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Assimilation of TOVS radiance information through one‐dimensional variational analysis
Author(s) -
Eyre J. R.,
Kelly G. A.,
McNally A. P.,
Andersson E.,
Persson A.
Publication year - 1993
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.49711951411
Subject(s) - numerical weather prediction , radiance , data assimilation , meteorology , depth sounding , covariance , environmental science , satellite , variational analysis , remote sensing , weather research and forecasting model , range (aeronautics) , weather forecasting , computer science , mathematics , geography , statistics , aerospace engineering , engineering , cartography
In recent years difficulties have been experienced in exploiting satellite sounding data in numerical weather prediction (NWP) in the form of independently retrieved temperature and humidity profiles. Attention has now focused on methods through which the information in the radiance measurements may be assimilated more directly into the NWP system%. A scheme known as ‘one‐dimensional variational analysis’ (1DVAR) has been developed at the European Centre for Medium‐range Weather Forecasts as a method for extracting information from TIROS Operational Vertical Sounder radiances for use in the operational data‐assimilation system. The 1DVAR scheme is based on variational principles applied to the analysis of the atmospheric profile at a single location, using a forecast profile and its error covariance as constraints. The details of the scheme are presented. Errors in 1DVAR products are correlated with those of the short‐range forecast which serves as a background for the subsequent three‐dimensional analysis. Methods for addressing this aspect of the assimilation problem are discussed. The characteristics of 1DVAR products and their impact on the analysis are described. A series of forecast impact experiments has been conducted and has demonstrated consistent positive impacts on forecast skill in the northern hemisphere.