Stretch
MSc Assignments

Hyper-local wind now casting using drone measurements

Summary
Compensation: Compenation provided by NLR

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:

  • Step 1: Airborne drones measure instantaneous wind states and transmit data to a ground station
  • Step 2: Ground station uses the Meteo Particle Model to estimate the wind field in real-time.
  • Step 3: The ground station communicates wind field data to drone operators via the U-space weather information service

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:

  • Perform a literature review on the Meteo Particle Model and other relevant assimilation approaches
  • Extend MPM to take into account the vertical motion of wind near the ground
  • Adapt the model to cope with the wind in the urban low-altitude environment
  • Develop a data acquisition and display system to visualize the wind field measured and estimated during the flight test
  • Aid with the execution of the flight test
  • Test and validate the model using simulation and real data from the flight test
  • Recommend improvements to the METSIS concept, and report results

 

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 work and residence permits to work in the Netherlands

We offer:

  • The opportunity to work on a practical and challenging MSc thesis assignment A chance to work in an academic and industry setting
  • NLR offers the candidate a monthly stipend to cover basic costs for up to 12 months

If interested, please send an email to:

  • Emmanuel Sunil( emmanuel.sunil@nlr.nl )
  • Junzi Sun ( j.sun-1@tudelft.nl )

 

Supervisors

  • Dr. Junzi Sun (TU Delft, Daily supervisor)
  • Dr. Emmanuel Sunil (NLR, External supervisor)
  • Prof. Jacco Hoekstra (TU Delft, Supervisor)

 

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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