METSIS: Hyperlocal Wind Nowcasting for U-space

Junzi Sun*, Emmanuel Sunil, Ralph Koerse, Stijn van Selling, Jan-Willem van Doorn, Thomas Brinkman

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

The METeo Sensors in the Sky (METSIS) project, funded by SESAR’s Engage knowledge transfer network, investigated the use of drones as an aerial wind sensor network for U-space applications. The concept aims to provide accurate, lowcost and hyperlocal wind nowcasts for drones using data collected by drones themselves and the Meteo-Particle Model (MPM) for wind field reconstruction. In this paper, we describe the METSIS concept and a proof-of-concept experiment that was performed using four drones to determine the feasibility and accuracy of the concept at low altitudes. For the experiment, ultrasonic anemometers were mounted to each drone to measure local winds. The calibration of the wind sensors was tested using the NLR Anechoic Wind Tunnel. Subsequently, flight-tests were performed at the NLR Drone Center to evaluate the effect of obstacles, drone motion, measurement density, and measurement errors on concept accuracy. Wind fields estimated during the flight-tests were published to the AirHub Drone Operations Center (DOC) system to demonstrate the communication of this data to U-space end-users in real-time. The results indicated that the METSIS concept is a promising solution for the wind nowcast component of the U-space weather information service. Further research is planned to improve the accuracy and sclability of the METSIS concept.
Original languageEnglish
Title of host publication11th SESAR Innovation Days
Number of pages8
Publication statusPublished - 2021
Event11th SESAR Innovation Days - virtual event
Duration: 7 Dec 20219 Dec 2021
Conference number: 11

Conference

Conference11th SESAR Innovation Days
Abbreviated titleSIDs 2021
Period7/12/219/12/21

Keywords

  • U-space
  • Weather Information Service
  • Meteo Particle Model (MPM)
  • UTM
  • Drones

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