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Air Traffic Management (CNS/ATM)

Hyper-local wind nowcasting based on low-cost drone sensors



The Meteo Sensors In the Sky (METSIS) project has successfully tested the feasibility of using drones to provide wind measurements and local wind field estimation using the Meteo-Particle Model [1] (MPM). However, it relied on expensive ultrasonic wind sensors and customized structures for installations. Such a solution cannot be scaled up to a large number of low cost drones.


Research description:

In this follow-up master thesis research, we want to explore the following wind estimations approaches:

  • Obtain wind information by replacing the ultrasonic wind sensor with low-cost airspeed sensors.
  • Estimate wind information using the onboard sensor data using advanced filtering techniques, e.g. [2,3], and test the robustness of the methods.

In addition to the above, this research will also aim to improve the current MPM implementation by designing an automatic process to optimize model parameters for different types of urban scenarios. For this optimization process, existing METSIS and new data can be used as a starting point.

A final optional bonus objective of this MSc thesis is to investigate the possibility of introducing a reduced-order aerodynamic model to extend MPM’s estimation capability for wind at regions with complicated aerodynamic conditions, for example, at boundaries, wind fronts, and urban canyons.


Requirements and opportunities:

We are looking for a student who is:

  • Experienced in Python programming and data analysis
  • Willing to engage in exploring new methods
  • Independent and self-motivated
  • From the EU or has the necessary Dutch work permits (Required by NLR)


  • Affiliation with NLR as part of the Delft-NLR Drone Collaboration (DNDC)
  • NLR offers the candidate a monthly stipend. As such a short interview will be part of the selection process for this thesis.




[1] Sun, J., Vû, H., Ellerbroek, J. and Hoekstra, J.M., 2018. Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model. PloS one, 13(10). [2] Neumann, P.P. and Bartholmai, M., 2015. Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit. Sensors and Actuators A: Physical, 235, pp.300-310. [3] Simma, M., Mjøen, H. and Boström, T., 2020. Measuring Wind Speed Using the Internal Stabilization System of a Quadrotor Drone. Drones, 4(2), p.23.


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