Identification

Title

U‐Net Kalman Filter (UNetKF): An example of machine learning‐assisted data assimilation

Abstract

Machine learning techniques have seen a tremendous rise in popularity in weather and climate sciences. Data assimilation (DA), which combines observations and numerical models, has great potential to incorporate machine learning and artificial intelligence (ML/AI) techniques. In this paper, we use U‐Net, a type of convolutional neutral network (CNN), to improve the localized error covariances for the Ensemble Kalman Filter (EnKF) algorithm. Using a 2‐layer quasi‐geostrophic model, U‐Nets are trained using data from EnKF DA experiments. The trained U‐Nets are then successfully implemented in U‐Net Kalman Filter (UNetKF) experiments to predict localized error covariances that possess adaptive localization and some state‐dependent features of the model error covariances. UNetKF is compared to traditional 3‐dimensional variational (3DVar), ensemble 3DVar (En3DVar) and EnKF methods. The performance of UNetKF can match or exceed that of 3DVar, or En3DVar and EnKF for small to moderate ensemble sizes. We also demonstrate that trained U‐Nets can be transferred to a higher‐resolution model for UNetKF implementation, which again performs competitively to 3DVar and EnKF, particularly for small ensemble sizes.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.net/ark:/85065/d7w381q3

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

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Temporal reference

Temporal extent

Begin position

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Dataset reference date

date type

publication

effective date

2025-04-01T00:00:00Z

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Use constraints

<span style="font-family:Arial;font-size:10pt;font-style:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span>

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact