Using Convolutional Neural Processes to Produce High-Resolution Weather Datasets Over New Zealand

Using Convolutional Neural Processes to Produce High-Resolution Weather Datasets Over New Zealand (Papers Track)

Emily O'Riordan (Bodeker Scientific)

NeurIPS 2024 Recorded Talk Cite

Meta- and Transfer Learning Climate Science & Modeling Extreme Weather Uncertainty Quantification & Robustness

Abstract

In recent years, there has been a surge in the development and success of artificial intelligence methods for global weather forecasting and climate modelling. For regional models, however, there is a lack of high-resolution datasets that can be used to train data-driven forecasting models. In this work, we showcase the use of convolutional neural processes (ConvNP) to generate hourly high-resolution (1km) weather datasets over New Zealand for temperature and precipitation. ConvNP models allow us to produce datasets that are enhanced by station observations and provide uncertainty estimates in their predictions. The generated datasets have applications in AI weather and climate models, model verification, and broader environmental research.