Enhanced radiance bias correction in the National Centers for Environmental Prediction's Gridpoint Statistical Interpolation data assimilation system

Abstract

Radiance bias correction is an important and necessary step in the proper use of satellite observations in a data assimilation system. The original radiance bias‐correction scheme used in the Gridpoint Statistical Interpolation (GSI) data assimilation system consists of two components: a variational air‐mass dependent component and a scan‐angle component. The air‐mass component is updated within the GSI, while the scan‐angle component is updated outside the GSI. This study examines and enhances several aspects of the radiance bias‐correction problem. First, a modified pre‐conditioning is applied to the bias‐correction coefficients and the analysis variables to speed up convergence of the minimization process. A new procedure for applying the modified pre‐conditioning in the GSI is utilized. Second, capabilities for detecting any new/missing/recovering radiance data and initializing the bias correction for new radiance data are implemented. A new scheme is proposed and employed to adjust the background‐error variances for the bias‐correction coefficients automatically, using an approximation of the analysis‐error variances from the previous cycle, and to remove the pre‐specified predictor scaling parameters. Finally, the capability to perform bias correction for passive channels within the GSI is developed with a new approach. The two‐step bias‐correction procedure originally used is replaced with a one‐step variational bias‐correction scheme within the GSI. Experiment results with the GSI‐based hybrid ensemble‐variational system show that using the modified pre‐conditioning leads to a better convergence rate. Moreover, with the one‐step scheme, the anomaly correlation of geopotential height at 500 mb is neutral in the Northern Hemisphere but improved in the Southern Hemisphere. The root‐mean‐square (RMS) error of wind is comparable to that of the two‐step scheme and the biases of the global temperature 24 h and 48 h forecasts fitted to the rawinsonde are reduced.

An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar and Hybrid Variants

“…While most of the methods in these previous studies rely exclusively on an ensemble-based error covariance, several recent studies did combine the ensemble covariances with time-invariant static covariances in their 4DEnVar (i.e., hybrid 4DEnVar; Buehner et al 2013;Desroziers et al 2014;Lorenc et al 2015;Wang and Lei 2014). Formulating the problem in the variational framework allows one to take full advantage of the many developments that have taken place over the years, such as dynamic constraints (Gauthier and Thépaut 2001;Kleist et al 2009) and variational bias correction (Derber and Wu 1998;Dee 2005;Zhu et al 2014).…”

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

“…This number of predictors exceeds that in radiation correction schemes, where six to eight predictors are typically used (e.g., Zhu et al, 2014).…”

Impact of assimilating GOES imager clear‐sky radiance with a rapid refresh assimilation system for convection‐permitting forecast over Mexico

“…It provides 90–95% of the actively assimilated data [ Bauer et al , ]. As the development of fast radiative transfer models and their adjoint models, a number of clear‐sky or all‐sky infrared and microwave channel radiance observations from satellite instruments have been assimilated directly into most operational centers, such as the National Centers for Environmental Prediction (NCEP), European Centre for Medium‐Range Weather Forecasts (ECMWF), Met Office, Japan Meteorological Agency, Météo‐France, and Environment Canada [ Greenwald et al , ; Heilliette and Garand , ; Pavelin et al , ; McNally , ; Pangaud et al , ; Heilliette , ; Bauer et al , ; Geer et al , ; Geer and Bauer , ; Guidard et al , ; Lupu and McNally , ; Okamoto , ; Zhu et al , ; Kazumori , ; Yang et al , ]. These infrared and microwave instruments are carried on different geostationary and polar‐orbiting satellites.…”