Over the past year, there has been a significant shift in weather forecasting with the rise of AI-based methods.
Traditionally, weather forecasts are made by creating a detailed three-dimensional model of the atmosphere, starting from an initial state.
This model uses complex equations to predict how the atmosphere will change over time.
For years, improving these forecasts involved enhancing the accuracy of the starting conditions, increasing the resolution of the models, and refining the equations used.
Commodity traders embrace AI for precise forecasts, enhancing decision-making in food and energy markets.The latest AI-driven weather forecasting, however, takes a different approach. Instead of relying on equations, AI models analyze patterns from years of historical data.
These statistical models identify trends and make predictions based on past data. Despite not using physical equations, AI forecasts are surprisingly accurate and significantly faster to generate than traditional methods.
In the commodity trading sector, where precise weather forecasts are crucial for predicting the prices of food, energy, and raw materials, the speed and flexibility of AI models are highly valued.
Traders and analysts have quickly adopted these new tools to gain an edge by adjusting forecast horizons and accelerating forecast generation.