Description
Predicting extreme weather is difficult because climate systems are complex and full of nonlinear behaviors. These challenges are especially relevant when trying to anticipate rare but serious events. Traditional forecasting models often fall short, mainly due to computational limits. In this study, we explore a hybrid model that blends classical convolutional neural networks with quantum data processing. We use weather data from Estes Park, Colorado, focusing on temperature, humidity, wind, and pressure to uncover useful patterns. The quantum circuits help reorganize and highlight relationships in the data before it enters the neural network. Our results show that this quantum preprocessing improves forecast accuracy, especially for 3-day and 7-day windows. The improvement is less noticeable for 10-day forecasts. Overall, this approach shows how combining quantum tools with machine learning can open new possibilities in weather prediction. It also sets the stage for more robust forecasting tools in the future.
