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

doi:10.25923/71tx-4809

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

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

2023
By Krasnopolsky, Vladimir
Series: Office note (National Centers for Environmental Prediction (U.S.)) ; 513

[PDF-995.55 KB]

Details You May Also Like

Details:

Corporate Authors:

Krasnopolsky, Vladimir
Conference Authors:

Environmental Modeling Center (U.S.) ; National Centers for Environmental Prediction (U.S.)

Environmental Modeling Center (U.S.) ; National Centers for Environmental Prediction (U.S.) Less -
NOAA Program & Office:

NWS (National Weather Service) ; EMC (Environmental Modelling Center) ; NCEP (National Centers for Environmental Prediction)

NWS (National Weather Service) ; EMC (Environmental Modelling Center) ; NCEP (National Centers for Environmental Prediction) Less -
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:

No Additional Files

More +

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

Details:

Supporting Files

You May Also Like