  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 substantially
outperforms the same model architecture trained only with reanalysis data or
only with station data, showing that reanalysis data can serve as a stepping
stone for learning from real observations. Sim2Real could thus enable more
accurate models for weather prediction and climate monitoring.

Machine Learning;Atmospheric and Oceanic Physics