35 papers in the archive.
DLESyM, a deep learning Earth system model, simulates 1000-year observed climate without drift, matching CMIP6 variability at a fraction of the cost.
Climate emulationInstitute of Atmospheric Physics, Chinese Academy of SciencesUniversity of Washington
CNN-BiLSTM-attention model forecasts convective and heavy precipitation from GFS data, reaching threat score 0.336 versus 0.252 for forecasters.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)Institute of Atmospheric Physics, Chinese Academy of Sciences
CNN post-processing of ECMWF-HRES forecasts at 390 North China stations raises threat scores by up to 296% at high precipitation thresholds.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Explainable CNN on 1200-year simulations attributes North China summer precipitation variability to PDO and AMO sea surface temperature patterns, with 96-98% accuracy.
Climate analysis & attributionFudan University (FuXi)Institute of Atmospheric Physics, Chinese Academy of Sciences
Case studies show Pangu-Weather runs stably from several operational NWP analyses as initial conditions, and better initial conditions improve its forecast skill.
Global medium-range NWPInstitute of Atmospheric Physics, Chinese Academy of Sciences
Machine learning seasonal prediction of summer extreme precipitation frequency over Southwest China; classified from the title only, as the abstract is missing.
Sub-seasonal to seasonalInstitute of Atmospheric Physics, Chinese Academy of Sciences
A convolutional neural network is reported to outperform dynamical models in predicting Central Pacific ENSO events.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)Institute of Atmospheric Physics, Chinese Academy of Sciences
Machine learning model estimates urban Beijing planetary boundary layer height from ERA5 predictors.
Observations & remote sensingInstitute of Atmospheric Physics, Chinese Academy of Sciences
U-Net reconstructs radar reflectivity from WRF simulations and Himawari-8 satellite data with 5.38 dBZ RMSE, revealing scale-separated atmospheric features.
Observations & remote sensingInstitute of Atmospheric Physics, Chinese Academy of Sciences
Attentional GAN with a spatial variability loss reduces the blurry effect in radar nowcasts and outperforms competing models on the test set and storm cases.
NowcastingTsinghua University (NowcastNet)Colorado State University (incl. CIRA)Institute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
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)
Ensemble neural networks correct nonstationary sea surface temperature bias in NCEP CFSv2 seasonal forecasts, reducing RMSE by 0.35 °C, with a deeper network giving further gains.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of Sciences
GRU_CNN forecasts northwestern Pacific tropical cyclone tracks for 6 to 72 hours using track history and reanalysis fields, beating RNN, LSTM and GRU.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)Institute of Atmospheric Physics, Chinese Academy of Sciences
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
A residual network with automatically selected precursors predicts ENSO up to 8 months ahead, with a 6-month correlation of 0.82 beating the multi-model mean of 0.70.
Sub-seasonal to seasonalTsinghua University (NowcastNet)Nanjing University of Information Science & Technology (NUIST)Institute of Atmospheric Physics, Chinese Academy of Sciences
Studies predictor selection for CNN-based statistical downscaling of monthly precipitation, aiming to improve regional precipitation fields from large-scale predictors.
Downscaling & super-resolutionInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
XGBoost corrects short-term wind speed forecasts from a local NWP system at Hangzhou stations, cutting RMSE 26.1% versus NWP and outperforming MOS.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of Sciences
Weighted U-Net corrects ECMWF-IFS precipitation over China, improving heavy-rainfall forecast skill over the ordinary U-Net, with gains growing at longer lead times.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
CatBoost bias-corrects WRF wind-speed forecasts at a Gobi wind farm, reducing a four-day mean deviation from 3.21 to about 2.50 m/s.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Machine-learning quality control for ABI water vapor radiances blends seven bands to reject surface-contaminated pixels, improving Hurricane Harvey track forecasts.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Institute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Random forest on three preceding atmosphere-ocean indices gives a seasonal early warning of Yangtze summer extreme precipitation, including the 2020 event.
Sub-seasonal to seasonalInstitute of Atmospheric Physics, Chinese Academy of Sciences
U-Net based CLP-CNN classifies cloud types from FY-4A L1 data over China in 0.9 s per 800x800 scene, matching Himawari-8 products 76.8% of the time.
Observations & remote sensingInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Hybrid XGBoost models using S2S dynamical forecasts predict northern China winter wheat yield 3–4 months ahead with R² 0.85, beating observed climate inputs.
Applications & impactsInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Model residual machine learning uses station forecasts to fit ECMWF residuals, producing high-resolution grids of temperature, humidity and wind that beat interpolation.
Downscaling & super-resolutionInstitute of Atmospheric Physics, Chinese Academy of Sciences
Hybrid machine learning and S2S dynamical forecasts improve northern China winter wheat yield prediction over observation-based inputs.
Applications & impactsInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
CNN-based statistical post-processing corrects ensemble precipitation forecasts, improving on raw ensemble output for rainfall prediction.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of Sciences
DeepSTF combines CNN and attention encoder-decoder to post-process multi-site forecasts at 226 Beijing stations, improving on MOS benchmarks.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of Sciences
U-Net-style CNN reconstructs 35 dBZ radar reflectivity from Himawari-8 infrared channels over North China, a proxy product for regions without radar.
Observations & remote sensingInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Trains ConvGRU radar echo extrapolation with SSIM and MS-SSIM losses to reduce blurring; MS-SSIM gives the most realistic strong-echo forecasts out to 2 hours.
NowcastingNanjing University of Information Science & Technology (NUIST)Institute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
A deep neural network fuses NUCAPS, ABI and RTMA data to cut low-level temperature and moisture profile RMSE by over 30% versus ERA5.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Institute of Atmospheric Physics, Chinese Academy of Sciences
Multitask local linear regression predicts China precipitation 2–6 weeks ahead with skill 0.11, rising to 0.13 when the Arctic Oscillation index is added.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)Institute of Atmospheric Physics, Chinese Academy of SciencesBaidu
An LSTM with rolling forecasts predicts typhoon intensity up to 120 h from historical data and outperforms a feed-forward network on 2015 typhoons.
Extremes & hazardsInstitute of Atmospheric Physics, Chinese Academy of Sciences
A CNN-BiConvLSTM reconstructs missing weather radar frames, outperforming interpolation, optical flow and ConvLSTM baselines across missing patterns.
Observations & remote sensingInstitute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Random Subfeature Ensemble Learning (RSEL) combines random feature subsets and ensembling to improve wind speed and direction forecasts over traditional methods, with claimed robustness.
Local & station forecastingInstitute of Atmospheric Physics, Chinese Academy of Sciences
Machine learning post-processing of model output to produce grid-point temperature forecasts for the Beijing area.
Post-processing & calibrationInstitute of Atmospheric Physics, Chinese Academy of Sciences
