In the world of climate science, one of the most complex challenges lies in producing accurate seasonal forecasts. These forecasts are critical for agriculture, as they enable farmers to make informed decisions about what to plant, when to plant, and how to manage their resources over the coming months. However, achieving accurate medium-range seasonal forecasts—those that predict conditions three to six months in advance—comes with significant computational challenges and costs for forecasters. At finres, we wanted to use our expertise in machine learning techniques to confront this challenge.
