DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting

WashU Affiliated Authors: Tao Ge (Electrical and Systems Engineering)

Abstract: Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however,
is based on supervision on mesh-gridded weather data without the utilization of raw
climate observational data, the gold standard ground truth. In this work we develop
a methodology to correct, remap, and fine-tune gridded uniform forecasts of FCN so
it can be directly compared against observational ground truth, which is sparse and
non-uniform in space and time. This is akin to bias-correction and post-processing
of numerical weather prediction (NWP), a routine operation at meteorological and
weather forecasting centers across the globe. The Adaptive Fourier Neural Operator
(AFNO) architecture is used as the backbone to learn continuous representations
of the atmosphere. The spatially and temporally non-uniform output is evaluated
by the non-uniform discrete inverse Fourier transform (NUIDFT) given the output
query locations. We call this network the Deep-Learning-Corrector-Remapper
(DLCR). The improvement in DLCR’s performance against the gold standard
ground truth over the baseline’s performance shows its potential to correct, remap,
and fine-tune the mesh-gridded forecasts under the supervision of observations.

Citation/DOI: DOI: 10.48550/arXiv.2210.12293