English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

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