Can I read Sim2Real for Environmental Neural Processes on EtoBox?

Sim2Real for Environmental Neural Processes by Scholz, Jonas; Andersson, Tom R.; Vaughan, Anna; Requeima, James; Turner, Richard E. is a scholarly article available to read on EtoBox.

What is Sim2Real for Environmental Neural Processes about?

Machine learning (ML)-based weather models have recently undergone rapid improvements. These models are typically trained on gridded reanalysis data from numerical data assimilation systems. However, reanalysis data comes with limitations, such as assumptions about physical laws and low spatiotemporal resolution. The gap between reanalysis and reality has sparked growing interest in training ML models directly on observations such as weather stations. Modelling scattered and sparse environmental observations requires scalable and flexible ML architectures, one of which is the convolutional conditional neural process (ConvCNP). ConvCNPs can learn to condition on both gridded and off-the-grid context data to make uncertainty-aware predictions at target locations. However, the sparsity of real observations presents a challenge for data-hungry deep learning models like the ConvCNP. One potential solution is 'Sim2Real': pre-training on reanalysis and fine-tuning on observational data. We analyse Sim2Real with a ConvCNP trained to interpolate surface air temperature over Germany, using varying numbers of weather stations for fine-tuning. On held-out weather stations, Sim2Real training su

Author

Scholz, Jonas; Andersson, Tom R.; Vaughan, Anna; Requeima, James; Turner, Richard E.

Published

2023

Language

EN