Description
Carlos Alberto Gomez Gonzalez nos presenta "Deep learning-based super-resolution of climate forecast data".
Resumen: Seasonal climate predictions can forecast the climate variability up to several months ahead and support a wide range of societal activities. The coarse spatial resolution of seasonal forecasts needs to be refined to the regional/local scale for specific applications. Super-resolution, or statistical downscaling in the climate jargon, aims at learning a mapping between low and high resolution images (gridded climate datasets). In this talk, I would like to explain how I developed deep convolutional networks in supervised and generative adversarial training frameworks for the task of super-resolving seasonal forecast of temperature over Catalonia. Additionally, I will stress on the importance of Python for scientific software development and for the application of cutting-edge machine learning and AI in Earth Sciences.
--- La novena edición de la PyConES se celebra como un evento en línea y totalmente gratuito durante los días 2 y 3 de Octubre de 2021. Web: https://2021.es.pycon.org Agenda: https://2021.es.pycon.org/#schedule