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Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground
Stockholm University, Faculty of Science, Department of Meteorology .
Stockholm University, Faculty of Science, Department of Meteorology . Uppsala University, Sweden.
Number of Authors: 22019 (English)In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 12, no 7, p. 2797-2809Article in journal (Refereed) Published
Abstract [en]

Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom-up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple general circulation models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging - in contrast to earlier promising results on a model without seasonal cycle.

Place, publisher, year, edition, pages
2019. Vol. 12, no 7, p. 2797-2809
National Category
Earth and Related Environmental Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
URN: urn:nbn:se:su:diva-171750DOI: 10.5194/gmd-12-2797-2019ISI: 000474740000001OAI: oai:DiVA.org:su-171750DiVA, id: diva2:1348561
Available from: 2019-09-04 Created: 2019-09-04 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

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