111 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)
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
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
A CNN with cosine-distance loss predicts equatorial Pacific SST zonal patterns up to 16 months ahead, outperforming dynamical models, with physically interpreted ENSO precursors.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
SNCA-CLDASSD, a Swin cross-attention network with DEM input, downscales CLDAS air temperature from 0.05° to 0.01° and beats interpolation and super-resolution baselines in mountains.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)
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)
A CNN trained on daily precipitation from climate models detects emerging anthropogenic signals in observed rainfall since the mid-2010s.
Climate analysis & attributionNanjing University of Information Science & Technology (NUIST)University of Washington
U-Net calibrates GEFS 10-m wind forecasts over Southwest China, raising the share of errors under 0.5 m/s by 46-50% at day 1 and beating CNN and linear baselines.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)
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)
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)
A CNN (PCINet) identifies day and night precipitation clouds from FY-4A imagery, outperforming five segmentation baselines and enabling near-real-time monitoring.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
DABU-Net, a data-augmented deep learning correction of GFS wintertime precipitation over southeastern China, cuts RMSE by 19–25% at 24–72 h.
Post-processing & calibrationTsinghua University (NowcastNet)Nanjing University of Information Science & Technology (NUIST)
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
LSTMAtU-Net combines ConvLSTM with U-Net and an attention module to improve 0-2 hour radar precipitation nowcasts, especially for medium and high intensities.
NowcastingNanjing University of Information Science & Technology (NUIST)
Dynamic weight loss function added to ConvLSTM, ConvGRU, PredRNN and PredRNN++ reduces radar echo extrapolation MAE by up to 14.8% over 1 hour.
NowcastingNanjing University of Information Science & Technology (NUIST)
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)
CLGAN, a GAN with U-Net and LSTM generator, improves heavy-precipitation nowcasts up to 2 h over PredRNN-v2 and optical flow baselines.
NowcastingNanjing University of Information Science & Technology (NUIST)Forschungszentrum Jülich / Jülich Supercomputing Centre
Dynamic-balance CNN estimates tropical cyclone intensity from satellite imagery with 8.32 kt RMSE, 35% lower than the Dvorak technique.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
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)
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)
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
Rotation-equivariant convolution combined with a Transformer estimates tropical cyclone intensity from multi-platform satellite and environmental data, with RMSE of 8.19 kt.
Extremes & hazardsNanjing 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
Neural network sub-grid cloud fraction scheme trained on CloudSat data reaches r > 0.9 and reduces high- and low-level cloud biases in GCM simulations.
Hybrid physics-ML & parameterizationFudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)
A two-step U-Net (TU-Net) predicts 4-month-lead summer precipitation over the Yangtze and beats most NMME dynamical models on correlation and RMSE.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
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)
LSTM post-processing uses MJO and jet-stream quasi-periodic signals to weight operational S2S ensembles for Asian minimum 2 m temperature.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
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
3D-Geoformer, a self-attention transformer, predicts Niño3.4 SST anomalies 18 months ahead with high correlation skill for ENSO.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
LSTM outperforms feed-forward networks for predicting monthly maximum temperature at Kaba Aye, Myanmar, with MAE of 1.33 degrees.
Local & station forecastingNanjing University of Information Science & Technology (NUIST)
A U-Net replaces EOF models for precipitation response to SST anomalies in hybrid ENSO modelling, outperforming EOF over the western-central tropical Pacific.
Hybrid physics-ML & parameterizationNanjing University of Information Science & Technology (NUIST)
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)
Proposes MS-ConvLSTM, adding echo top, VIL and radar-retrieved winds to reflectivity inputs to improve precipitation nowcasts over conventional deep learning baselines.
NowcastingNanjing University of Information Science & Technology (NUIST)
Review of deep learning for weather radar analysis covering beam blockage correction, echo extrapolation and precipitation nowcasting, with open challenges.
NowcastingNanjing University of Information Science & Technology (NUIST)
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)
A divided 3D CNN (TS-3DCNN) predicts the 2020–22 multi-year La Niña, comparable to a dynamical intermediate coupled model.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
Random Forest, GAM and LightGBM map landslide susceptibility under CMIP6 warming, finding high-susceptibility zones grow by up to 4.0% at 4 °C warming.
Applications & impactsNanjing University of Information Science & Technology (NUIST)
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)
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)
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)
NFC-Net fuses FY-4A satellite, DEM and ERA5 data with a UNet to correct ECMWF forecasts, cutting 2m temperature and 10m wind speed RMSE by about 50%.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)
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)
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
Lightweight multitask CNN estimates tropical cyclone intensity and size from satellite data, with intensity RMSE of 8.40 kt, 33.5% below the ADT baseline.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
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)
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)
SHAP-explained SVM, RF, CNN and LSTM forecast climatic water balance 1-3 months ahead over Bangladesh; SVM performs best with antecedent CWB inputs.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
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)
UTrans-Net adds a transformer to a U-Net to predict short-term precipitation from prior observations, reaching 59%, 78% and 82% accuracy at 0.1, 3 and 100 mm thresholds.
NowcastingNanjing University of Information Science & Technology (NUIST)
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)
A CNN trained on CMIP5/6 models forecasts eastern Indian Ocean Dipole SST anomalies up to 6 months ahead, matching the ACCESS-S2 dynamical model.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)Bureau of Meteorology (Australia)
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)
GSFM uses SE-ResNet to predict intraseasonal Z500 and T850 components, substantially outperforming CFSv2 at 10–30 day leads.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
Cluster-wise typhoon rainfall model combines SVM, RF, AdaBoost, CNN and LSTM with grid-search tuning, claiming 45–90% forecast efficiency gains for 1–6 h lead times.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
Self-attention spatiotemporal LSTM extrapolates edge-radar echoes for nowcasting in an Internet of Radars setting; the abstract is missing, so the assessment relies on the title.
NowcastingNanjing University of Information Science & Technology (NUIST)
CEMA-LSTM adds contextual correlation and attention blocks to recurrent radar extrapolation, improving POD, FAR and CSI over PhyDNet and PredRNN++.
NowcastingNanjing University of Information Science & Technology (NUIST)
GAN with sub-pixel convolution performs 8x super-resolution of ERA5 temperature fields, improving RMSE 37% over bicubic interpolation.
Downscaling & super-resolutionIBM ResearchNanjing University of Information Science & Technology (NUIST)
Feature-constrained Zooming Slow-Mo network spatiotemporally downscales rain radar fields, outperforming U-Net variants and interpolation on a public radar dataset.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)
A dendritic neural network corrects CWRF summer precipitation forecasts against station data, raising anomaly correlation by 0.1 and cutting MSE by about 26%.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)
Neural network retrieves ocean surface wind, SST and near-surface air temperature and humidity from AMSR2, with heat flux RMSE of 18-40 W/m2 against buoys.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
A CNN using Northeast Pacific SST predicts North China winter temperature with 0.65 correlation at 5-month lead, beating a state-of-the-art dynamical seasonal system.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
A deep learning model fuses 25 CMIP6 ESMs and downscales soil moisture to 0.25° over China, outperforming individual models and the multi-model mean.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)
A deep autoencoder detects anomalous heavy precipitation events in Japan and attributes their increase since 1977 partly to anthropogenic forcing, with more expected this century.
Climate analysis & attributionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARPrinceton UniversityNanjing University of Information Science & Technology (NUIST)
Tests seven optimizers and eight learning rates for a CNN forecasting hourly typhoon rainfall 1–6 h ahead; moderate rates (0.1–0.001) work best.
NowcastingNanjing University of Information Science & Technology (NUIST)
Random forest forecasts monthly extreme rainfall indices over Bangladesh 1–3 months ahead using CAPE, humidity, and ENSO/IOD/NAO teleconnections.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
BLSTM-attention and BLSTM-Transformer models predict wind speed at seven Qingdao stations, cutting RMSE by about 44% versus ECMWF forecasts.
Local & station forecastingNanjing University of Information Science & Technology (NUIST)
MCCS-LSTM adds criss-cross attention and multi-scale spatiotemporal blocks to radar echo extrapolation, improving accuracy at longer lead times on CIKM and Guangdong radar data.
NowcastingNanjing University of Information Science & Technology (NUIST)
Mahalanobis transform K-near-means algorithm retrieves boundary layer height from micropulse LiDAR under cloudy conditions, matching radiosonde references better than three baselines.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
Proposes a two-stream convolutional LSTM for radar-based precipitation nowcasting, combining two input streams to improve echo map prediction.
NowcastingNanjing University of Information Science & Technology (NUIST)
A deep learning model trained on image-like wind fields around PIREP turbulence reports predicts Japanese airspace turbulence with accuracy comparable to existing grid-point indices.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
Super-resolution deep learning merges 31 CMIP6 models into a 0.5° monthly near-surface temperature dataset (1850–2100) with bias correction and downscaling.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)
3D-attention CNN estimates tropical cyclone intensity from multichannel IR, water vapour and microwave imagery, with RMSE 9.48 kt, 25% better than the Advanced Dvorak Technique.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
Random forest predicts Yangtze summer precipitation with 0.473 skill from December, outperforming eight climate models in several respects.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
Generalised additive and machine learning models link North Indian Ocean tropical cyclone activity to boreal summer intraseasonal oscillation; no abstract was available, so this rests on the title.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
A multitask CNN estimates global tropical cyclone precipitation from HURSAT-B1 imagery, reaching 0.68 detection probability and beating MLR and random forest baselines.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
ML diagnostic model explains over 80% of monthly burnt-area variance in southeast Australian forests, showing extreme fire weather, linked to SAM and IOD, as the primary driver.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
Random forest combines CMIP6 multi-model ensembles over China, projecting less warming and wetter conditions with better spatial skill than the arithmetic mean.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)
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)
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)
K-means clustering and C4.5 decision trees link Poyang Lake basin precipitation clusters to preceding spring large-scale circulation indices.
Climate analysis & attributionNanjing University of Information Science & Technology (NUIST)
DC-LSTM, a dense-convolution LSTM, forecasts ENSO Nino3.4 up to 20 months ahead with higher skill than a dynamical model over 1994-2010.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
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)
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
CNN-based fusion of satellite and ERA5 predictors detects 97.1% of developing tropical cyclones in the western North Pacific at a 20.3% false alarm rate.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
MeteCNN classifies 11 weather phenomena in a new 6,877-image dataset, reaching about 92% test accuracy.
Observations & remote sensingFudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)
LSTM with correlation-based feature selection predicts hourly rainfall at Axim, Ghana, using ECMWF and station data, with MSE of 0.002.
Local & station forecastingNanjing University of Information Science & Technology (NUIST)
SCENT pairs sparse correspondence tracking with a deep network for precipitation nowcasting; abstract unavailable, so the classification rests on the title.
NowcastingNanjing University of Information Science & Technology (NUIST)
Deep convolutional autoencoder emulates statistical downscaling of precipitation projections over Northern Africa; abstract unavailable.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)
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)
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)
Applies a temporal convolutional network with transfer learning to forecast offshore visibility at Haikou Station, improving 24 h forecast scores and beating the CUACE model on TS score by 0.16.
Local & station forecastingNanjing University of Information Science & Technology (NUIST)
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)
A quasi-supervised Mask R-CNN reidentifies Northern Hemisphere extratropical cyclones, finding 8.29% more cyclones than the traditional automatic method.
Climate analysis & attributionNanjing University of Information Science & Technology (NUIST)
CNN statistical downscaling of daily temperature and precipitation over China outperforms GLM and BCSD/BCCI for precipitation, though ERA-Interim is better for temporal correlation.
Downscaling & super-resolutionNanjing University of Information Science & Technology (NUIST)
Hybrid CNN with classification and BP network estimates tropical cyclone intensity from satellite images with RMSE of 8.91 kt.
Extremes & hazardsFudan University (FuXi)Nanjing University of Information Science & Technology (NUIST)
A two-branch CNN estimates northwest Pacific tropical cyclone intensity from infrared and water vapour images, reaching RMSE 5.13 m/s with 60×60 pixel inputs.
Extremes & hazardsNanjing University of Information Science & Technology (NUIST)
Deep learning model trained on climate model simulations produces multi-year ENSO forecasts, a landmark result for long-lead ENSO prediction.
Sub-seasonal to seasonalNanjing University of Information Science & Technology (NUIST)
Real-time deep learning detector locates typhoon eyes in images for meteorological information; the abstract is unavailable, so accuracy is unknown.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
Surveys artificial neural network methods for rainfall prediction and argues they improve accuracy over traditional NWP and statistical models.
Post-processing & calibrationNanjing University of Information Science & Technology (NUIST)
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)
Applies deep belief networks to precipitation forecasting, claiming better results than conventional forecasting methods; quantitative results are not given in the abstract.
Local & station forecastingNanjing University of Information Science & Technology (NUIST)
A neural network method fuses spaceborne and ground radar reflectivity into three-dimensional fields. The abstract is missing, so the assessment relies on the title.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
ContrailMod, a CNN applied to Himawari-8 imagery, outperforms a conventional contrail detection algorithm and is used to evaluate potential contrail coverage from ERA-Interim over South China.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)
Survey of artificial neural network methods for rainfall prediction, arguing they outperform NWP and statistical models for nonlinear precipitation dynamics.
Local & station forecastingNanjing University of Information Science & Technology (NUIST)
