TU Delft / AE / CO / CNS-ATM / Master thesis assignment:

Hyper-local wind now casting using drone measurements


Background

The Meteo Sensors In the Sky (METSIS) project aims to test the use of drones as a wind sensor network for hyper-local wind now casting at low altitudes. METSIS consists of three steps:

This master assignment focuses on step 2 of the overall METSIS project. For this task, METSIS aims to extend the Meteo Particle Model (MPM) [1], a technique for estimation wind based on aircraft flight, to low altitude urban airspace using drone measurements.

Wind field nowcast based on drone measurements. Dashed arrows are MPM estimates and solid arrows are drone measurements.

Project description

In collaboration with NLR, the MSc assignment is expected to start in September 2020, with a maximum duration of 12 months.

METSIS plans to perform a flight test with 4 drones in April 2020 at the NLR Drone Center in Marknesse to validate the METSIS concept. This will also be an interesting part of the master project.

The main research tasks include:

Requirements and opportunities

We are looking for a student who is:

We offer:

Note: This assignment involves a selection process. If interested, please send an email to:

with your CV and a motivation letter. A short online interview will be conducted to select the most suitable candidate.

Supervisors

Example references

  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. Lorenc, A.C., Ballard, S.P., Bell, R.S., Ingleby, N.B., Andrews, P.L.F., Barker, D.M., Bray, J.R., Clayton, A.M., Dalby, T., Li, D. and Payne, T.J., 2000. The Met. Office global three‐dimensional variational data assimilation scheme. Quarterly Journal of the Royal Meteorological Society, 126(570), pp.2991-3012.
  3. Dalmau, R., Pérez-Batlle, M. and Prats, X., 2017, September. Estimation and prediction of weather variables from surveillance data using spatio-temporal Kriging. In 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC) (pp. 1-8). IEEE
  4. De Jong, P.M.A., Laan, J.V.D., Veld, A.I.T., Van Paassen, M.M. and Mulder, M., 2014. Wind-profile estimation using airborne sensors. Journal of Aircraft, 51(6), pp.1852-1863.
  5. Dalmau, R., Prats, X. and Baxley, B., 2019. Using wind observations from nearby aircraft to update the optimal descent trajectory in real-time.


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