In meteorology, combining different forecasting methods to create a more accurate prediction involves integrating numerical weather prediction models with other data sources, such as statistical models, observations, and even artificial intelligence. For example, a short-term forecast might blend a high-resolution numerical model with real-time radar and satellite data to improve predictions of imminent thunderstorms. This approach leverages the strengths of each contributing system while mitigating their individual weaknesses.
Such integrated approaches are crucial for improving forecast accuracy, especially for complex weather phenomena. Historically, weather prediction relied heavily on human interpretation of observations and simpler models. The increasing complexity and availability of computational resources have allowed for more sophisticated numerical models. However, these models are still imperfect. Supplementing them with other data sources, particularly for localized or short-term predictions, often yields significantly improved results. This ultimately enhances public safety and allows for more effective planning across various sectors sensitive to weather conditions.
