Properties and first application of an error‐statistics tuning method in variational assimilation

Abstract

SUMMARYThe method for tuning observational or background error statistics is presented and some of its properties are exposed using theoretical considerations and experiments carried out in a simplified framework. In particular, the method is shown to be equivalent to a maximum-likelihood evaluation and its efficiency is seen to depend on the number of observations. The results of several experiments carried out with the variational assimilation system of the French numerical weather-prediction system ARPEGE, both with simulated and actual datasets involving satellite radiances, are also presented. The temporal stability of the results and their consistency with the known quality of the measurements are shown.

Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance

“…The method could, in principle, provide guidance for any assimilation system. By considering the observation space subdomain [25], proper scaling, local averaging [26], or other methods discussed in Janjic et al [12] it may also be possible to extend this methodology to spatially varying error statistics. Based on our verification results in Part I [5], we found that there is a dependence between model values and error variances, which we will investigate further in view of our next operational implementation of the Canadian surface air quality analysis and assimilation.…”

Natural and anthropogenic methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric inversion

“…In Berchet et al (2015), we proposed a general method in order to objectively quantify most of the critical sources of errors in the inversion. This improved algorithm is based on a Monte Carlo approach superimposed to maximum likelihood estimators (Chapnik et al, 2004;Michalak and Kitanidis, 2005).…”

Error covariance sensitivity and impact estimation with adjoint 4D‐Var: theoretical aspects and first applications to NAVDAS‐AR

“…The estimates σ 2 o may be interpreted as a first iteration of a fixed-point algorithm for tuning the observationerror variances (Desroziers et al, 2005) and their quality may be impaired by various factors including approximations entailed by nonlinearities in the observational operator and misrepresentation of the observationand background-error correlations (Chapnik et al, 2004;Ménard et al, 2009). An artificial adjustment of various input parameters in the DAS may be used to alleviate deficiencies in the specification of the observation-and background-error covariances.…”