CoChat
Sign Up
Cited by 232Meteorological Phenomena and SimulationsClimate variability and modelsAtmospheric aerosols and clouds

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining

Open access
Brenowitz, Noah D., Bretherton, Christopher S.·2019·Journal of Advances in Modeling Earth Systems
DOI 10.1029/2019ms001711
Open this paper in CoChat — free accountOpen PDF

Abstract

General circulation models (GCMs) typically have a grid size of 25–200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphysics and account for subgrid‐scale motions and variability. Unlike traditional approaches, neural networks (NNs) can readily exploit recent observational data sets and global cloud‐system resolving model (CRM) simulations to learn subgrid variability. This article describes an NN parametrization trained by coarse‐graining a near‐global CRM simulation with a 4‐km horizontal grid spacing. The NN predicts the residual heating and moistening averaged over (160 km) 2 grid boxes as a function of the coarse‐resolution fields within the same atmospheric column. This NN is coupled to the dynamical core of a GCM with the same 160‐km resolution. A recent study described how to train such an NN to be stable when coupled to specified time‐evolving advective forcings in a single‐column model, but feedbacks between NN and GCM components cause spatially extended simulations to crash within a few days. Analyzing the linearized response of such an NN reveals that it learns to exploit a strong synchrony between precipitation and the atmospheric state above 10 km. Removing these variables from the NN's inputs stabilizes the coupled simulations, which predict the future state more accurately than a coarse‐resolution simulation without any parametrizations of subgrid‐scale variability, although the mean state slowly drifts.

Overview

Integrity signals

Registry

Crossref · 10.1029/2019ms001711

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 4

  1. 10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2 · DOI
  2. ECMWF, 2018, IFS Documentation CY41R1, 1
  3. Goodfellow I., 2016, Deep learning, adaptive computation and machine learning
  4. Hanin B.(2017).Universal function approximation by deep neural nets with bounded width and ReLU activations. arXiv e‐prints.

Cited by

Sorted by citations

See all 232

Related

See all

Metadata

Authors
Brenowitz, N. D., & Bretherton, C. S.
Affiliations
University of Washington (US)
Corresponding author
Brenowitz, Noah D.
ORCID
Venue
Journal of Advances in Modeling Earth Systems
Publisher
American Geophysical Union (AGU)
Published
2019-08
Volume
11
Issue
8
Pages
2728-2744
ISSN
1942-2466 (ISSN-L)
DOI
10.1029/2019ms001711
OpenAlex
W2930756996
Cited by
231
References
37
Citations by year
2026: 21 · 2025: 16 · 2024: 45 · 2023: 50 · 2022: 30 · 2021: 47 · 2020: 20 · 2019: 2
FWCI
9.88
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Advances in Modeling Earth Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • Alfred P. Sloan Foundation (3835)
  • Gordon and Betty Moore Foundation (2013-10-29)
  • U.S. Department of Energy (DE-SC0012451, DE-SC0016433)
  • Washington Research Foundation
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
Keywords
neural network parametrization, coarse-graining, cloud-resolving model, subgrid-scale processes, linear stability analysis, model drift

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review