8 papers in the archive · tier-1 watchlist.
WeatherGFM is a generalist weather foundation model using in-context visual prompting to handle up to ten weather tasks, including forecasting, super-resolution and post-processing.
Foundation modelsShanghai Artificial Intelligence Laboratory
First data-driven global model at 0.09 degree resolution, initialized by transfer from a low-resolution model, outperforming IFS-HRES in 2022 hindcasts.
Global medium-range NWPChina Meteorological Administration (CMA)Nanjing University of Information Science & Technology (NUIST)Shanghai Artificial Intelligence Laboratory
Adas is an AI data assimilation model that, combined with FengWu, forms an end-to-end AI forecast system with skill comparable to IFS.
Data assimilationFudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)Shanghai Artificial Intelligence Laboratory
Couples the FengWu forecast model with 4D-Var using autodifferentiation instead of adjoints, producing cyclic analyses from simulated observations.
Data assimilationShanghai Artificial Intelligence LaboratoryTsinghua University (NowcastNet)
CycleGAN bias correction of NUIST-CFS summer precipitation forecasts in southeastern China outperforms quantile mapping and better captures extreme precipitation frequency and intensity.
Post-processing & calibrationShanghai Artificial Intelligence LaboratoryNanjing University of Information Science & Technology (NUIST)
FengWu is a multi-modal transformer forecasting system that extends skillful forecasts to 10.75 days, beating GraphCast on 80% of 880 predictands.
Global medium-range NWPInstitute of Atmospheric Physics, Chinese Academy of SciencesNanjing University of Information Science & Technology (NUIST)Shanghai Artificial Intelligence Laboratory
MTL-NET multi-task deep learning predicts the Indian Ocean Dipole up to 7 months ahead, outperforming most dynamical models in hindcasts.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)Shanghai Artificial Intelligence Laboratory
PGnet is a physics-informed two-stage generative network that uses a mask to refine ERA5 500 hPa temperature predictions and reduce error accumulation in time-series rollouts.
Global medium-range NWPShanghai Artificial Intelligence Laboratory
