期刊
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
卷 136, 期 653, 页码 2000-2012出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/qj.693
关键词
state analysis; parameter estimation; forecast impact; covariance tuning
资金
- NASA [NNG06GC67G]
- National Science Foundation [DMS-0914937]
The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjoint-DAS) makes feasible the evaluation of the local sensitivity of a model forecast aspect with respect to a large number of parameters in the DAS. In this study it is shown that, by exploiting sensitivity properties that are intrinsic to the analyses derived from a minimization principle, the adjoint-DAS software tools developed at numerical weather prediction centres for observation and background sensitivity may be used to estimate the forecast sensitivity to observation-and background-error covariance parameters and for forecast impact assessment. All-at-once sensitivity to error covariance weighting coefficients and first-order impact estimates are derived as a particular case of the error covariance perturbation analysis. The use of the sensitivity information as a DAS diagnostic tool and for implementing gradient-based error covariance tuning algorithms is illustrated in idealized data assimilation experiments with the Lorenz 40-variable model. Preliminary results of forecast sensitivity to observation-and background-error covariance weight parameters are presented using the fifth-generation NASA Goddard Earth Observing System (GEOS-5) atmospheric DAS and its adjoint developed at the Global Modeling and Assimilation Office. Copyright (C) 2010 Royal Meteorological Society
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