Variational Bias Correction of Polar-Orbiting Satellite Radiances in Convective-Scale Data Assimilation

Abstract

Maintenance of robust bias correction is a major challenge in the assimilation of meteorological polar-orbiting satellite data into limited-area Numerical Weather Prediction (NWP) systems. This article presents a variant of the variational bias correction algorithm suitable for use in convection-resolving systems. Stable bias correction requires continuous and representative sampling of predictor variables such as satellite view angles and air-mass properties. In convection-resolving NWP systems, the sampling is often compromised because of small computing domains, short assimilation time windows, and large diurnal variation in data availability.The proposed variant is designed around the assumption of one recurring daily analysis hour at which a given satellite provides comprehensive data coverage inside the computing domain. The idea is to allow the variational algorithm to adjust the bias correction coefficients at that analysis hour only, and otherwise keep the updated coefficients constant during the analysis. The time of the daily coefficient update is to be specified separately for each satellite, taking account of the satellite orbit parameters. The proposal is an alternative to the widely-adopted operational practice where independent streams of coefficients are maintained and updated separately at each analysis hour.The proposal is evaluated by data assimilation experiments in the context of a stateof-the-art Northern-European limited-area NWP system. In comparison with the operational setup, the proposed method is found to slightly improve the satellite radiance data fit to the NWP model background. Nevertheless, verification against independent data sources indicates no solid and statistically significant impact on forecast system performance.