Forecast Clarity: A User-Focused Index Framework

The expanding user base of meteorological forecasts underscores the growing demand for accurate forecast interpretation, particularly among aviation professional users who rely on various applications and models. Meteorologists often face the questions not only about the reliability of these forecasts but also about the changes users observe during forecast updates. This work proposes a framework of indices aimed at providing users with relevant forecast insights tailored to their specific needs. These indices consider factors such as overall suitability for activities, mission requirements, and the reliability of forecast products. For instance, the Warning Related Source Suitability Index (WSSI) assesses the suitability of TAF forecasts in predicting conditions that meet warning threshold values. Vehicle-related feasibility index (VRFI) presents probability that vehicle with its limits will be able to accomplish the mission. The Source Uncertainty Index (SUI) describes the spread characteristics of the product at a specific time and estimates its ongoing uncertainty when continuously observed by the user. These indexes might be expanded or improved, providing potential for the novel interpretation framework.

Our research involved testing various methods for determining probabilities from numerical model archives, professional landing forecasts and TAF forecasts. The results indicate the superiority of machine learning methods, which achieved accuracy rates up to 10% higher than conditional probability (98% vs 88%) for aviation indices. 

The index framework will help simplify the output of increasingly sophisticated systems and evaluate only relevant information from meteorological big data databases. In the area of decision-making processes, the machine-readable nature of these indices supports integration into automated systems. This enables fast and informed decision making in real time. Overall, this open index system aims to enhance the understanding and trustworthiness of forecast predictions, delivering to both amateur and professional users.