Numerical Prediction Development Center, Japan Meteorological Agency, Tsukuba, Ibaraki 305-0052, Japan
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki 305-0052, Japan
Miodrag Rancic
NOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland 20740, USA
Lynker, College Park, Maryland 20740, USA
Ting Lei
NOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland 20740, USA
Lynker, College Park, Maryland 20740, USA
R. James Purser
NOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland 20740, USA
Lynker, College Park, Maryland 20740, USA
Manuel S. F. V. De Pondeca
NOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland 20740, USA
Lynker, College Park, Maryland 20740, USA
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Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 2,150 (including HTML, PDF, and XML)
Thereof 2,138 with geography defined
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Total article views: 77 (including HTML, PDF, and XML)
Thereof 77 with geography defined
and 0 with unknown origin.
Total article views: 2,073 (including HTML, PDF, and XML)
Thereof 2,061 with geography defined
and 12 with unknown origin.
Covariance localization to mitigate sampling error of ensemble-based forecast error covariances is one of the main parts of the calculation in ensemble-variational data assimilation for the atmosphere. This study clarifies that the multigrid beta filter-based localization makes it several times faster than the conventional recursive filter-based one without significantly changing the analysis if a coarser filter grid is applied and filters except for the coarsest resolution are omitted.
Covariance localization to mitigate sampling error of ensemble-based forecast error covariances...