The Adjoint Sensitivity Guidance to Diagnosis and Tuning of Error Covariance Parameters
Adjoint techniques are effective tools for the analysis and optimization of the observation performance on reducing the errors in the forecasts produced by atmospheric data assimilation systems (DASs). This chapter provides a detailed exposure of the equations that allow the extension of the adjoint-DAS applications from observation sensitivity and forecast impact assessment to diagnosis and tuning of parameters in the observation and background error covariance representation. The error covariance sensitivity analysis allows the identification of those parameters of potentially large impact on the forecast error reduction and provides a first-order diagnostic to parameter specification. A proof-of-concept is presented together with comparative results of observation impact assessment and sensitivity analysis obtained with the adjoint versions of the Naval Research Laboratory Atmospheric Variational Data Assimilation System – Accelerated Representer (NAVDAS-AR) and the Navy Operational Global Atmospheric Prediction System (NOGAPS).
KeywordsForecast Error Observation Error Data Assimilation System Background Error Background Error Covariance
The work of D.N. Daescu was supported by the Naval Research Laboratory Atmospheric Effects, Analysis, and Prediction BAA #75-09-01 under award N00173-10-1-G032 and by the National Science Foundation under award DMS-0914937. Support for the second author from the sponsor ONR PE-0602435N is gratefully acknowledged.
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