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http://hdl.handle.net/2445/187022
Title: | Sub-seasonal to seasonal climate forecasting using machine learning |
Author: | Bech Sala, Sergi |
Director/Tutor: | Vitrià i Marca, Jordi Lledó Ponsatí, Llorenç |
Keywords: | Aprenentatge automàtic Previsió del temps Programari Treballs de fi de grau Precipitacions (Meteorologia) Temperatura atmosfèrica Models matemàtics Machine learning Weather forecasting Computer software Precipitations (Meteorology) Atmospheric temperature Bachelor's theses Mathematical models |
Issue Date: | 24-Jan-2022 |
Abstract: | [en] The main topic of this work is the study and the application of Machine Learning (ML) techniques to improve probabilistic forecasts of two-meter temperature and total precipitation at sub-seasonal scales (i.e. several weeks ahead) for the whole globe. We analyze the performance of a number of Machine Learning methods and finally we combine the best models to obtain the optimal prediction at each latitude, longitude, and for each lead time. In addition, the results of this work have been presented to an open prize challenge launched by the World Meteorological Organization (WMO) to improve current forecasts of precipitation and temperature from state-of-the-art numerical weather and climate prediction models 3 to 6 weeks into the future using Artificial Intelligence. |
Note: | Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Jordi Vitrià i Marca i Llorenç Lledó Ponsatí |
URI: | http://hdl.handle.net/2445/187022 |
Appears in Collections: | Programari - Treballs de l'alumnat Treballs Finals de Grau (TFG) - Enginyeria Informàtica Treballs Finals de Grau (TFG) - Matemàtiques |
Files in This Item:
File | Description | Size | Format | |
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codi font.zip | Codi font | 112.81 kB | zip | View/Open |
tfg_bech_sala_sergi.pdf | Memòria | 3.8 MB | Adobe PDF | View/Open |
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