133 papers in the archive.
FuXi Weather is an ML system that assimilates multi-satellite observations and produces 10-day global forecasts, outperforming ECMWF HRES in observation-sparse regions like central Africa.
Data assimilationChina Meteorological Administration (CMA)Fudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)Tsinghua University (NowcastNet)University of California, Los Angeles (UCLA)
FuXi-ENS, a VAE-based ensemble trained with CRPS and KL loss at 0.25°, beats ECMWF ENS in CRPS on 98.1% of 360 variable and lead time combinations.
Probabilistic & ensemble forecastingChina Meteorological Administration (CMA)Fudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)
FuXi-DA is a deep learning data assimilation framework that assimilates FY-4B AGRI satellite radiances, reducing analysis errors and improving forecasts.
Data assimilationChina Meteorological Administration (CMA)Fudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)
YingLong is a 3 km AI limited-area model that beats WRF-ARW on surface wind speed in two domains while running much faster, using Pangu-Weather boundaries.
Regional & km-scale modelsBaiduChina Meteorological Administration (CMA)
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
Machine learning S2S model outperforms ECMWF's S2S system for precipitation and OLR up to 42 days, extending MJO prediction skill from 30 to 36 days.
Sub-seasonal to seasonalChina Meteorological Administration (CMA)Fudan University (FuXi)
ML-derived ventilation parameters in a simple dynamical model show deep-layer vertical wind shear dominates tropical cyclone weakening across basins during 1982–2021.
TaNet, a transformer, estimates near-surface air temperature from FengYun-4A imagery, reaching correlation 0.990 with ERA5 and 0.959 with stations.
Observations & remote sensingChina Meteorological Administration (CMA)
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)
3D CNN forecasts precipitation 4 h ahead from FY-4A multichannel satellite imagery, beating NWP on critical success index (0.25 vs 0.21).
DSRSS-Net jointly super-resolves and segments FY-4A imagery for snow-cover mapping on the Tibetan Plateau, improving classification accuracy by 4.45% over MOD10A1.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
An LSTM forecasts the Oceanic Niño Index 18 months ahead, with the extratropical western North Pacific predictor giving the best skill and beating linear regression.
Sub-seasonal to seasonalChina Meteorological Administration (CMA)
LSTM nowcasts 10-minute rainfall fields reconstructed from 26 commercial microwave links, reaching correlation 0.91 at one minute and falling to 0.20 at ten.
Reviews seasonal and multi-annual tropical cyclone forecasting, including emerging ML/AI methods, ENSO-neutral predictability and landfall skill gaps relative to user needs.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARMet OfficeMétéo-France (incl. CNRM)Colorado State University (incl. CIRA)China Meteorological Administration (CMA)Japan Meteorological Agency (JMA)Bureau of Meteorology (Australia)
LightGBM with SHAP explanations predicts tropical cyclone disaster loss grades in China with 0.86 accuracy, with rainfall and wind as the main drivers.
Applications & impactsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
WEY-TCNet combines wavelet fusion and YOLOX with channel attention to detect tropical cyclones in Fengyun-2D infrared images, reaching 91.5% mAP versus 86.7% baseline.
NowcastNet unifies advection physics with learned conditional generation for extreme precipitation nowcasts up to 3 hours, ranked first by meteorologists in 71% of cases.
NowcastingChina Meteorological Administration (CMA)Tsinghua University (NowcastNet)University of California, Berkeley
Multi-scale deep learning framework with attention and a spatiotemporal loss improves 1-hour storm nowcast skill scores by 8.5–42.6% on SEVIR.
Physics-constrained deep learning (CGsNet) nowcasts convective wind gusts from radar at 0.01° and 6-minute resolution for 0-2 h, beating NWP-based nowcasting.
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)
End-to-end DNN, CNN and CNN-LSTM models detect clouds in FY-3D HIRAS infrared hyperspectral soundings, with CNN and CNN-LSTM reaching 0.96 accuracy.
Observations & remote sensingChina Meteorological Administration (CMA)
CNN combines multi-GCM ensembles to improve monthly precipitation predictions at local scales over China compared with other methods.
Post-processing & calibrationChina Meteorological Administration (CMA)
An ensemble of 20 segmentation networks detects atmospheric rivers with 1.7–10.1% higher IoU; CMIP6 projections show AR-driven extreme precipitation rising under SSP5-8.5.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Downscales GPM satellite precipitation from 0.1° to 1 km using a BP neural network with terrain and vegetation predictors, improving accuracy in complex terrain with sparse gauges.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
Deep learning nowcasts 6-minute precipitation up to 2 h over Guizhou from station data; PredRNN beats optical flow at all tested thresholds.
NowcastingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
W-MAE pretrains a masked autoencoder on ERA5 and fine-tunes it for forecasting, lifting FourCastNet's anomaly correlation by about 20 percent.
Residual attention back-projection network super-resolves weather radar base echoes, outperforming compared CNN methods visually and quantitatively in echo detail and edges.
Observations & remote sensingChina Meteorological Administration (CMA)
Dual-polarization radar nowcasting of severe convective weather combines physical constraints with a deep neural network; abstract unavailable, so details are limited.
A sensor-adaptive leaf area index retrieval for FY-3D MERSI-II combines radiative transfer with a neural network backup and agrees well with MODIS (R2 0.85).
Observations & remote sensingChina Meteorological Administration (CMA)
SeaUnet, a fully convolutional network trained on CMIP TC tracks, gives skillful seasonal forecasts of Western North Pacific typhoon track density up to four months ahead.
Sub-seasonal to seasonalTsinghua University (NowcastNet)University of ReadingChina Meteorological Administration (CMA)
XGBoost corrects FIO-CPS ENSO forecasts, raising Niño3.4 anomaly correlation at 7 to 13 month leads from 0.40 to 0.63 and cutting RMSE by 25%.
Sub-seasonal to seasonalChina Meteorological Administration (CMA)
Stacking ensemble of neural networks corrects ECMWF-IFS maximum temperature forecasts at about 2400 stations, cutting RMSE by 52% versus IFS.
Post-processing & calibrationChina Meteorological Administration (CMA)
Random forests using wind profiler divergence and vorticity predict rainfall onset 30 minutes ahead, outperforming inputs derived from ERA5 reanalysis.
NowcastingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
CNN and FNN multi-model ensembles of ECMWF and CMA forecasts reduce 12-72 h surface air temperature errors in Henan, with the CNN best at short range.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
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)
Machine learning examines basin-dependent tropical cyclone genesis environments and their projected future changes; the abstract is unavailable, so this is judged from the title.
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)
A conditional GAN retrieves CloudSat 94 GHz radar reflectivity from MODIS cloud products over global oceans, performing best for deep convective clouds.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Multi-task learning models nowcast convective storm vertically integrated liquid and lightning using satellite and weather radar observations; abstract not provided.
NowcastingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Random forest retrieves Himawari-8 AHI water vapor 35–45% more accurately than 1DVAR at much lower cost, demonstrated for Typhoon Maria.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Grid-to-point deep learning corrects 3 km PRUFS forecasts at 311 stations, cutting RMSE 19% for temperature, 24% for humidity and 42% for wind.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
A deep CNN estimates site-specific surface wind speed from gridded ECMWF ERA5 across eastern China, reducing RMSE 23–26% versus linear interpolation.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
CNN and GRU models forecast East China rainfall anomalies at 10–30 day leads, beating CMA and ECMWF dynamical models in skill and extreme-event scores.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Machine-learning correction of ECMWF and grid forecasts for Shenyang station maximum and minimum temperature improves accuracy by up to 186% in some seasons.
Post-processing & calibrationChina Meteorological Administration (CMA)
STUNNER uses a two-stream spatiotemporal fusion network with temporal differencing for radar echo extrapolation, achieving best results on two public datasets.
NowcastingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
HST-AFP fuses radar echoes with NWP data via attention-based multi-encoder and fusion decoder, outperforming RNN and U-Net nowcasting baselines in South China.
Random Forest downscales the summer temperature-humidity index from 6 km to 1 km over Anhui, achieving correlations above 0.97 with site observations.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
DenseNet-based fusion of infrared and water vapor satellite images, with a tropical-cyclone-specific loss, improves TC center location accuracy.
Observations & remote sensingChina Meteorological Administration (CMA)
CWNNet, a ConvRNN fusing Fengyun-4A satellite and radar inputs, predicts 0-1 h reflectivity and convective initiation, outperforming single-source models (POD 0.87 at 1 h).
Terrain-guided attention CNN downscales 2 m air temperature from 0.0625° to 0.01° over Southwest China, reaching 1.12 °C RMSE using DEM attention blocks.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
A 3D DBSCAN clustering workflow detects the 2022 Yangtze heatwave and drought from ERA5 and tests how clustering parameters change event tracking.
Extremes & hazardsFudan University (FuXi)China 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)
Neural networks reconstruct daily and monthly 780 m Greenland Ice Sheet air temperature from MODIS land surface temperature, reaching R of 0.96 against automatic weather stations.
Observations & remote sensingChina Meteorological Administration (CMA)
Study of loss functions to counter class imbalance when deep learning corrects precipitation forecasts; abstract unavailable, so based on title only.
Post-processing & calibrationChina Meteorological Administration (CMA)
Survey of AI methods for ENSO forecasting, reviewing deep learning approaches since Ham et al. (2019) and comparing them with traditional statistical and dynamical forecasts.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
T-UNet combines UNet with TrajGRU for radar echo extrapolation, improving CSI and HSS by up to 10.57% and 7.80% over 60-minute nowcasts.
A ConvGRU with CBAM attention and a Deep&Cross CNN fusion forecasts tropical cyclone tracks from CMA and ERA-Interim data, outperforming prior deep learning baselines.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
CNN post-processing of GEFS re-forecasts beats CSG EMOS and raw ensembles for 1–7 day China precipitation, especially for extremes above 50 mm/day.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Deep learning on Himawari-8 and FY-4 imagery identifies typhoon vortices (88.6% accuracy above strong tropical storm grade) and estimates intensity with 3.8 m/s MAE.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Machine learning ensemble of 27 CMIP6 models improves China extreme precipitation simulation and projects increases of up to 13.7% by late century.
Post-processing & calibrationChina 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)
Bayesian-optimised gradient boosted trees predict western North Pacific tropical cyclone intensity change at 12–72 h, beating linear regression by 4–8% in R².
LightGBM uses preceding-winter sea surface temperature, soil moisture and snow to simulate summer eastern European heatwave frequency, with SST contributing most.
Genetic-algorithm-optimized backpropagation network forecasts summer precipitation in the Yangtze-Huaihe basin, reaching MAPE near 20% and beating SVM and linear regression.
Sub-seasonal to seasonalChina Meteorological Administration (CMA)
Hierarchical clustering separates northward-moving typhoon tracks affecting Northeast China and links each type to preceding Pacific SST anomalies, with numerical experiments confirming the mechanism.
Climate analysis & attributionChina Meteorological Administration (CMA)
A ConvLSTM encoder-decoder with spatial attention forecasts 24 h western North Pacific tropical cyclone tracks from satellite images, about 30 km better than ECMWF-HRES and GFS.
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)
A CNN-LSTM predicts cloud-cluster evolution from geostationary IR imagery to rolling-correct 1-6 h GRAPES_Meso precipitation forecasts over China, improving rain/no-rain scores.
Post-processing & calibrationChina Meteorological Administration (CMA)
A multi-modal spatio-temporal deep neural network for meteorological forecasting; the abstract is missing, so the topic is inferred from the title.
Global medium-range NWPChina Meteorological Administration (CMA)
Deep neural network ensemble for tropical cyclone track forecasting; abstract unavailable, so no quantitative results can be reported.
Deep learning model forecasts quantitative precipitation from basic NWP variables; no abstract available, so the method and gains are unverified.
Post-processing & calibrationChina 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)
A deep learning model pre-trained on GCM hindcasts and transfer-learned on ERA5 raises seasonal China precipitation correlation by 0.10-0.13 over raw GCM output; used operationally for guidance.
Sub-seasonal to seasonalTsinghua University (NowcastNet)China Meteorological Administration (CMA)
Multi-site collaborative deep learning for wind speed forecasting in valley complex terrain; abstract unavailable, judged from title only.
Local & station forecastingChina Meteorological Administration (CMA)
A GAN with convolutional recurrent units extrapolates Shenzhen radar echoes, raising POD and CSI by 5.5% and 10.4% over rgcPredNet.
Two machine learning models forecast summer Eurasian heatwave frequency from preceding SST, snow, soil moisture and sea ice, outperforming linear regression in 2010–2019 forecasts.
A 3D CNN combined with a GRU forecasts tropical cyclone tracks, aiming to improve track prediction over conventional approaches.
Introduces QpefBD, a minute-scale radar-based benchmark dataset for machine learning precipitation estimation and forecasting (abstract not provided; classified from title).
Benchmarks, datasets & evaluationChina Meteorological Administration (CMA)
Light-CLDASSD, a lightweight deep model with station data, downscales temperature from 1/16° to 0.01° and beats HRCLDAS by 0.08 °C RMSE.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
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)
Uses a MaxEnt model on environmental variables to project a significant decline in future tropical cyclone genesis across CMIP6 SSP scenarios, with a nonlinear link to potential intensity.
Extremes & hazardsTsinghua University (NowcastNet)China Meteorological Administration (CMA)
An LSTM-based health monitoring framework for CINRAD-SA weather radar flags early faults from deviations between predicted and measured monitoring parameters, reducing false alarms.
Observations & remote sensingTsinghua University (NowcastNet)China Meteorological Administration (CMA)
Proposes M-ConvGRU, a convolutional GRU with an encoder-forecaster design for radar echo prediction, cutting MAE 6.29% and MSE 10.25% versus ConvLSTM.
SVM and general regression neural network models estimate western North Pacific tropical cyclone size parameters (RMW, R34, R50, R64) from infrared satellite imagery.
Observations & remote sensingChina Meteorological Administration (CMA)
Introduces InstGCN, an inductive spatiotemporal graph network for radar-based short-term rainfall forecasting, with a novel elliptic graph construction and subgraph decomposition for unseen nodes.
Nonlocal residual CNN super-resolves weather radar echo images at x2 and x4, outperforming interpolation in quantitative metrics and edge detail.
Observations & remote sensingChina Meteorological Administration (CMA)
A CNN remaps FY-3D MWRI brightness temperatures to NWP grids, reducing observation minus background bias and spread in GRAPES 4D-Var.
Observations & remote sensingChina Meteorological Administration (CMA)
A deep residual CNN with dropout and transfer learning extends ENSO Niño3.4 forecast skill to a 20-month lead, three months beyond the previous optimal model.
Sub-seasonal to seasonalChina Meteorological Administration (CMA)
LightNet+ fuses radar, station and NWP data in a deep network for lightning forecasting, beating three established schemes and improving as data sources are added.
Extremes & hazardsFudan University (FuXi)China Meteorological Administration (CMA)
ATMConvGRU adds axial attention memory to ConvGRU for sequential weather prediction of temperature, humidity, wind and radar echo, outperforming state-of-the-art methods.
AutoML post-correction of WRF forecasts at 13 Xinjiang stations cuts temperature RMSE from 3.24 to 2.34 °C and humidity RMSE from 19.54% to 11.54%.
Post-processing & calibrationChina Meteorological Administration (CMA)
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)
QPENet deep learning on dual-polarization radar, trained on 11 typhoons, beats DSD-based parametric QPE for rainfall estimation in South China.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)Colorado State University (incl. CIRA)China 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)
CU-net, a CNN, corrects gridded ECMWF-IFS 24–240 h forecasts of four surface variables over North China, beating the ANO correction at all lead times.
Post-processing & calibrationColorado State University (incl. CIRA)China Meteorological Administration (CMA)
Backpropagation neural network estimates the Holland B wind-field parameter for Northwest Pacific typhoons from pressure difference, improving super-typhoon wind speed estimates over statistical models.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
DR-Transformer estimates tropical cyclone intensity from satellite images with rotation and distance-consistency features, reaching 7.76-knot RMSE on the TCIR dataset.
A deep learning regression retrieves radar composite reflectivity maps from Fengyun-4A geostationary satellite data, with R² of 0.88-0.96 and MAE of 0.3-0.6 dBZ.
Observations & remote sensingChina Meteorological Administration (CMA)
Survey of deep learning methods, including RNNs and CNNs, for predicting extreme weather events.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Neural networks retrieve Arctic temperature and humidity profiles from FY-3D HIRAS under clear skies, closer to ERA5 than AIRS products.
Observations & remote sensingChina Meteorological Administration (CMA)
Generalized regression neural network calibrates MODIS and ERA5 precipitable water vapor against GNSS over China, improving accuracy most at monthly timescales.
Observations & remote sensingChina Meteorological Administration (CMA)
DeepDT trains a GAN with separate generator and discriminator steps to reduce artifacts in CNN-based climate downscaling, outperforming most CNN baselines.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
Combines WUDAPT urban morphology data with machine learning to spatially estimate heatwave patterns; the abstract is unavailable, so the details are limited.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
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)
Machine-learning bias correction of 2016 smartphone pressure data reduces bias, and dense analysis resolves convective structure in a Beijing hailstorm better than station data.
Observations & remote sensingNSF NCAR / UCARChina Meteorological Administration (CMA)
A 3-D CNN plus conditional GAN postprocessor sharpens radar echo extrapolations, improving intense echo and convective prediction over North China in 2015–2016.
A cascaded CNN classifies and estimates northwest Pacific tropical cyclone intensity from infrared satellite images, reaching 8.60 kt RMSE against best-track data.
CNN retrieves near-real-time precipitation from FengYun-2 satellite imagery over Xinjiang, China; no abstract was available, so this summary is based on the title.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Fully convolutional learnable optical-flow network extrapolates six years of Guangdong radar mosaics, outperforming optical flow and other deep models for convective nowcasting.
A transfer-learned EfficientNet-b6 CNN classifies airborne cloud-probe ice-crystal images with 98% average precision, supporting cloud microphysics research.
Observations & remote sensingChina Meteorological Administration (CMA)
Compares six ML models for near-surface air temperature from FY-4A satellite data; XGBoost gives R² 0.902 and RMSE 1.946 °C.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Applies GANs and deep belief networks to improve ECMWF short-term intense rainfall forecasts; the abstract is missing, so reported gains are unverified.
Post-processing & calibrationChina Meteorological Administration (CMA)
Graph-based multicell features with a random forest predict next-hour short-term intense precipitation in North China with 79.2% POD and 68.3% CSI.
End-to-end CNN post-processing (MODL) of TIGGE ECMWF forecasts improves Tianjin 2 m temperature RMSE over ECMWF, MOS and other ML baselines, especially in the first three days.
Post-processing & calibrationChina Meteorological Administration (CMA)
XGBoost and statistical analysis of ERA-Interim identify relative intensity, sea-surface temperature drop and wind shear as dominant drivers of western North Pacific typhoon weakening.
Artificial neural networks retrieve temperature and humidity profiles from FY-4 GIIRS hyperspectral radiances, outperforming a simulation-trained network and matching AIRS consistency.
Observations & remote sensingChina Meteorological Administration (CMA)
LightningNet, a 3D convolutional encoder-decoder fusing Himawari-8, radar and lightning data, gives 0-1 h cloud-to-ground lightning nowcasts over China with threat score 0.453.
Proposes a task-based graph convolutional network for ground-based cloud classification and releases a large labeled sky-image database (GRSCD).
Observations & remote sensingChina Meteorological Administration (CMA)
Introduces Label Distribution Learning with Climate Probability to post-process ensemble precipitation forecasts in East China, improving RMSE and CRPS over baselines.
Post-processing & calibrationChina Meteorological Administration (CMA)
Retraction notice for a CNN-based ground-based cloud image recognition system that duplicated prior work; the underlying topic is cloud classification.
Observations & remote sensingChina Meteorological Administration (CMA)
ResLap applies a residual-dense Laplacian pyramid super-resolution network to downscale China Meteorological Administration climate data and releases datasets.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
Himawari-8 brightness temperatures and NWP predictors estimate summer air temperature over China; a neural network reaches R=0.97 and RMSE 1.72°C against stations.
Observations & remote sensingChina Meteorological Administration (CMA)
A multi-scale deconstructed ConvLSTM encoder-decoder forecasts precipitation from multiple meteorological fields on EC and CM datasets, combining NWP data with deep learning.
Deep belief network retrieves horizontal visibility from MODIS aerosol optical depth and ECMWF data over Eastern China, with validation correlation near 0.70.
Observations & remote sensingChina Meteorological Administration (CMA)
Nowcasts convective initiation 17-40 minutes ahead from FY-4A satellite data using optical flow and BP-AdaBoost, reaching detection probability 0.80 in South China.
Cubist regression downscales PERSIANN-CDR to 1 km and calibrates it, yielding 1983–2015 Tibetan Plateau precipitation with bias reduced from 41% to about 5%.
Downscaling & super-resolutionChina Meteorological Administration (CMA)
Deep learning approach to forecasting different convective weather types; the abstract is missing, so details are limited.
GAN-based super-resolution of degraded weather radar echo images produces sharper reconstructions with higher PSNR and SSIM than classical methods.
Observations & remote sensingChina Meteorological Administration (CMA)
Random forest on Himawari-8 brightness temperatures and NWP water vapour classifies weak, medium and severe pre-convective storms over East Asia with 0.79 accuracy.
Combines wavelet decomposition and machine learning to improve local precipitation forecasts from the NMME multi-model seasonal ensemble.
Post-processing & calibrationChina Meteorological Administration (CMA)
Random forest estimates summertime precipitation from Himawari-8 infrared brightness temperatures plus GFS predictors, reaching 0.87 classification accuracy and 2.0 mm/h RMSE against GPM.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA)
Deep learning similarity search to retrieve historical tropical cyclones matching a query storm; abstract missing, so details are inferred from the title.
Neural network parameterizations of typhoon-induced sea surface cooling reduce maximum wind intensity errors by 60–70% in four atmosphere-only typhoon forecast cases.
Hybrid physics-ML & parameterizationMassachusetts Institute of Technology (MIT)China Meteorological Administration (CMA)
