English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Data assimilation combines forecasts from a numerical model withobservations. Most of the current data assimilation algorithms consider themodel and observation error terms as additive Gaussian noise, specified bytheir covariance matrices Q and R, respectively. These error covariances, andspecifically their respective amplitudes, determine the weights given to thebackground (i.e., the model forecasts) and to the observations in the solutionof data assimilation algorithms (i.e., the analysis). Consequently, Q and Rmatrices significantly impact the accuracy of the analysis. This review aims topresent and to discuss, with a unified framework, different methods to jointlyestimate the Q and R matrices using ensemble-based data assimilationtechniques. Most of the methodologies developed to date use the innovations,defined as differences between the observations and the projection of theforecasts onto the observation space. These methodologies are based on two mainstatistical criteria: (i) the method of moments, in which the theoretical andempirical moments of the innovations are assumed to be equal, and (ii) methodsthat use the likelihood of the observations, themselves contained in theinnovations. The reviewed methods assume that innovations are Gaussian randomvariables, although extension to other distributions is possible forlikelihood-based methods. The methods also show some differences in terms oflevels of complexity and applicability to high-dimensional systems. Theconclusion of the review discusses the key challenges to further developestimation methods for Q and R. These challenges include taking into accounttime-varying error covariances, using limited observational coverage,estimating additional deterministic error terms, or accounting for correlatednoises.

A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation