Challenges and design choices for global weather and climate models based on machine learning
| Autor: | Peter D. Dueben, Peter Bauer |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Toy model
010504 meteorology & atmospheric sciences Artificial neural network business.industry Computer science Deep learning lcsh:QE1-996.5 Weather and climate Context (language use) Resolution (logic) Machine learning computer.software_genre 01 natural sciences Boom 010305 fluids & plasmas lcsh:Geology 13. Climate action 0103 physical sciences Artificial intelligence business computer Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences |
| Zdroj: | Geoscientific Model Development, Vol 11, Pp 3999-4009 (2018) Geoscientific Model Development |
| ISSN: | 1991-9603 |
| Popis: | Can models that are based on deep learning and trained on atmospheric data compete with weather and climate models that are based on physical principles and the basic equations of motion? This question has been asked often recently due to the boom of deep learning techniques. The question is valid given the huge amount of data that is available, the computational efficiency of deep learning techniques and the limitations of today's weather and climate models in particular with respect to resolution and complexity. In this paper, the question will be discussed in the context of global weather forecasts. A toy-model for global weather predictions will be presented and used to identify challenges and fundamental design choices for a forecast system based on Neural Networks. |
| Databáze: | OpenAIRE |
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