elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Can Artificial Intelligence‐Based Weather Prediction Models Simulate the Butterfly Effect?

Selz, Tobias and Craig, George C. (2023) Can Artificial Intelligence‐Based Weather Prediction Models Simulate the Butterfly Effect? Geophysical Research Letters, 50 (20), pp. 1-9. Wiley. doi: 10.1029/2023GL105747. ISSN 0094-8276.

[img] PDF - Published version
1MB

Official URL: https://dx.doi.org/10.1029/2023GL105747

Abstract

We investigate error growth from small-amplitude initial condition perturbations, simulated with a recent artificial intelligence-based weather prediction model. From past simulations with standard physically-based numerical models as well as from theoretical considerations it is expected that such small-amplitude initial condition perturbations would grow very fast initially. This fast growth then sets a fixed and fundamental limit to the predictability of weather, a phenomenon known as the butterfly effect. We find however, that the AI-based model completely fails to reproduce the rapid initial growth rates and hence would incorrectly suggest an unlimited predictability of the atmosphere. In contrast, if the initial perturbations are large and comparable to current uncertainties in the estimation of the initial state, the AI-based model basically agrees with physically-based simulations, although some deficits are still present.

Item URL in elib:https://elib.dlr.de/199357/
Document Type:Article
Title:Can Artificial Intelligence‐Based Weather Prediction Models Simulate the Butterfly Effect?
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Selz, TobiasDLR, IPAhttps://orcid.org/0000-0002-1767-4381UNSPECIFIED
Craig, George C.MIM, LMU München, DLR, IPAUNSPECIFIEDUNSPECIFIED
Date:26 October 2023
Journal or Publication Title:Geophysical Research Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:50
DOI:10.1029/2023GL105747
Page Range:pp. 1-9
Publisher:Wiley
ISSN:0094-8276
Status:Published
Keywords:artificial-intelligence-based models synoptic-scale error
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Climate, Weather and Environment
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Transport Meteorology
Deposited By: Ziegele, Brigitte
Deposited On:30 Nov 2023 08:03
Last Modified:30 Jan 2024 13:00

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.