# Weather Forecasting

77 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 implementations## Datasets

## Most implemented papers

# Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

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

# NGBoost: Natural Gradient Boosting for Probabilistic Prediction

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

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

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

# Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

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.

# Eidetic 3D LSTM: A Model for Video Prediction and Beyond

We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.

# Verified Uncertainty Calibration

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

# WeatherBench: A benchmark dataset for data-driven weather forecasting

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

# Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.

# PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.

# Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

However, many tasks of practical interest have different modalities, such as tabular data, audio, text, or sensor data, which offer significant challenges involving regression and discrete or continuous structured prediction.