Precipitation Forecasting
12 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Precipitation Forecasting
Most implemented papers
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.
W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting
gufrannn/w-mae
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On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data.
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
apratimbhattacharyya18/seg_pred
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ICLR 2019
For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence.
Short-term daily precipitation forecasting with seasonally-integrated autoencoder
On the other hand, deep learning models are good at learning nonlinear interactions, but they are not designed to deal with the seasonality in time series.
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction
osilab-kaist/komet-benchmark-dataset
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Historically, this challenge has been tackled using numerical weather prediction (NWP) models, grounded on physics-based simulations.
Adaptive Bias Correction for Improved Subseasonal Forecasting
Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mitigation.
PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting
To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark consisting of three datasets for NWP post-processing-based precipitation forecasting.
Learning Robust Precipitation Forecaster by Temporal Frame Interpolation
This achievement not only underscores the effectiveness of our methodologies but also establishes a new standard for deep learning applications in weather forecasting.
