13 papers in the archive.
SwinIR and Uformer transformers downscale and bias-correct ECMWF HRES 2 m temperature and 10 m wind to 0.01°, outperforming UNet and RCAN baselines.
Downscaling & super-resolutionFudan University (FuXi)Alibaba (DAMO Academy)
A U-Net reconstructs radar composite reflectivity over the ocean from satellite data, with interpretability showing cloud features matter most.
Observations & remote sensingInstitute of Atmospheric Physics, Chinese Academy of SciencesAlibaba (DAMO Academy)
LSTM and deep LSTM models forecast daily air temperature in Tabriz, Iran, from 1,097 observations, with the deep version reaching RMSE 0.08 °C.
Physics-incorporated network enforces flux-heating rate energy conservation in radiative transfer; bidirectional RNNs and transformers beat FC nets and CNNs.
Hybrid physics-ML & parameterizationNanjing University of Information Science & Technology (NUIST)Alibaba (DAMO Academy)
Machine learning gives probabilistic tropical cyclone intensity forecasts with intervals, and its point forecasts outperform an operational statistical-dynamical model over 2010–2020.
SwinRDM couples a SwinRNN forecaster with a diffusion super-resolution step to reach 0.25° forecasts that beat IFS up to 5 days on Z500, T850, T2M and precipitation.
WRF-ML coupler lets Python deep learning parameterizations run inside the Fortran WRF model, demonstrated with trained DL radiation emulators coupled to WRF.
PTCIF gives probabilistic 6-24 h tropical cyclone intensity forecasts from multimodal data, with prediction intervals comparable to forecast centres on deterministic scores.
Two deep-learning bias-correction pathways improve 4-month-lead summer precipitation forecasts from a Chinese GCM, with the deterministic pathway better for extremes.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)Alibaba (DAMO Academy)
Physics-incorporated network enforces energy conservation between fluxes and heating rates for fast radiative transfer emulation; Bi-LSTM performs best among tested architectures.
Hybrid physics-ML & parameterizationNanjing University of Information Science & Technology (NUIST)Alibaba (DAMO Academy)
SwinVRNN learns VAE-based distribution perturbations within a Swin transformer recurrent model, giving better ensemble accuracy and spread than other perturbation methods on WeatherBench.
Probabilistic & ensemble forecastingAlibaba (DAMO Academy)Fudan University (FuXi)
Neural network system with time-frequency decomposition forecasts tropical cyclone wave height in real time; the abstract is missing, so the classification relies on the title.
Spatial and spectrum GANs with masked style loss sharpen ConvLSTM and U-Net radar echo extrapolations on SEVIR, plus a new power spectral density score.
