Weather Forecasting
101 papers with code • 2 benchmarks • 13 datasets
Weather Forecasting is the prediction of future weather conditions such as precipitation, temperature, pressure and wind.
Source: MetNet: A Neural Weather Model for Precipitation Forecasting
Libraries
Use these libraries to find Weather Forecasting models and implementationsDatasets
nuScenes
A2D2
Shifts
SEVIR
Oxford Radar RobotCar Dataset
IowaRain
Hurricane
Latest papers
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics.
Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting
patrickesa/stormsurgecastnet
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Hurricanes and coastal floods are among the most disastrous natural hazards.
Uncertainty quantification for data-driven weather models
cbuelt/dduq
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In a case study on medium-range forecasts of selected weather variables over Europe, the probabilistic forecasts obtained by using the Pangu-Weather model in concert with uncertainty quantification methods show promising results and provide improvements over ensemble forecasts from the physics-based ensemble weather model of the European Centre for Medium-Range Weather Forecasts for lead times of up to 5 days.
KARINA: An Efficient Deep Learning Model for Global Weather Forecast
jmj2316/KARINA
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This model achieves forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only 4 NVIDIA A100 GPUs and less than 12 hours of training.
Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
A recent architecture, Mamba, based on state space models has been shown to achieve comparable performance for modeling text sequences, while scaling linearly with the sequence length.
Consistent Validation for Predictive Methods in Spatial Settings
davidrburt/consistent-spatial-validation
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Unfortunately, classical approaches for validation fail to handle mismatch between locations available for validation and (test) locations where we want to make predictions.
GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcasting
In light of this, we propose GA-SmaAt-GNet, a novel generative adversarial architecture that makes use of two methodologies aimed at enhancing the performance of deep learning models for extreme precipitation nowcasting.
GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
wendig/gd-caf
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In particular, we introduce Graph Dual-stream Convolutional Attention Fusion (GD-CAF), a novel approach designed to learn from historical spatiotemporal graph of precipitation maps and nowcast future time step ahead precipitation at different spatial locations.
Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK
acse-ww721/da_ml_era5_asos_weather_forecasting_uk
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In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting.
FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
nci/fourcastnext
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FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements.
