Effects of observation operator nonlinearity on the assimilation of visible and infrared radiances in ensemble data assimilation

Author(s)
Lukas Kugler, Martin Weissmann
Abstract

Numerical weather prediction is becoming increasingly reliant on the assimilation of cloud-affected satellite observations. Their assimilation implicitly linearizes nonlinear observation operators in ensemble Kalman filters. The linearization causes the posterior to deviate from its linear approximation, which is often used for analysis verification. We investigate the linearization error for visible and infrared radiances in the ensemble adjustment Kalman filter (EAKF) using observing-system simulation experiments (OSSEs). We found that increments can be detrimental for small first-guess departures, but they are beneficial on average. The increments were typically about half of their linear approximation. Similarly, the ensemble spread reduction was smaller than its linear approximation, and sometimes negative (spread increase), but overall the mean nonlinear variance adjustment was consistent with the mean nonlinear squared error reduction. Lastly, the linear approximation overestimated the analysis mean absolute error (MAE) reduction by 37% for visible reflectance and 71% for infrared brightness temperature. Thus, the linear approximation of the posterior of observed variables, such as satellite radiances, should not be used for verification.

Organisation(s)
Department of Meteorology and Geophysics
Journal
Quarterly Journal of the Royal Meteorological Society
No. of pages
14
ISSN
0035-9009
DOI
https://doi.org/10.1002/qj.4970
Publication date
03-2025
Peer reviewed
Yes
Austrian Fields of Science 2012
105206 Meteorology
Keywords
ASJC Scopus subject areas
Atmospheric Science
Portal url
https://ucrisportal.univie.ac.at/en/publications/3a1d2fdb-5aae-4a75-8690-c9a4fcd1daa3