edu.ucar.opensky::articles:22741 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:34:26.236438 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7348md9 Model-space localization in serial ensemble filters 2019-05-02T00:00:00Z publication https://n2t.org/ark:/85065/d7348md9 https://doi.org/10.1029/2018MS001514 Shlyaeva, A. author Whitaker, J. S. author Snyder, Chris author UCAR/NCAR - Library publisher Ensemble-based data assimilation systems typically use covariance localization to dampen spurious correlations associated with sampling error while increasing the rank of the covariance estimate. Variational methods use model-space localization, in which localization is applied to ensemble estimates of covariances between model variables and is based on distances between those variables, while ensemble filters apply observation-space localization to estimates of model-observation covariances, based on distances between model variables and observations. It has been shown that for nonlocal observations, such as satellite radiances, model-space localization can be superior. This paper demonstrates a new method for performing model-space localization in serial ensemble filters using the linearized observation operators (or Jacobians). Results of radiance-only assimilation in a global forecast system show the benefit of using model-space localization relative to observation-space localization. The serial ensemble square root filter with vertical model-space localization gives results similar to those of the Ensemble Variational system (without outer loops or extra balance constraints) while increasing the runtime compared to the filter with observation-space localization by a factor between 2 and 8, depending on how sparse the Jacobian matrices are. The results are also similar to another approach to model-space localization in ensemble filters: ensemble Kalman filter with modulated ensembles. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2019 American Geophysical Union. None eng geoscientificInformation