Next Webinar: Deep Learning for Weather Forecast

On Wednesday, there will be another webinar focused on Deep Learning for Weather Forecast this time and you are cordially invited!


  • Description of the data and preprocessing pipeline.
  • Description of the used methodology and frameworks.
  • Introduction to the challenge deep learning for weather forecasting

What: The weather is a chaotic system. Small errors in the initial conditions of a forecast grow rapidly, and affect predictability. Furthermore, predictability is limited by model errors due to the approximate simulation of atmospheric processes of the stateof- the-art numerical models. These days, on the other hand, we gather a lot of data thanks to modern IoT technologies richly occupied in the fields. Incorporation of global weather data among data collected from sensors in the same field can contribute to accurate weather forecast in the local environment. Preparation of training data i.e. creation of preprocessing pipeline where the global weather data would be correctly enhanced by in local data from sensors. Using RNNs (LSTM) with supporting Vowpal Wabbit or Prophet.

Why: Adaptation of deep learning algorithms specialized for time-series prediction can be beneficial or more accurate for weather forecasting in the local environment for farmers than the publicly available global forecast model.

Who (is the webinar for): Researchers who are interested in time-series and high-dimensional function modelling/prediction via deep learning.

If you want to attend the webinar, do not forget to register HERE!