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As assessment of the singular‐vector approach to targeted observing using the FASTEX dataset
Author(s) -
Gelaro R.,
Langland R. H.,
Rohaly G. D.,
Rosmond T. E.
Publication year - 1999
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.49712556109
Subject(s) - extratropical cyclone , data assimilation , subspace topology , computer science , troposphere , environmental science , meteorology , climatology , geology , artificial intelligence , geography
In this study, we investigate whether the results of assimilating special targeted observations from the Fronts and Atlantic Storm‐Track EXperiment (FASTEX) in an operational forecast model support the underlying principles of the singular‐vector (SV) approach to targeted observing. A simple framework is presented that allows explicit examination of the changes made to the analysis in the subspace of the leading SVs from assimilation of the observations. the impact of this component on the forecast provides a key measure of the effectiveness of SV‐based targeting. Results confirm that the impact of the additional observations occurs primarily as a result of changes to the analysis in the subspace of the leading SVs. These changes account for a small fraction of the total targeting increment at initial time, but explain a large fraction of the response of the forecast at the verification time. the results also confirm that analysis errors in the middle and lower troposphere are an important source of error in forecasts of extratropical cyclones. While moist processes can play an important role in the forecast‐error evolution, SVs that exclude these processes can remain an effective targeting tool. This is because the location of maximum sensitivity will not necessarily differ from that identified by the dry SVs. It is also shown that the locations of the leading (target) SVs can be computed accurately with lead times of up to 48 hours, allowing ample time for the deployment of observational resources.