Multi-sensor Weather Forecast Competition

  • Study multi-channel weather movies.
  • Predict weather products in various Earth regions.
  • Apply transfer learning to new earth regions.
clouds and sun rays

Weather4cast 2021

The goal of the competition is a short-term prediction of selected weather products based on meteorological satellites data obtained in collaboration with AEMET/ NWC SAF. Following recent success of our Traffic4cast competitions at NeurIPS in 2019 and 2020, this challenge presents weather forecast as a video frame prediction task. The weather movies consist of multi-channel images encoding the cloud properties, temperature, turbulence, and rainfall. The images are recorded at 15 minute intervals through the entire year. Each pixel in the images represents the area of about 4 km x 4 km, and each region contains 256 x 256 pixels.  The regions span varying landscapes including mountains, deserts,  islands and seas, and others. The training and validation data contain observations in various earth regions and the test data include additional new regions. The challenge is to predict the encoded weather products in all regions. The competition outcomes are expected to go beyond methodological advances in weather forecasting and video frame prediction. The challenge offers real-world benchmark for few shot and transfer learning and allows testing multi-sensor data fusion. Learn more…

AEMET logo
NWC SAF logo


The competition goal is to predict the next 32 images (8 hours in 15 minute intervals) of the weather movies. The images contain four channels encoding the following weather products: temperature (on accessible surface: top cloud or earth), convective rainfall rate, probability of occurrence of tropopause folding, and cloud mask.

The competition data are split into training, validation, and test sets. The core challenge data contain the training, validation, and test sets. The resulting training set contains about 25 billion data points. The transfer learning challenge data contain only the test set in additional regions.

We are hosting 2 incremental competitions:

  • Stage 1 (April 1 – June 30, finished): Selected papers will be invited to present their work in the 1st Workshop on Complex Data Challenges in Earth Observation @ CIKM.
  • IEEE BigData Cup (July 1 – October 27): Selected papers will be invited to present their work in a special session devoted to the challenge @ IEEE BigData. While the data for the IEEE BigData Cup is not available yet, we recommend participants getting familiar with the data by downloading the subset of regions provided for the Stage 1 competition and submitting predictions to its leaderboard.

The competitions comprise two challenges:

  1. Core challenge: predict the target weather products in the training regions:
    • R1, R2, R3 – Stage 1 competition (finished).
    • R1, R2, R3, R7, R8 – IEEE BigData Cup. Get the data.
  2. Transfer learning: predict the target weather products in the additional regions where we do not provide any data for training nor evaluation:
    • R4, R5, R6 – Stage 1 competition (finished).
    • R4, R5, R6, R9, R10, R11 – IEEE BigData Cup. Get the data.

For each day, the images are grouped into a separate folder, within which there are several folders containing the weather products. Learn more…

data example
Data example: tropopause turbulence in the South West Europe region.

Regions in blue: core challenge; regions in orange: transfer learning.


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