Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter

Abstract This study uses the local ensemble transform Kalman filter to assimilate Atmospheric Infrared Sounder (AIRS) specific humidity retrievals with pseudo relative humidity (pseudo-RH) as the observation variable. Three approaches are tested: (i) updating specific humidity with observations other than specific humidity (“passive q”), (ii) updating specific humidity only with humidity observations (“univariate q”), and (iii) assimilating the humidity and the other observations together (“multivariate q”). This is the first time that the performance of the univariate and multivariate assimilation of q is compared within an ensemble Kalman filter framework. The results show that updating the humidity analyses by either AIRS specific humidity retrievals or nonhumidity observations improves both the humidity and wind analyses. The improvement with the multivariate-q experiment is by far the largest for all dynamical variables at both analysis and forecast time, indicating that the interaction between the s...

[1]  Junjie Liu,et al.  Applications of the LETKF to adaptive observations, analysis sensitivity, observation impact and the assimilation of moisture , 2007 .

[2]  Dick Dee,et al.  On the choice of variable for atmospheric moisture analysis , 2022 .

[3]  Takemasa Miyoshi,et al.  Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution , 2007 .

[4]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[5]  Jeffrey L. Anderson.  An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[6]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[7]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[8]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[9]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[10]  Christopher D. Barnet,et al.  Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  Thomas M. Hamill,et al.  Ensemble Data Assimilation with the NCEP Global Forecast System , 2008 .

[12]  Istvan Szunyogh,et al.  A local ensemble transform Kalman filter data assimilation system for the NCEP global model , 2008 .

[13]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[14]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[15]  Istvan Szunyogh,et al.  A Local Ensemble Kalman Filter for Atmospheric Data Assimilation , 2002 .

[16]  E. Kalnay,et al.  Four-dimensional ensemble Kalman filtering , 2004 .