Analog ensemble data assimilation in a quasigeostrophic coupled model

Details

• Journal Title:
  Quarterly Journal of the Royal Meteorological Society
• Personal Author:
  Grooms, Ian ; Renaud, Camille ; Stanley, Zofia ; Yang, L. Minah
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; PSL (Physical Sciences Laboratory)
• Description:
  The ensemble forecast dominates the computational cost of many data assimilation methods, especially for high‐resolution and coupled models. In situations where the cost is prohibitive, one can either use a lower‐cost model or a lower‐cost data assimilation method, or both. Ensemble optimal interpolation (EnOI) is a classical example of a lower‐cost ensemble data assimilation method that replaces the ensemble forecast with a single forecast and then constructs an ensemble about this single forecast by adding perturbations drawn from climatology. This research develops lower‐cost ensemble data assimilation methods that add perturbations to a single forecast, where the perturbations are obtained from analogs of the single model forecast. These analogs can either be found from a catalog of model states, constructed using linear combinations of model states from a catalog, or constructed using generative machine‐learning methods. Four analog ensemble data assimilation methods, including two new ones, are compared with EnOI in the context of a coupled model of intermediate complexity: Q‐GCM. Depending on the method and on the physical variable, analog methods can be up to 40% more accurate than EnOI.
• Source:
  Quarterly Journal of the Royal Meteorological Society, 149(752), 1018-1037
• DOI:
  https://doi.org/10.1002/qj.4446
• ISSN:
  0035-9009 ; 1477-870X
• Format:
  pdf
• Publisher:
  Wiley
• Document Type:
  Journal Article-Accepted Manuscript
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:84ec3ec8a436baf0e385d6baeb2d70bb6ffacc6173d9843c7a256ddb66dd6d1e4d983c5a64d289d56982be29424029169164695c3d536dfa7fce56a0a2b304f2
• Download URL:
  https://repository.library.noaa.gov/view/noaa/68131/noaa_68131_DS1.pdf
• File Type:
  [PDF - 2.68 MB ]