Adaptive bias correction for satellite data in a numerical weather prediction system

Thomas Auligné, A. P. McNally, D. P. Dee

Research output: Contribution to journalArticlepeer-review

322 Scopus citations

Abstract

Adaptive bias corrections for satellite radiances need to separate the observation bias from the systematic errors in the background in order to prevent the analysis from drifting towards its own climate. The variational bias correction scheme (VarBC) is a particular adaptive scheme that is embedded inside the assimilation system. VarBC is compared with an offline adaptive and a static bias correction scheme. In simulation, the three schemes are exposed to artificial shifts in the observations and the background. The VarBC scheme can be considered as a good compromise between the static and the offline adaptive schemes. It shows some skill in distinguishing between the background-error and the observation biases when other unbiased observations are available to anchor the system. Tests of VarBC in a real numerical weather prediction (NWP) environment show a significant reduction in the misfit with radiosonde observations (especially in the stratosphere) due to NWP model error. The scheme adapts to an instrument error with only minimal disruption to the analysis. In VarBC, the bias is constrained by the fit to observations - such as radiosondes - that are not bias-corrected to the NWP model. In parts of the atmosphere where no radiosonde observations are available, the radiosonde network still imposes an indirect constraint on the system, which can be enhanced by applying a mask to VarBC.

Original languageEnglish
Pages (from-to)631-642
Number of pages12
JournalQuarterly Journal of the Royal Meteorological Society
Volume133
Issue number624 PART A
DOIs
StatePublished - Apr 2007

Keywords

  • Bias estimation
  • Model bias
  • Observation bias
  • Radiance assimilation
  • Systematic error
  • Variational bias correction

Fingerprint

Dive into the research topics of 'Adaptive bias correction for satellite data in a numerical weather prediction system'. Together they form a unique fingerprint.

Cite this