Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model
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

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

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

i

Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model

Filetype[PDF-5.25 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed

Details:

  • Journal Title:
    Geoscientific Model Development
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    The ability of machine-learning-based (ML-based) model components to generalize to the previously unseen inputs and its impact on the stability of the models that use these components have been receiving a lot of recent attention, especially in the context of ML-based parameterizations. At the same time, ML-based emulators of existing physically based parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural-network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art general circulation model (GCM) are robust with respect to the substantial structural and parametric change in the host model: when used in two 7-month-long experiments with a new GCM, they remain stable and generate realistic output. We concentrate on the stability aspect of the emulators' performance and discuss features of neural network architecture and training set design potentially contributing to the robustness of ML-based model components.
  • Keywords:
  • Source:
    Geoscientific Model Development, 14(12), 7425-7437
  • DOI:
  • ISSN:
    1991-9603
  • Format:
  • Publisher:
  • Document Type:
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26.1