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Influence-matrix diagnostics can be adapted for large-dimension variational data assimilation systems to quantify observation influence, revealing that in the ECMWF system, prior background information accounts for 85% of the global influence while assimilated observations account for 15%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 1, 2004

Influence‐matrix diagnostic of a data assimilation system

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Abstract

Abstract The influence matrix is used in ordinary least‐squares applications for monitoring statistical multiple‐regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis—the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub‐set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self‐sensitivities) has been developed for a large‐dimension variational data assimilation system (the four‐dimensional variational system of the European Centre for Medium‐Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short‐range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface‐based observing systems, and 75% by satellite systems. Low‐influence data points usually occur in data‐rich areas, while high‐influence data points are in data‐sparse areas or in dynamically active regions. Background‐error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation‐error covariance matrices can be identified, interpreted and better understood by the use of influence‐matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

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