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Found 8 papers, 5 papers with code
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling.
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
We propose the ENS-10 prediction correction task for improving the forecast quality at a 48-hour lead time through ensemble post-processing.
Machine Learning Emulation of Urban Land Surface Processes
Can we improve the modeling of urban land surface processes with machine learning (ML)?
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting.
High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations
Here, we develop a method to make probabilistic precipitation predictions based on features that climate models can resolve well and that is not highly sensitive to the approximations used in individual models.
Computation Applications
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model.
Deep Learning for Post-Processing Ensemble Weather Forecasts
Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%.
Predicting Weather Uncertainty with Deep Convnets
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations.
