Awesome-Weather-Forecast

An algorithm that automatically obtains data from a remote server on a regular basis, automatically loads models and makes forecasts.

To Do

• [✅] Create ⏳Recently Focused Papers

Content

• Medium Range:

  • ⭐(Nature 2023) FuXi, chen2023fuxi et al. [Paper]
    • 🍬 FuXi: the first : A cascade machine learning forecasting system for 15-day global weather forecast.
  • ⭐(Nature 2023) Pangu,[Paper]
    • 🍬 Pangu: the first : Accurate medium-range global weather forecasting with 3D neural networks.
  • ⭐(Science 2023) GraphCast, [Paper]
    • 🍬 GraphCast: the first : Learning skillful medium-range global weather forecasting.
  • ⭐(Arxiv preprint 2023) FengWu, [Paper]
    • 🍬 GraphCast: the first : FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead.
  • ⭐(Website 2023) ClimaX, [Paper]
    • 🍬 ClimaX: the first : The first foundation model for weather and climate.
  • ⭐(Arxiv preprint 2023) W-MAE, [Paper]
    • 🍬 W-MAE: the first : W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting
  • ⭐(Arxiv preprint 2023) SAFNO, [Paper]
    • 🍬 SAFNO: the first : Spherical Fourier Neural Operators:Learning Stable Dynamics on the Sphere
  • ⭐(Arxiv preprint 2022) FourCastNet, [Paper]
    • 🍬 FourCastNet: the first : FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

• NowCasting:

  • Precipitation:
    • ⭐(Nature 2023) NowcastNet, [Paper]
      • 🍬 NowcastNet: the first : Skilful nowcasting of extreme precipitation with NowcastNet
    • ⭐(Nature 2023) Corrformer, [Paper]
      • 🍬 Corrformer: the first : Interpretable weather forecasting for worldwide stations with a unified deep model
    • ⭐(Nature 2023) SRNDiff [SRNDiff]
      • 🍬 SRNDiff: : Short-term Rainfall Nowcasting with Condition Diffusion Model
    • ⭐(Nature 2023) SRNDiff [SRNDiff]
      • 🍬 SRNDiff: : Short-term Rainfall Nowcasting with Condition Diffusion Model
    • ⭐(Arxiv preprint 2023) MetNet3, [Paper]
      • 🍬 MetNet3: the first : Deep Learning for Day Forecasts from Sparse Observations
    • ⭐(Arxiv preprint 2023) MetNet2, [Paper]
      • 🍬 MetNet2: the first : Deep learning for twelve hour precipitation forecasts
    • ⭐(Arxiv preprint 2023) MetNet, [Paper]
      • 🍬 MetNet: the first : MetNet: A Neural Weather Model for Precipitation Forecasting
    • ⭐(Arxiv preprint 2023) PreDiff, [Paper]
      • 🍬 PreDiff: the first : PreDiff: Precipitation Nowcasting with Latent Diffusion Models
    • ⭐(Arxiv preprint 2023) DGMR, [Paper]
      • 🍬 DGMR: the first : Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification
    • ⭐(AAAI) SwinRDM, [Paper]
      • 🍬 SwinRDM: the first : SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting
    • ⭐(MDPI) GAN, [Paper]
      • 🍬 GAN: the first : Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method
    • ⭐(GRL) ω-GNN, [Paper]
      • 🍬 ω-GNN: the first : Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network
    • ⭐(GRL) MultiScaleGAN, [Paper] [Code]
      • 🍬 MultiScaleGAN: the first : Experimental Study on Generative Adversarial Network for Precipitation Nowcasting

• Seasonal:

  • ⭐(Arxiv preprint 2023) S2S, [Paper]
    • 🍬 *FuXi-S2S: An accurate machine learning model for global subseasonal forecasts
  • ⭐(Nature 2021) [Paper]
    • 🍬 Seasonal-Precip: the first :Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts

• Climate:

  • ⭐(Nature 2019) ENSO, [Paper]
    • 🍬 ENSO: the first : MetNet: A Neural Weather Model for Precipitation Forecasting
  • ⭐(Nature 2023) Climate, [Paper]
    • 🍬 Climate: the first : Climate Model Driven Seasonal Forecasting Approach with Deep Learning

• Extreme:

  • ⭐(Nature 2019) FuXi-Extreme, [Paper]
    • 🍬 FuXi-Extreme: the first : FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model

• Typhoon*:

  • ⭐(Arxiv 2024) MSCAR, [Paper]
    • 🍬 MSCAR: the first : Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
    • Tropical cyclone intensity forecasting: the MSCAR model incorporates causal relationships and operates on a large-scale multimodal dataset.

• SR:

  • ⭐(RMetS 2023) Uformer [Paper]
    • 🍬 Uformer : Investigating transformer-based models for spatial downscaling and correcting biases of near-surface temperature and wind-speed forecasts.
  • ⭐(Earth-Science Reviews 2023) ** [Paper]
    • 🍬 A comprehensive review on deep learning based remote sensing image super-resolution methods
  • ⭐(NeurIPS 2022) RCMs [Paper]
    • 🍬 RCMs : Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach
  • ⭐(NeurIPS 2021) ESRGAN [Paper]
    • 🍬 A comparative study of convolutional neural network models for wind field downscaling
  • ⭐(Water Resources Research 2021) SRDRN [Paper]
    • 🍬 Deep learning for daily precipitation and temperature downscaling
  • ⭐(IEEE Transactions on Geoscience and Remote Sensing 2021) PSD-Net [Paper]
    • 🍬 Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network
  • ⭐(Meteorological Applications 2020) DeepRU [Paper]
    • 🍬 A comparative study of convolutional neural network models for wind field downscaling
  • ⭐(Association for Computing Machinery 2020) YNet [Paper]
    • 🍬 Climate downscaling using ynet: a deep convolutional network with skip connections and fusion
  • ⭐(Mathematical Problems in Engineering 2020) CDN [Paper](https://dl.acm.org/doi/abs/10.1145/3394486.3403366]
    • 🍬 A climate downscaling deep learning model considering the multiscale spatial correlations and chaos of meteorological events
  • ⭐(Journal of Applied Meteorology and Climatology 2020a) ** [Paper]
    • 🍬 Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain. Part i: daily maximum and minimum 2-m temperature
  • ⭐(Journal of Applied Meteorology and Climatology 2020b) ** [Paper]
    • 🍬 Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain. Part i: daily maximum and minimum 2-m temperature
  • ⭐(NeurIPS 2019) PSD-Net [Paper]
    • 🍬 PSD-Net : Numerical Weather Model Super-Resolution
  • ⭐(Theoretical and Applied Climatology 2019) ** [Paper]
    • 🍬 Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation
  • ⭐(Association for Computing Machinery 2017) Deepsd [Paper]
    • 🍬 Deepsd: generating high resolution climate change projections through single image super-resolution. Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining

• DA

  • ⭐(Arxiv preprint 2023) FengWu-4DVar [Paper]
    • 🍬 FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation

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