Analysis of vulnerability of ATM to weather phenomena

Abstract

Weather conditions and Air Traffic Management (ATM) operations are strongly related, due to the relevant influence that the observed and forecasted weather conditions into assigned airspace have on the operational conditions that are therefore possible for such airspace. In the framework of the SESAR JU funded project CREATE (Innovative operations and climate and weather models to improve ATM resilience and reduce impacts), therefore, a dedicated study has been carried out in order to investigate the relation between weather and ATM. Such study both investigated the consequences that adverse weather conditions can have on ATM and, on the other side, addressed the currently and future available tools that can support ATM in the prediction and management of weather conditions from the aeronautical operations point of view. In this paper, the results of such literature study are outlined. More in details, in the paper first the main outcomes of the study are summarized, in terms of identification of the most relevant weather phenomena that affect the ATM operations, indication of their main impacts on them in terms of operational disruptions and, finally, identification of the associated level of severity. In particular, the above indicated aspects are addressed by taking into specific consideration the enroute and the TMA (Terminal Manoeuvring Area) flight phases and, for each of them, the main affecting weather phenomena and the main affected operations are identified. Then, in the paper the results of the study are summarized about the identification of the most relevant meteo tools that are expressed by state-of-the-art technologies to support the ATM operations in order to properly take into account the weather conditions in a precise and timely manner. This study addresses both the review of available and perspective tools for weather reporting and of the available and perspective numerical models supporting ATM in terms of weather models and air quality models.