27 papers with code • 0 benchmarks • 6 datasets
Weather Forecasting is the prediction of future weather conditions such as precipitation, temperature, pressure and wind.
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.
We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.
The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN.
In this paper we introduce several new Deep recurrent Gaussian process (DRGP) models based on the Sparse Spectrum Gaussian process (SSGP) and the improved version, called variational Sparse Spectrum Gaussian process (VSSGP).
Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere.
Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%.