Satellite radiance assimilation using constrained bias correction

Dr. Wei Han Leader Data Assimilation Group Deputy Director Research and Development division Numerical Weather Prediction Centre Chinese Meteorological Administration Beijing, China

The success of satellite radiance data assimilation has been well established in Numerical Weather Prediction in this century and the use of variational bias correction (VarBC) schemes plays a crucial role in this. An unwanted side effect of variational bias correction may be in artificial model drifts that remain undetected. The Constrained Bias Correction (CBC) scheme for satellite radiances (Han 2014; Han 2015a; Han 2015b) constrains the size of the bias correction, using uncertainty information of calibration and radiative transfer model, in order to avoid the drift of model biases. Dr. Wei Han has successfully implemented the Constrained VarBC (CVarBC) in the ECMWF IFS for satellite radiance data assimilation by providing further constraints using potential available information, such as constraints on the size of the bias correction and innovative bias correction metrics, using uncertainty estimation from calibration and radiative transfer computations.This has been studied in the full ECMWF global 4D-Varsystem, using data from microwave sounders which are sensitive to stratospheric temperature and from ozone-sensitive infrared radiances from IASI, AIRS and CrIS. The constrained VarBC of AMSU-A stratospheric sounding channels reduces the biases in the stratosphere and improves the medium-range forecasts in both stratosphere and troposphere. The constrained VarBC of ozone channels reduces the bias and standard deviation of ozone analyses.