Weather data supply for high altitude pseudo satellites (HAPS): A use case of a new prototype universal Web-API for probabilistic height-resolved weather datasets

Within the framework of the OBeLiSk project funded by the  Federal Aeronautical Research Programme (LuFo VI-1), we investigate the integration of stratospheric drones into the air space with special focus on weather challenges and data supply. While on one hand the stratospheric drones, so-called high altitude pseudo satellites (HAPS), form a special use case, the challenge to supply suitable weather data for new air space users is a much wider and more general field of interest.

Having a glider-like design with a wide wingspan makes the HAPS very weather-susceptible. They fly very slowly, thus achieving only small climb rates. Hence, ascend and descent take long time and play an important role in the operational concept of the HAPS. For this reason, special focus must be put on the distance between earth’s surface and the first kilometer above ground, where both, other air traffic but also weather conditions feature lots of constraints. Laterally well resolved weather data in this range is not only key for the HAPS, but ultimately addresses a much wider spectrum of users. Since, however, data sets and their supply ways to the customer in this field are still sparsely available, new dataset products as well as suitable web application programming interfaces (web-APIs) are needed.

Moreover, particularly strategic flight planning for the next days requires probabilistic weather information since deterministic weather forecast lacks reliability in this regime. But, probabilistic ensemeble-data consists of vast amounts of data that can be challenging to handle. We present a new prototype probabilistic weather data set with detailed height level resolution based on the ICON-D2 NWP model of the Deutsche Wetterdienst. We combine this with the prototype of a new web-API that allows the evaluation of general probabilistic weather data in different height levels in a universal and user-driven fashion. With this contribution we address new air space users but also customers interested in near-surface probabilistic weather information.