Variational particle smoothers and their localization

Details

• Journal Title:
  Quarterly Journal of the Royal Meteorological Society
• Personal Author:
  Morzfeld, M. ; Hodyss, D. ; Poterjoy, J.
• NOAA Program & Office:
  AOML (Atlantic Oceanographic and Meteorological Laboratory) ; OAR (Oceanic and Atmospheric Research)
• Description:
  Given the success of 4D‐variational methods (4D‐Var) in numerical weather prediction, and recent efforts to merge ensemble Kalman filters with 4D‐Var, we revisit how one can use importance sampling and particle filtering ideas within a 4D‐Var framework. This leads us to variational particle smoothers (varPS) and we study how weight‐localization can prevent the collapse of varPS in high‐dimensional problems. We also discuss the relevance of (localized) weights in near‐Gaussian problems. We test our ideas on the Lorenz'96 model of dimensions n = 40, n = 400, and n = 2,000. In our numerical experiments the localized varPS does not collapse and yields results comparable to ensemble formulations of 4D‐Var, while tuned EnKFs and the local particle filter lead to larger estimation errors. Additional numerical experiments suggest that using localized weights may not yield significant advantages over unweighted or linearized solutions in near‐Gaussian problems.
• Keywords:
  Atmospheric Science
• Source:
  Quarterly Journal of the Royal Meteorological Society, 144(712), 806-825
• DOI:
  https://doi.org/10.1002/qj.3256
• ISSN:
  0035-9009 ; 1477-870X
• Format:
  PDF
• Publisher:
  Wiley
• Document Type:
  Journal Article
• Rights Information:
  Accepted Manuscript
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:9c2ecb2d55db701478b5905289c2529d2d3d6d614112b39820fedeb798d691e9b2ede5d3ddd186f355566e2b8444dbd139b54dcf965fb3da6c34b85ceacf08ed
• Download URL:
  https://repository.library.noaa.gov/view/noaa/63800/noaa_63800_DS1.pdf
• File Type:
  [PDF - 634.68 KB ]