2012Quarterly Journal of the Royal Meteorological SocietyRequires access

Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter

Brian Holland, Xuguang Wang

Open publisher page 17 citations

Abstract

Abstract The various implementations of the ensemble Kalman filter (EnKF) differ from each other in several ways. The effects of these differences are not yet well and completely explored and they include the use of sequential or simultaneous assimilation of observations and the application of localization to the observation error covariance matrix (R‐localization) or the background error covariance matrix (B‐localization). This study seeks to examine and better understand the effects of these differences, both individually and in combination. To that end, a B‐localized sequential scheme, a B‐localized simultaneous scheme, an R‐localized sequential scheme and an R‐localized simultaneous scheme are compared using a primitive equation two‐layer model with simulated observations and an imperfect model assumption. The comparisons in initial assimilation experiments show that the use of sequential or simultaneous assimilation and R‐/B‐localization impacts the accuracy of the EnKF analyses and forecasts. Diagnostic experiments show that the schemes generate different amounts of imbalance in the analysis as a result of systematic differences among the schemes in height gradient and wind increments. These disparities in analysis balance translate into accuracy differences during the subsequent forecast and analysis steps. Additional simplified experiments suggest that the differences caused by the sequential or simultaneous assimilation and the R‐/B‐localization are a function of the characteristic shape of the background error covariances in the model system. Several elements of the forecast‐assimilation system including the use of digital filter initialization, the number and type of observations, the ensemble size and the ratio of forecast error to observation error are identified that can significantly increase or decrease the magnitude of the observed differences caused by sequential or simultaneous assimilation and the R‐/B‐localization. Copyright © 2012 Royal Meteorological Society

About this research paper

What this paper is about

Abstract The various implementations of the ensemble Kalman filter (EnKF) differ from each other in several ways. The effects of these differences are not yet well and completely explored and they include the use of sequential or simultaneous assimilation of observations and the application of localization to the observation error covariance matrix (R‐localization) or the background error covariance matrix (B‐localization). This study seeks to examine and better understand the effects of these differences, both individually and in combination. To that end, a B‐localized sequential scheme, a B‐localized simultaneous scheme, an R‐localized sequential scheme and an R‐localized simultaneous scheme are compared using a primitive equation two‐layer model with simulated observations and an imperfect model assumption. The comparisons in initial assimilation experiments show that the use of sequential or simultaneous assimilation and R‐/B‐localization impacts the accuracy of the EnKF analyses and forecasts. Diagnostic experiments show that the schemes generate different amounts of imbalance in the analysis as a result of systematic differences among the schemes in height gradient and wind increments. These disparities in analysis balance translate into accuracy differences during the subsequent forecast and analysis steps. Additional simplified experiments suggest that the differences caused by the sequential or simultaneous assimilation and the R‐/B‐localization are a function of the characteristic shape of the background error covariances in the model system. Several elements of the forecast‐assimilation system including the use of digital filter initialization, the number and type of observations, the ensemble size and the ratio of forecast error to observation error are identified that can significantly increase or decrease the magnitude of the observed differences caused by sequential or simultaneous assimilation and the R‐/B‐localization. Copyright © 2012 Royal Meteorological Society

Why it matters

OpenAlex reports 17 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Abstract The various implementations of the ensemble Kalman filter (EnKF) differ from each other in several ways. The effects of these differences are not yet well and completely explored and they include the use of sequential or simultaneous assimilation of observations and the application of localization to the observation error covariance matrix (R‐localization) or the background error covariance matrix (B‐localization). This study seeks to examine and better understand the effects of these differences, both individually and in combination. To that end, a B‐localized sequential scheme, a B‐localized simultaneous scheme, an R‐localized sequential scheme and an R‐localized simultaneous scheme are compared using a primitive equation two‐layer model with simulated observations and an imperfect model assumption. The comparisons in initial assimilation experiments show that the use of sequential or simultaneous assimilation and R‐/B‐localization impacts the accuracy of the EnKF analyses and forecasts. Diagnostic experiments show that the schemes generate different amounts of imbalance in the analysis as a result of systematic differences among the schemes in height gradient and wind increments. These disparities in analysis balance translate into accuracy differences during the subsequent forecast and analysis steps. Additional simplified experiments suggest that the differences caused by the sequential or simultaneous assimilation and the R‐/B‐localization are a function of the characteristic shape of the background error covariances in the model system. Several elements of the forecast‐assimilation system including the use of digital filter initialization, the number and type of observations, the ensemble size and the ratio of forecast error to observation error are identified that can significantly increase or decrease the magnitude of the observed differences caused by sequential or simultaneous assimilation and the R‐/B‐localization. Copyright © 2012 Royal Meteorological Society

Key concepts: Data assimilation, Initialization, Ensemble Kalman filter, Kalman filter, Covariance, Algorithm, Mathematics, Covariance matrix

Related papers

Back to paper searchBrowse research topicsOriginal source
Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter — Research Paper | ScholarLens