Observation Assimilation Transformer: Probabilistic Weather Analysis from Sparse Station Observations

Key points

• OAT is an encoder-decoder transformer that considers a set of observed locations and their NWP prior to condition predictions at a set of arbitrary unobserved query locations.1
• A station-randomized training algorithm is developed with continuous ranked probability score (CRPS) loss.1
• We test OAT on two datasets - one at regional scale with hundreds of stations, and one at continental scale with thousands of stations.1
• In an illustrative case study, OAT's downscaling capabilities are demonstrated by downscaling ERA5 ( 31km) to COSMO (1km), where OAT achieves lower mean absolute error (MAE) and CRPS for temperature and precipitation on test stations compared to COSMO.1

Sources (1)

• [1]Observation Assimilation Transformer: Probabilistic Weather Analysis from Sparse Station Observations
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:00 PM

  We present the Observation Assimilation Transformer (OAT), a geometry-aware neural process that corrects and refines a prior numerical weather prediction (NWP) field into a high-resolution posterior field by assimilating sparse observations.

  OAT is an encoder-decoder transformer that considers a set of observed locations and their NWP prior to condition predictions at a set of arbitrary unobserved query locations.

Extractive summary: sentences quoted from the sources.