RPAS based observation on the Arctic Boundary Layer during the ISOBAR campaigns on Andøya and Hailuoto

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1 U N I V E R S I T Y O F B E R G E N RPAS based observation on the Arctic Boundary Layer during the ISOBAR campaigns on Andøya and Hailuoto S Kral 1, J Reuder 1, L Båserud 1, G Urbancic 1,M Jonassen 2;1, A Bhandari 1, A Rautenberg 3, J Bange 3, M Hundhausen 3, P Hilsheimer 3, A Platis 3, B Wrenger 4, C Langohr 4, H Voss 4, M Müller 5, C Lindenberg 5, T Vihma 6;2, I Suomi 6, E O Connor 6, R Kouznetsov 6 1, University of Bergen; 2 The University Centre in Svalbard, Longyearbyen; 3 University of Tübingen; 4 University of Applied Sciences Ostwestfalen-Lippe, Höxter; 5 Lindenberg und Müller GmbH & Co. KG, Hohenhameln; 6 Finnish Meteorological Institute, Helsinki;

2 Overview The ISOBAR Project Methods Andøya campaign Hailuoto campaign Results Summary

3 ISOBAR (Innovative Strategies for Observations in the arctic atmospheric Boundary layer) Funded by the Norwegian Research Council + In Kind Project Partners:, University of Bergen Uni Research AS, Bergen The University Centre in Svalbard, Longyearbyen Finnish Meteorological Institute, Helsinki University of Tübingen University of Applied Sciences Ostwestfalen-Lippe Leibniz University Hannover

4 Purpose Understanding of ABL processes in the Arctic Characterization of turbulence within the stable ABL Goal Improvement of ABL parameterization schemes Approach Observations targeting all relevant processes AWS Profiling systems (RPAS, balloon, remote sensing) Turbulence systems (RPAS, ground based) Numerical Modelling

5 Methods Measurement strategy ground based flux and met stations ABL remote sensing and profiling systems RPAS Numerical modeling experiments Single Column Model (SCM) Large-Eddy Simulation (LES) Weather Research and Forecasting Model (WRF)

6 Andøya Campaign (Dec. 2016) Test and validation campaign Rough conditions (cold, dark, windy) Andøya Space Centre Flight permission UiB, UT, UOWL SUMO MASC AMOR Quadcopters Bebop Quadcopters 100 m Mast

7 Andøya Campaign Lessons learned Conditions on Andøya can be very challenging Bebop poses considerable quality issues under cold conditions Own flight permissions are desirable for an efficient test campaign To do Validation of SUMO wind algorithm against mast data

8 Hailuoto Campaign Feb 2017 (3.5 weeks) UiB, UT, UOWL, FMI Observations over sea-ice RPAS (3 different fixed-wing, 5 different rotary-wing) Ground stations (EC, AWS) Wind scanning Lidar (Windcube 100s) Sodar (FMI)

9 Hailuoto Campaign Flight permission D-Area ca. 20 km 2 Below FL 65 (ca m) Very positive attitude by Finnish authorities Easy communication with tower in Oulu Cell phone contact for activation

10 Hailuoto Campaign RPAS operations SUMO profiles up to 1800 m Quadcopter profiles up to 400 m MASC/miniTalon turbulence legs between 30 m and 300 m RPAS SUMO Bebop MASC minitalon AMOR Scientific flights

11 Hailuoto Campaign SUMO profile MASC race track Bebop profile

12 Temperature, wind and net radiation Temperature, wind, net-radiation

13 Results: T-profiles Bebop

14 Results: T-profiles Bebop

15 Results: T-profiles Bebop

16 Results: T-profiles Bebop

17 Results: T-profiles Bebop

18 Results: T-profiles Bebop

19 Results: T-profiles Bebop

20 Results: T-profiles Bebop

21 Results: T-profiles Bebop

22 Results: T-profiles SUMO

23 Results: T-profiles SUMO

24 Results: T-profiles SUMO

25 Results: T-profiles SUMO

26 Results: T-profiles SUMO

27 Results: Combined T-profiles (AWS, Bebop, SUMO)

28 Results: Combined T-profiles (AWS, Bebop, SUMO) time Bebop SUMO profile time SUMO different z scaling Bebop profile mast profile

29 Results: Combined T-profiles (AWS, Bebop, SUMO)

30 Results: Combined T-profiles (AWS, Bebop, SUMO)

31 Results: Combined T-profiles (AWS, Bebop, SUMO)

32 Results: Combined T-profiles (AWS, Bebop, SUMO)

33 Results: Combined T-profiles (AWS, Bebop, SUMO)

34 Results: Combined T-profiles (AWS, Bebop, SUMO)

35 Results: Combined T-profiles (AWS, Bebop, SUMO)

36 Results: Combined T-profiles (AWS, Bebop, SUMO)

37 Wind speed profiles Bebop (very rough estimate) Simple but crude assumptions Based on tilt angles Symmetric behavior for pitch and roll Neglecting horizontal movements Neutral for φ = θ = 0 u = C sin φ v = C sin θ U = u 2 + v 2 Proper calibration required "face wind" control algorithm (θ 0)

38 Wind speed profiles Bebop (very rough estimate)

39 Wind speed profiles Bebop (very rough estimate)

40 Summary High resolution data covering the entire ABL and a good part of the free atmosphere Estimate ABL fluxes from profiles RPAS based fluxes Link profiles to surface fluxes Remote sensing data as reference and for monitoring temporal evolution Strong gradients require even slower climb rate or faster sensors GPS altitude might be misleading when combining profiles from different systems Robustness of quadcopter needs to be improved for Arctic conditions Quadcopter wind estimation needs to be improved

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