Correcting weather predictions of sea-surface wind with deep learning
Numerical models for weather prediction are frequently utilized to drive ocean simulations due to their provision of continuous data over both time and space. However, the outputs of these models exhibit inaccuracies in certain global regions, particularly in predicting the direction of sea-surface...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/424879 |
| Acceso en línea: | https://hdl.handle.net/2117/424879 |
| Access Level: | acceso abierto |
| Palabra clave: | Weather forecasting Deep learning (Machine learning) convolutional neural networks deep learning U-Net marine sciences wind PyTorch Previsió del temps Aprenentatge profund Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Numerical models for weather prediction are frequently utilized to drive ocean simulations due to their provision of continuous data over both time and space. However, the outputs of these models exhibit inaccuracies in certain global regions, particularly in predicting the direction of sea-surface winds. Even the latest versions of these models demonstrate such errors, which are attributed to natural processes that remain unmodeled. The aim of this master?s thesis is to predict these errors, referred to in this project as ERA5 model biases, using the most suitable Deep Learning architecture. This work builds on previous research that employed point-by-point prediction using Machine Learning and Fully Connected Neural Networks, which showed promising results. A Neural Network, specifically the U-Net for regression, is proposed, implemented, and evaluated to predict the model differences in sea surface wind direction. |
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