Weather Forecasting
101 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 implementationsDatasets
nuScenes
A2D2
Shifts
SEVIR
Oxford Radar RobotCar Dataset
IowaRain
Hurricane
Most implemented papers
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.
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block.
Real-time Linear Operator Construction and State Estimation with the Kalman Filter
In addition, using this method, an expectation maximization algorithm can be used to estimate the parameters of the model.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks
IsmailAlaouiAbdellaoui/weather-forecasting-explanable-recurrent-convolutional-NN
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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
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The supply and demand of energy is influenced by meteorological conditions.
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
MIT-AI-Accelerator/neurips-2020-sevir
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NeurIPS 2020
To help address this problem, we introduce the Storm EVent ImagRy (SEVIR) dataset - a single, rich dataset that combines spatially and temporally aligned data from multiple sensors, along with baseline implementations of deep learning models and evaluation metrics, to accelerate new algorithmic innovations.
Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms
To this end, we introduce a novel deep learning architecture for forecasting high-resolution spatio-temporal weather data.
Climate Modeling with Neural Diffusion Equations
jeehyunhwang/neural-diffusion-equation
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On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.
