Review: Using Machine Learning for Data Assimilation, Model Physics, and Post-Processing model outputs

Details

• Corporate Authors:
  Krasnopolsky, Vladimir
• Conference Authors:
  Environmental Modeling Center (U.S.) ; National Centers for Environmental Prediction (U.S.)
• NOAA Program & Office:
  NWS (National Weather Service) ; EMC (Environmental Modelling Center) ; NCEP (National Centers for Environmental Prediction)
• Description:
  In this paper machine learning (ML) applications developed at EMC and at other leading weather centers for numerical weather and climate modeling systems (NWCMS) are briefly introduced. The most important papers published in this field during recent years are reviewed. Advantages and limitations of the ML approach in applications to NWCMS are briefly discussed.
• Keywords:
  Review: Using Machine Learning for Data Assimilation, Model Physics, and Post-Processing model outputs
• Series:
  Office note (National Centers for Environmental Prediction (U.S.))   513
• DOI:
  https://doi.org/10.25923/71tx-4809
• Format:
  PDF
• Document Type:
  Office Note
• Name as Subject:
  In this paper machine learning (ML) applications developed at EMC and at other leading weather centers for numerical weather and climate modeling systems (NWCMS) are briefly introduced. The most important papers published in this field during recent years are reviewed. Advantages and limitations of the ML approach in applications to NWCMS are briefly discussed.
• License:
  https://creativecommons.org/publicdomain/zero/1.0/
• Rights Information:
  CC0 Public Domain
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:0e8b49bd6586b0c0ad7c72ba82a8d23d16e658ffa2d596b8b4685b3709b51b3d
• Download URL:
  https://repository.library.noaa.gov/view/noaa/50158/noaa_50158_DS1.pdf
• File Type:
  [PDF - 995.55 KB ]