Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning
Stockholm University, Faculty of Science, Department of Meteorology .
Number of Authors: 12018 (English)In: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 45, no 22, p. 12616-12622Article in journal (Refereed) Published
Abstract [en]

It is shown that it is possible to emulate the dynamics of a simple general circulation model with a deep neural network. After being trained on the model, the network can predict the complete model state several time steps aheadwhich conceptually is making weather forecasts in the model world. Additionally, after being initialized with an arbitrary model state, the network can through repeatedly feeding back its predictions into its inputs create a climate run, which has similar climate statistics to the climate of the general circulation model. This network climate run shows no long-term drift, even though no conservation properties were explicitly designed into the network. Plain Language Summary Numerical weather prediction and climate models are complex computer programs that represent the physics of the atmosphere. They are essential tools for predicting the weather and for studying the Earth's climate. Recently, a lot of progress has been made in machine learning methods. These are data-driven algorithms that learn from existing data. We show that it is possible that such an algorithm learns the dynamics of a simple climate model. After being presented with enough data from the climate model, the network can successfully predict the time evolution of the model's state, thus replacing the dynamics of the model. This finding is an important step toward purely data-driven weather forecastingthus weather forecasting without the use of traditional numerical models and also opens up new possibilities for climate modeling.

Place, publisher, year, edition, pages
2018. Vol. 45, no 22, p. 12616-12622
Keywords [en]
machine learning, weather prediction, neural networks, deep learning, climate models
National Category
Earth and Related Environmental Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
URN: urn:nbn:se:su:diva-163610DOI: 10.1029/2018GL080704ISI: 000453250000058OAI: oai:DiVA.org:su-163610DiVA, id: diva2:1277130
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2020-04-27Bibliographically approved
In thesis
1. Artificial intelligence in weather and climate prediction: Learning atmospheric dynamics
Open this publication in new window or tab >>Artificial intelligence in weather and climate prediction: Learning atmospheric dynamics
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Weather and climate prediction is dominated by high dimensionality, interactions on many different spatial and temporal scales and chaotic dynamics. This makes many problems in the field quite complex ones, and also state-of-the-art numerical models are - despite their immense computational costs - not sufficient for many applications. Therefore, it is appealing to use emerging new technologies such as artificial intelligence to tackle these problems.

We show that it is possible to use deep neural networks to emulate the full dynamics of a strongly simplified general circulation model, providing both good forecasts of the model state several days ahead as well as stable long-term climate timeseries. This method partly also works on more complex and realistic models, but only for forecasting the model's weather several days ahead, not for creating climate runs. It is sufficient to use 50-100 years of data for training the networks. The same neural network method can be combined with singular value decomposition from numerical ensemble weather forecasting in order to generate probabilistic ensemble forecasts with the neural networks.

On a more fundamental level, we show that in a simple dynamical systems setting there seem to be limitations in the ability of feed-forward neural networks to generalize to new regions of the system. This is caused by different parts of the network learning to model different parts of the system. Contradictory, for another simple dynamical system this is shown not to be an issue, raising doubts on the usefulness of results from simple models in the context of more complex ones. Additionally, we show that neural networks are to some extent able to “learn” the influence of slowly changing external forcings on the dynamics of the system, but only given broad enough forcing regimes.

Finally, we present a method to complement operational weather forecasts. Given the initial fields and the error of past weather forecasts, a neural network is used to predict the uncertainty in new forecasts, given only the initial field of the new forecast.

Place, publisher, year, edition, pages
Stockholm: Department of Meteorology, Stockholm University, 2020. p. 30
National Category
Meteorology and Atmospheric Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
urn:nbn:se:su:diva-180877 (URN)978-91-7911-128-1 (ISBN)978-91-7911-129-8 (ISBN)
Public defence
2020-06-12, Vivi Täckholmsalen (Q-salen), Svante Arrhenius väg 20, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2016-03724
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.

Available from: 2020-05-18 Created: 2020-04-20 Last updated: 2020-05-26Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Scher, Sebastian
By organisation
Department of Meteorology
In the same journal
Geophysical Research Letters
Earth and Related Environmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 73 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf