 2018 and aimed at improving the use of assimilated observations in reducing the initial-condition errors. Assessment is carried out for April 15 – May 31 of 2018 to investigate the performance of the weighted assimilation system in reducing the errors in analyses and 24-hour model forecasts. In average, as compared with the control run and verified against the ECMWF analyses, the weighted approach provided 17.4% reduction in analysis errors and 3.1% reduction in 24-hour forecast errors, measured in a dry total energy norm. Observation impacts are calculated to assess the use of various observation types in reducing the analysis and forecast errors. In particular, assimilation of satellite wind data is significantly improved through the innovation-weighting procedure.