Weather is one of the most significant factors influencing agricultural production and therefore, the most accurate weather forecast possible is a very valuable information that farmer can get.

One of the weather forecast challeges is learning weather patterns using a massive volume of historical observed data and building a robust weather prediction model. 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.

On Friday’s webinar hosted by Bente Lilja Bye, we had an excellent opportunity to learn more about current methods for weather forecasting from Amit Kirschenbaum (Leipzing University) and about deep learning methods use for building weather prediction model from Ondrej Kaas (Plan4all). Unfortunately, internet connection problems made it impossible to learn about Climate Trends Change from Samuel Ekwacu (Uganda National Meteorological Authority), but he is very kind to record his presentation separately and afterwards we will provide you with that.

Here is a recording of the webinar for you!