Application of Principal Component Algorithm to the Direct Assimilation of AIRS in the WRF 4D-Var System

Abstract

The use of Principal Component (PC) algorithm is explored for the efficient representation observations from high-resolution infrared sounders for the purposes of data assimilation into numerical weather prediction (NWP) models. A new version of the fast radiative transfer model has been developed that exploits principal component analysis and then implemented into the WRF 4D-Var data assimilation system, thus allow the investigation of the direct assimilation of PC scores from Atmospheric Infrared Sounder (AIRS). Testing of a prototype system where 119 AIRS spectra replaced by only 20 PC scores show significant computational saving with no detectable loss of skill in the resulting analyses or forecasts. The methodologies implemented in this regard are examined and the potential for future increased use of the data are explored.