elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Understanding and Modelling Convection with Machine Learning

Behrens, Gunnar (2024) Understanding and Modelling Convection with Machine Learning. Dissertation, Universität Bremen und DLR, IPA. doi: 10.26092/elib/3050.

[img] PDF
21MB

Offizielle URL: https://media.suub.uni-bremen.de/handle/elib/8016

Kurzfassung

Machine Learning (ML) has demonstrated its potential to improve the performance of an Earth System Model (ESM), yet many challenges remain. ESMs are essential tools to project and understand climate change but have known biases. Convective processes are in general unresolved as their typical length scale is smaller than the grid size of ESMs. The effect of such subgrid processes is estimated with parameterizations, that are often attributed to be sources of biases in ESMs. A way to reduce these limitations of ESMs is to take advantage of ML or deep learning (DL) algorithms that learn actively on output from convection permitting high-resolution simulations which explicitly resolve convective processes. The resulting ML parameterizations are then coupled with an ESM and replace existing traditional subgrid parameterizations in hybrid (physics + ML) ESMs. This thesis presents novel approaches to transform DL algorithms from data science concepts towards an operational use in ESM simulations. First, a DL algorithm is developed that enables to better understand subgrid convective processes and interactions with the large-scale environment. Specifically, the latent space of a Variational Encoder Decoder (VED) reveals the meridional temperature differences between the tropics and poles together with the characteristics of subtropical and subpolar air masses along the mid-latitude storm tracks are key drivers of convective processes. Moreover, the VED separates key characteristics of shallow convective, cumulus, cirrus-like and deep convection regimes. Second, a novel DL algorithm ensemble approach is developed, that provides an improved representation of convective processes. Third, it is demonstrated that the more realistic uncertainty quantification of the ensembles capturing the chaotic nature of subgrid processes stabilizes hybrid simulations and reduces longstanding biases in a hybrid model run of an ESM. This thesis thus advances the modelling of convective processes with DL in Earth system sciences via enhanced representation and understanding of convective processes in ESMs. It provides ways to reduce limitations of state-of-the-art ML models and paves a way forward to the operational use of DL and ML in the next generation of ESMs.

elib-URL des Eintrags:https://elib.dlr.de/204839/
Dokumentart:Hochschulschrift (Dissertation)
Titel:Understanding and Modelling Convection with Machine Learning
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Behrens, GunnarUniversität Bremen und DLR, IPAhttps://orcid.org/0000-0002-5921-5327NICHT SPEZIFIZIERT
Datum:17 Juni 2024
Erschienen in:Staats- und Universitätsbibliothek Bremen
Open Access:Ja
DOI:10.26092/elib/3050
Seitenanzahl:189
Status:veröffentlicht
Stichwörter:Machine Learning; Convective Processes; Large-Scale Drivers of Convective Processes; Stochasticity; Superparameterization; Ensembles
Institution:Universität Bremen und DLR, IPA
Abteilung:Fachbereich 01: Physik/Elektrotechnik (FB 01)
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Atmosphären- und Klimaforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Physik der Atmosphäre > Erdsystemmodell -Evaluation und -Analyse
Hinterlegt von: Behrens, Gunnar
Hinterlegt am:24 Jun 2024 13:09
Letzte Änderung:27 Aug 2024 10:51

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.