Premium
An error analysis of radiance and suboptimal retrieval assimilation
Author(s) -
Joiner J.,
Dee D. P.
Publication year - 2000
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.49712656514
Subject(s) - radiance , data assimilation , computer science , depth sounding , remote sensing , assimilation (phonology) , satellite , numerical weather prediction , context (archaeology) , meteorology , variational analysis , environmental science , mathematical optimization , mathematics , geology , geography , linguistics , philosophy , aerospace engineering , engineering , paleontology , oceanography
One of the outstanding problems in data assimilation has been, and continues to be, how best to utilize satellite data while balancing the trade‐off between accuracy and computational cost. A number of weather‐prediction centres have recently achieved remarkable success in improving their forecast skill by changing the method in which satellite data are assimilated into the forecast model from the traditional approach of assimilating retrieved products to the direct assimilation of radiances in a variational framework. Although there are. clear theoretical advantages to the direct radiance‐assimilation approach, it is not obvious at all to what extent the improvements that have been obtained so far can be attributed to the change in methodology or to various technical aspects of the implementation. The central question we address here is: how much improvement can we expect from assimilating radiances rather than retrievals, all other things being equal? We compare the two approaches in a simplified theoretical framework. Direct radiance analysis is optimal in this idealized context, while the traditional method of assimilating retrievals is suboptimal because it ignores the cross‐covariances between background errors and retrieval errors. We show that interactive retrieval analysis (where the same background used for assimilation is also used in the retrieval step) is equivalent to direct assimilation of radiances with suboptimal analysis weights. We illustrate and extend these theoretical arguments with several one‐dimensional analysis experiments, where we estimate vertical atmospheric profiles using simulated data from temperature sounding channels of both the High‐resolution InfraRed Sounder 2 (HIRS2) and the future Atmospheric InfraRed Sounder (AIRS). In the case of non‐interactive retrievals the results depend very much on the quality of the background information used for the retrieval step. In all cases, the impact of the choice of analysis method is dwarfed by the effect of changing some of the experimental parameters that control the simulated error characteristics of the data and the retrieval background.