Weather Forecasting

45 papers with code • 1 benchmarks • 11 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

Most implemented papers

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

ndrplz/ConvLSTM_pytorch NeurIPS 2015

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.

Verified Uncertainty Calibration

AnanyaKumar/verified_calibration NeurIPS 2019

In these experiments, we also estimate the calibration error and ECE more accurately than the commonly used plugin estimators.

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

stanfordmlgroup/ngboost ICML 2020

NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.

Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

BruceBinBoxing/Deep_Learning_Weather_Forecasting 22 Dec 2018

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.

WeatherBench: A benchmark dataset for data-driven weather forecasting

pangeo-data/WeatherBench 2 Feb 2020

Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.

Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

benedekrozemberczki/pytorch_geometric_temporal 16 Feb 2021

Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.

Real-time Linear Operator Construction and State Estimation with the Kalman Filter

ZoneTsuyoshi/lock 30 Jan 2020

In addition, using this method, an expectation maximization algorithm can be used to estimate the parameters of the model.

Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks

IsmailAlaouiAbdellaoui/weather-forecasting-explanable-recurrent-convolutional-NN 23 Sep 2020

Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models.

Deep coastal sea elements forecasting using U-Net based models

jesusgf96/Sea-Elements-Prediction-UNet-Based-Models 6 Nov 2020

The supply and demand of energy is influenced by meteorological conditions.

Spatio-temporal Weather Forecasting and Attention Mechanism on Convolutional LSTMs

sftekin/ieee_weather 1 Feb 2021

In this paper, we forecast high-resolution numeric weather data using both input weather data and observations by providing a novel deep learning architecture.