New Generation Ultra-Fast Escan X-band Radar/ 1.5 µm Lidar Sensors: Lessons learnt from UFO Toulouse trials

Transcription

1 New Generation Ultra-Fast Escan X-band Radar/ 1.5 µm Lidar Sensors: Lessons learnt from UFO Toulouse trials F. Barbaresco, C. Rahatoka, L. Thobois J.P. Cariou, A. Dolfi, R. Wilson UFO Dissemination Workshop NLR, Amsterdam & H. de Siebren

2 2 / UFO Scientific & Technological Challenges Studies of Ultra Fast Lidar/Radar Wind & EDR sensors Experimental trials in Munich and Toulouse Airports 2D electronic scanning antenna based on low cost X-band tile Comparison with Mode-S Downlink & classical sensors New high power laser source of 1.5 micron Lidar 3D scanner Doppler signal processing algorithm for 3D wind field and EDR monitoring Design tool based on simulator coupling EM/EO & Fluid-Meca Models

3 3 / X-band 2D E-scan Bipolar Antenna Demonstrator

4 4 / EDR sources : Obstacles & Ground Thermal Convection EDR generated by Obstacles When strong wind pass over a large obstacle they create turbulence as high as 10 time obstacle height. In PRANDL layer (the Surface Boundary Layer SBL), turbulence is characterized by Roughness Length EDR generated by Ground Thermal Convection When the earths surface is heated by the sun, it will also heat the air directly above it. Since hot air is less dense than cool air, this heated air will rise from the earths surface to a higher elevation. In the evening, the opposite occurs.

5 5 / Wind Models: Surface Boundary Layer The Surface Boundary Layer SBL Logarithmic Wind Law (Prandl equation when condition of neutral stability is fulfilled) u τ z U ( z) = ln EDR κ z0 U ( z) : wind speed at heigh z u : the "friction velocity" h/10 : the roughness length with h : the height of During diabatic conditions, we can take into account Monin-Obukhow length L. L is positive for stable conditions (usually at night), negative for unstable conditions (usually daytime, and approaches infinity for neutral conditions (cloudy and windy conditions). A.K. Yadav, S. Raman & M. Sharan, Surface Layer Turbulence Spectra and Dissipation Rates During Low Wind in Tropics, Dec u z) = κ z 3 τ ( κ : von Karman's constant ( 0.38) z τ 0 u τ z U ( z) = ln + 5. κ z0 the roughness element z L Table of roughness length 3 1 uτ z EDR( z) = φm φ : dimensionless wind gradient κ z L m

6 6 / Others Models of Surface Boundary Layer For a statically stable or neutral PBL: For the surface layer and above layer (z the height above the ground; h is the PBL height): 2 2 u z u τ z 0.1h EDR( z) = τ z z z > 0.1h EDR( z) = κ. z L κ. z L h For a statically unstable PBL: convective regime dominated by buoyancy prevails: 2/3 3/ u wτ z 0.1 ( ) = τ z z h EDR z z > 0.1h EDR( z) = κ. z h L h wτ : the convective velocity scale Estimation of parameters (PBL height, friction velocity, convective velocity scale) kv = ( ) Ψ V velocity at the top of the surface layer a a uτ Max, uτ, n with ln za / z0 m za height of the surface layer 1/3 w = g Θ. h / T 0 with Θ mean surface heat flow, T reference temperatu τ ( ) re. 0 Hogstrom, U., 1996: Review of Some Characteristics of the Atmospheric Surface Layer. Boundary-Layer Meteorol., Vol. 78, Rao, K. S. and Nappo, C. J., 1998: Turbulence and Dispersion in the Stable Atmospheric Boundary Layer. Dynamics of the Atmospheric Flows: Atmospheric Transport and Diffusion Processes, Singh, M. P. and Raman, S., eds., Computational Mechanics Publications, pp Monin, A. S. and Obukhov, A. M., 1954: Basic Laws of Turbulent Mixing in the Atmosphere Near the Ground. Tr. Akad. Nauk SSSR Geoph. Inst., No. 24 (151), Han, J., S. Shen, S. P. Arya, and Y.-L. Lin, 1999: An estimation of turbulent kinetic energy and energy dissipation rate based on atmospheric boundary layer similarity theory. [NASA contract report] Charney Joseph J. & al, A New Eddy Dissipation Rate Formulation for the Terminal Area PBL Prediction System, 38th Aerospace Sciences Meeting & Exhibit January 10-13, 2000

7 7 / Turbulence caused by Airport infrastructure Mechanical turbulence is caused by Airport buildings, or other large obstructions. When strong winds are bent around the obstacles, this creates the turbulence. When the wind is strong and pass over a large enough obstacle they can create turbulent areas as high as 10 time obstacle height. 7

8 Toulouse-Blagnac Airport Trials UFO Dissemination Workshop NLR, Amsterdam

9 9 / UFO Toulouse-Blagnac Airport Trials Field tests in Toulouse Airport April-May 2014 X-band Radar and 1.5 micron Lidar sensors trials for monitoring Wind/EDR in Glide slope and in 3D around the airport (until 10 km) EDR Diurnal cycle study with X-band Radar & Lidar Profilers by UPMC Correlation with In-Situ Measurements (Experimental TUB Aircraft) Comparison with Mode-S EHS Downlink Data post-processed by KNMI Use of High Resolution Weather Forecast Models (HARMONIE, MHRPS) Records Database Uploaded on UFO ftp server (hosted by TUD)

10 Co-localized X-band E-scan Radar & 1.5 micron 3D scanner Lidar 10 /

11 11 / Map of Sensors Positions at Toulouse-Blagnac Airport Runway anemometer Radar Profiler ADSB-UHF E-scan Rdar Lidar scanner Mode S Data Disdrometer Rain Gauge In Situ Aircraft Probes Glide Slope axis of 32L runway Lidar Scanner

12 12 / Other sources around Toulouse-Blagnac Airport Runway treshold Mode S Data Downlink Network of Rain Gauge HARMONIE (KNMI) HR Weather Forecast Models MHRPS (MF) Lidar Profiler C-band Weather Radar

13 13 / UFO Trials on Toulouse Airport (3 mn video)

14 Diurnal EDR Study with Radar/Lidar Profilers UFO Dissemination Workshop NLR, Amsterdam

15 15 / Radar/Lidar Profilers CURIE X-band Radar Profiler WindcubeV2 Lidar Profiler

16 16 / Study of Diurnal Cycle of turbulence intensity (EDR) Time series of EDR during day-time (red lidar, blue radar) Good matching between Radar and Lidar Data Diurnal cycle of turbulence intensity Both radar and lidar measurements reveal the diurnal cycle of turbulence intensity based on EDR estimates. The magnitude of EDR estimates are close one to the other during day time. X-band Radar Profiler EDR Retrieval by Doppler Spectrum Width 1.5 micron Lidar Profiler EDR Retrieval by Velocity Spectrum

17 17 / Curie Radar Measurement Observation of the diurnal cycle of turbulence intensity based on EDR estimates: During day-time, convection occurs within the entire measurement range (up to the top of the boundary layer), turbulence is fully developed During night time, convection stops, the low is laminar and the turbulence is very weak and intermittent. This results from the fact that during night time, the lower atmosphere is stably stratified, thus preventing the occurrence of developed turbulence.

18 Electronic-Scanning X-band Radar Monitoring of Wind/EDR UFO Dissemination Workshop NLR, Amsterdam

19 19 / Radar 3D Scanning Strategy E-Scan X-band Radar 3 simultaneous Beams in Elevation

20 20 / Block Diagram of X-band Radar for WIND & EDR Processing Processing Chain for Wind & EDR Monitoring

21 21 / EDR RETRIEVAL ALGORITHMS D v Algorithms studied for Radar: v ' r Structure function (Kolmogorov model) ( s) = 1 ε s 1 H H k = 1 ( r, θ, φ) = v r D ( ) v s Cv ( v 3/ 2 ' r ( r, θ, φ) v 1 ( r, θ, φ) H ' r H k = 1 ( r v + s, θ, φ)) r ( r, θ, φ) 2 EDR : ε D v ( s) C v ε 2/3 s 2/3 Red curve: The average Doppler structure function Dashed blue line: The best fit Kolmogorov models for the structure function Spectrum width Algorithm If then If σ r < r.σ θ then Wind field Algorithm Variance of Wind field r.σ θ < σ r [ ] 3 3/ / 2 σ / σ (1.35 A) ( + r σ ) ε v r 3 σ v ε r. A σ θ 3/ 2 θ 1.53 < A < 1.68

22 22 / 2D Wind Field Elevation : 3.5 X-Band Radar : Analysis - 06/05/2014 Elevation : 5 Elevation : 6.5 Wind main direction : -50 wrt north 2 wind main directions Wind radial velocity span : 0 to 20m/s EDR Retrieval (e.g. by Structure Function) Elevation : 3.5 Elevation : 5 Elevation : 6.5

23 23 / EDR/wind from scanning radar X-Band Radar : Analysis - 24/04/2014 2D Wind Field Elevation : 3.5 Elevation : 5 Elevation : 6.5 Heading ~0 EDR Retrieval Wind main direction EDR value (in db) Wind main radial velocity ~8m/s EDR values below -2dB (0.01) on the main part of the map Some high EDR values (> 1.5dB) especially at low elevation

24 1.5 micron 3D Scanner Lidar Monitoring of Wind/EDR UFO Dissemination Workshop NLR, Amsterdam

25 25 / Integration of a high power ONERA laser into a scanning lidar Windcube-based Development of a innovative fiber laser Source by ONERA : Main characteristics: ~800 ns, ~10 khz PRF, >300µJ Integrated by LEOSPHERE in a WindcubeScanning LIDAR deployed at Toulouse airport Long range measurement ~10km, Range resolution : 200 m for wind Accumulation frequency ~6Hz Elevation = 6 scan 8 /s 24/03/2015

26 26 / Lidar Scanning strategy Windcube UFO 3 modes performed on a hourly basis Vertical profiler DBS Volume scanning (9x PPI in 5min) GlidePath scanning (7x part PPI in 1min) Typical configuration 6 Hz / 8 /s

27 27 / Volume Wind Measurements with Windcube UFO Lidar 2 Example of the 05/05/ h02 22m 2D Winds allow to better understand wind flow at low altitude In case of North West Winds, Winds fluctuations were observed on radial data and better on volume winds Wind fluctuations should have been induced by buildings (terminal of Blagnac- Airport) 3 217m 5 347m

28 28 / Methodology to assess wind accuracy Accuracy of wind speed of several methods and sensors has been assessed with a reference sensor a short range vertical profiler LIDAR 1. This LIDAR has been validated before (In January) and after (In June) the campaign of Toulouse (April) with a certified vertical profiler LIDAR 2014/ /06

29 29 / Methodology to assess wind accuracy Accuracy of wind speed of several methods and sensors has been assessed with a reference sensor a short range vertical profiler LIDAR 1. This LIDAR has been validated before (In January) and after (In June) the campaign of Toulouse (April) with a certified vertical profiler LIDAR 2. This certified LIDAR has been validated by a certified met mast of the DTU research Lab (reference organism for wind certifications)

30 30 / Methodology to assess wind accuracy Accuracy of wind speed of several methods and sensors has been assessed with a reference sensor a short range vertical profiler LIDAR 1. This LIDAR has been validated before (In January) and after (In June) the campaign of Toulouse (April) with a certified vertical profiler LIDAR 2. This vertical LIDAR has been validated by a certified met mast of the DTU research Lab 3. The anemometer of this certified met mast has been certified by the Danish accreditation body and validated in wind tunnel in March 2012

31 31 / Volume Wind Measurements with Windcube UFO Lidar Wind speed (m/s) /13 00:00 05/15 00:00 Time 05/17 00:00 WINDCUBE V2 Scanning Doppler LIDAR 05/19 00:00 05/21 00:00 Retrieved data available at 3km 96% Comparisons with certified WindcubeV2 LIDAR show good agreement better than 0.2m/s in abs difference but with a relative high deviation 1.7m/s due to the sampling of 3D measurements only 2 per 10min and the use of extrapolation for lowest altitude (log law) Scanning LIDAR Winds (m/s) m - Extrapolated y = 0.94*x R 2 = 73% Windspeed linear Scanning Lidar (m/s) y = 0.984*x R 2 = 77% 200 m 200 m 200 m linear WINDCUBE V2 Winds (m/s) WINDCUBE V2 (m/s)

32 32 / Volume Wind Measurements with Windcube UFO Lidar Better agreements are obtained with the strategy using 9 PPIs instead of 5 B Height AGL Scan pattern #A: 9 Scans Bias (m/s) Std. Dev of Diff (m/s) Height AGL Scan pattern #B: 5 scans Bias (m/s) Std. Dev of Diff (m/s) * Time Synchronization issue fixed

33 33 / Lidar EDR Retrieval Dimension en m Lidar EDR retrieval algorithms validated on Von Karman 2D Turbulence Simulator + lidar simulator Improvement of previous Kolmogorov 2D turbulence simulator with isotropic Von Karman 2D turbulence spectrum EDR and L 0 set as inputs Composante x de vent turbulent Dimension en m Composante y de vent turbulent Longitudinal (blue) transverse (red) structures, theory (dashed) 0.8 of individual components Vx and Vy Dimension in m Dimension en m Dimension en m EDR retrieval algorithms for UFO lidar validated on Von Karman 2D Turbulence Simulator + lidar simulator turbulent wind speed projected on lidar axis V=vx1.*cos(angle1)+vy1.*sin(angle1) Z (m) lidar measurment Y (m) m/s m²/s² azimuthal structure function for lidar measurement on simulated wind field lidar data azimuthal structure function best fit from Von Karman model averaged over 50 wind field realisations m EDR1.3 (m2/3.s-1) input EDR1/3 value Retrieved lidar EDR1/3 value Structure in m 2.s EDR1.3 (m2/3.s-1)

34 34 / EDR from scanning lidar 15-Apr :19:39 Vr (m/s) Apr :14:47 averaged 10 mn Azimuthal structure fonction Azimuth from 47 to Elevation = 6 m²/s² Azimuthal structure fonction for 150m <h< 200m EDR 1/3 = m 2/3. s-1 L0 = 505 m m EDR1/3 (m2/3.s-1) z=0050 m to0100 m z=0100 m to0150 m z=0150 m to0200 m z=0200 m to0250 m z=0250 m to0300 m z=0300 m to0350 m z=0350 m to0400 m averaged over 10 mn, no error terms 24-Apr : EDR1/3 0 07:12 08:10 09:09 10:08 11:07 12:06 13:05 14:04 15:03 16:02 Time altitude(m) Apr EDR1/3 (m2/3.s-1) averaged over 10 mn, no error terms 08:12 09:09 10:07 11:04 12:01 12:59 13:56 14:53 time EDR1/3 (m2/3.s-1) Thales Air Systems Date

35 35 / EDR from scanning lidar EDR from scanning lidar EDR1/3 (m2/3.s-1) May : EDR1/3 averaged over 10 mn, no error terms 0 06:00 06:58 07:57 08:56 09:55 10:54 11:53 12:52 13:51 14:50 15:49 16:48 Time z=0050 m to0100 m z=0100 m to0150 m z=0150 m to0200 m z=0200 m to0250 m z=0250 m to0300 m z=0300 m to0350 m z=0350 m to0400 m altitude(m) EDR^1/3 (m2/3.s-1) averaged over 10 mn, no error terms 06-May :00 08:01 09:02 10:02 11:03 12:04 13:04 14:05 15:06 16:06 EDR^(1/3) (m2/3.s-1) EDR1/3 (m2/3.s-1) Apr : EDR1/3 averaged over 10 mn, no error terms z=0050 m to0100 m z=0100 m to0150 m z=0150 m to0200 m z=0200 m to0250 m z=0250 m to0300 m z=0300 m to0350 m z=0350 m to0400 m altitude(m) Apr EDR1/3 (m2/3.s-1)averaged over 10 mn, no error terms EDR1/3 (m2/3.s-1) :33 12:19 13:05 13:51 14:37 15:22 16:08 16:54 17:40 18:26 19:12 Time 50 10:50 11:38 12:26 13:13 14:01 14:49 15:36 16:24 17:12 time

36 Radar/Lidar Comparison UFO Dissemination Workshop NLR, Amsterdam

37 37 / LIDAR 24/04/2014 Rainy & cloudy with strong wind RADAR db Blind Range has been reduced in new Radar Waveform (interleaving of a short range waveform) db CNR (Lidar) / SNR (Radar) Lidar Range Reduction in high density rain Radial Velocity m/s Homogeneous Rain favorable for Radar (range > 12 km) but also for Lidar (Range > 6 km) 2km m/s

38 38 / 24/04/14 - Radar/Lidar Correlation of Radial Speed Nb of plots: 2362 Bias (at 0 m/s): -0.2 m/s Standard Deviation: 1.08 m/s Lidar Radial Velocity (m/s) Radar Radial Velocity (m/s)

39 39 / LIDAR 28/04/2014 Some clouds located at east RADAR db db CNR / SNR Lidar Range Reduction by Localized Rain Radial Velocity 2km m/s Scattered Rain non favorable for Radar With potential extinction for Lidar in heavy localized rain m/s

40 40 / 30/04/2014 Some clouds moving at ~5m/s speed LIDAR RADAR db db CNR / SNR Lidar Range Extinction by Rain Front m/s m/s Radial Velocity 2km

41 41 / 30/04/14 - Radar/Lidar Correlation of Radial Speed (After signal thresholding in case of low SNR) Lidar Radial Velocity (m/s) Nb of plots: 323 Bias (at 0 m/s): 0.52 m/s Standard Deviation: 0.93 m/s Radar Radial Velocity (m/s)

42 42 / 06/05/2014 Briefly rainy High doppler speed clouds LIDAR RADAR db db CNR / SNR m/s m/s 2km Radial Velocity

43 43 / 06/05/14 - Radar/Lidar Correlation of Radial Speed Nb of plots: 2382 Bias (at 0 m/s): -0.8 m/s Standard Deviation: 1.18 m/s Lidar Radial Velocity (m/s) Radar Radial Velocity (m/s)

44 44 / High Resolution Lidar wind Monitoring Advanced Lidar capability to monitor turbulent wind at very low altitude generated by terrain roughtness and obstacles

45 45 / Radar/Lidar EDR Retrieval Comparison Lidar EDR Retrieval by structure function (time-altitude in the glide slope) EDR 1/3 Retrieval 300 m < Altitude < 400 m: 0.15 For Lidar and Radar Radar EDR Retrieval by Doppler spectrum Width(time-altitude in the glide slope)

46 Comparison with other weather sources UFO Dissemination Workshop NLR, Amsterdam

47 47 / In-Situ Wind/EDR Measurement with TUBS aircraft 4 Flights multiple data recorded all relevant atmospheric data items according to future ADS-B standard including online calculated EDR items for post-processing high accurate wind speed and direction including EDR calculation based on different proposed algorithms

48 48 / Toulouse in-situ EDR calculation different approaches possible accelerometer based vertical wind based true airspeed based ADS-B like one method chosen offline data six methods provided raw data provided Acronym Unit Type Description I01_Time s double Time (seconds of day) I02_Latitude deg double GPS latitude I03_Longitude deg double GPS longitude I04_PressureAltitude m double barometric altitude, calculated I05_RadioAltitude m double height above ground (radar altimeter) I06_A_C_Speed m/s double true airspeed I07_A_C_Heading deg double True Heading, LaserNav I08_AngleOfAttack deg double angle of attack I09_SlipAngle deg double angle of sideslip I10_WindSpeed m/s double wind speed, Baro/GPS/LaserNav I11_WindDirection deg double wind direction, Baro/GPS/LaserNav I12_StaticTemp deg_c double static/outside air temperature, adiabatic correction (fast sensor, nose boom) I13_StaticPressure hpa double static pressure (nose boom), position error corrected I14_EDR m^(2/3)/s double Eddy Dissipation Rate (EDR), Labitt:, M.: Coordinated Radar and Aircraft Observations of Turbulence, FAA-RD-81-44, Project Report ATC- 108, Lincoln Laboratory MASSACHUSETTS INSTITUTE OF TECHNOLOGY, 26 May 1981 I15_RelativeHumidity % double relative humidity, chilled mirror dew point hygrometer

49 49 / Wind Retrievals with 1.5micron 3D Scanner LIDAR Wind speed vectors superimposed with the glide slope Valuable wind data can be retrieved with Lidar Comparison with TUB Research Aircraft shows accuracy between 0.3m/s and 0.9m/s Height AGL Abs Diff (m/s) Std. Dev (m/s) 130 0,914 0, ,371 0, ,672 0, ,319 0,298

50 50 / EDR Retrieval: Comparison between 3D scanner Lidar & In-Situ EDR1/3 (m2/3.s-1) z=0200 m to0250 m z=0250 m to0300 m z=0300 m to0350 m z=0350 m to0400 m LIDAR averaged over 10 mn, no error terms 20-May : EDR1/3 0 08:43 09:49 10:54 12:00 Time EDR1/3 (m2/3.s-1) EDR avion TUB Labbit 2014_05_20_Flight1... : EDR1/3 z=0200 m to m z=0250 m to m z=0300 m to m z=0350 m to m 0 08:09 08:25 08:41 08:56 09:12 09:28 09:43 09:59 10:15 10:30 10:46 11:0 Time

51 51 / Mode-S EHS Downlink data during Toulouse trial by KNMI Interrogation of registers Change on 2014/04/17 12:15 UTC Before 2014/04/17 12:15 : BDS40+BDS50+BDS60 After 2014/04/17 12:15 : BDS40+BDS44+BDS45+BDS50

52 52 / Comparison with HR Weather Forecast Model Set-up of Harmonie & MHRPS model domain for Toulouse 1km resolution: 500 x 500 grid points Calculation of Model background error Characteristics Observations Assimilation in inner area (400x400 grid points) First focus on 2014/05/23 12UTC Preprocessing set up for UFO observations WINDCUBE 200S (radial wind, light red) Thales MFR (radial wind, red) WINDCUBE V2: (ff+dd, green dot) Curie RADAR; (EDR, not shown) TUBS: (ff+dd+t, blue dots) Other sources: WINDCUBE UFO: no data found on ftp Mode-S EHS: CAT048 format under investigation

53 53 / TKE and EDR in Harmonie TKE equation mixing length closure for the length scale L (Bougeault et al., 1989). L depends on local buoyancy, varies in stable stratified layers, in/outside of clouds and in proximity to the ground

54 54 / KNMI model Collocations of X-band Radar Profiler at Toulouse April 9th min model data interpolated linearly to observation time and radar location HARMONIE model EDR 2.5km (top) 1.0km (bottom) Observations and 1.0km model are slightly less dissimilar than at 2.5km, mostly due to an increased dynamic range in model EDR Resolution is determines interpretation of model EDR. And thus data fusion

55 55 / Comparison of Mode S EHS with LIDAR Profiler Comparison of Mode S EHS data available for the 17 th of April Similar trends on wind speed and direction between Mode S EHS and Windcube7v2 Lidar Dispersions of wind speed and direction vary with time and from a wind source to another For Wind direction, dispersion is high with low winds (below 4 m/s) Statistical analysis of accuracy For Wind Speed, mean difference is 1.82m/s and its deviation is 2.49m/s

56 56 / Comparison of Mode MRAR with LIDAR Profiler MRAR Data from the 17 th of April to the beginning of May 10min averaging Very good agreement between the two sensor technology For wind speed, mean difference is 0.51m/s and dispersion is 0.3m/s

57 57 / Synthesis on Wind measurements accuracy To be consolidated Results to be completed

58 Synthesis & Lessons Learnt UFO Dissemination Workshop NLR, Amsterdam

59 59 / WIND/EDR Monitoring: Information sources Final Approach Fix (FAF) ILS Glide Slope Wind Runway 3000 m / 60 m (typically Threshold ILS Interception Area HR Forecast Model Altitude between 2000 and 6000 ft (typically) Final Approach Mode-S Downlink Radar/Lidar Sensors ILS Glide Slope : 3 (5.2%) 4 Max course deviation : 2 horizontal deviation at FAF : +/-800m Landing H = 400 ft 2500 m 400 m m (3 NM) m (13.6 NM) Touch - Down Area

60 60 / Information Sources according to weather conditions Light Rain Weather Heavy Rain Weather HR Weather Forecast HR Weather Forecast Radar Lidar Radar 50 km 10 km 50 km 10 km Clair Air Weather Foggy Weather HR Weather Forecast HR Weather Forecast Lidar 50 km 10 km 50 km 10 km

61 61 / Information Sources Accuracy Altitude TO BE UPDATED & CONSOLIDATED 1500 m TOMORROW TODAY 1000 m Mode S MRAR HR Weat her Forec ast Mode S EHS 500 m Lidar Radar HR Weather HR Weather Forecast Forecast (ingestion of sensors 0.5 m/s 1 m/s data) 1.5 m/s 2 m/s 2.5 m/s Wind Accuracy

62 62 / Conclusions Lessons learnt from UFO s Toulouse test campaign: Complementarity of X-band Electronic Scanning Radar and 1.5 micron 3D Scanner for Ultra-Fast and High Range monitoring at low altitude (altitude < 500 m, Range < 10 km in Glide slope) of: WIND (average on 1 mn) EDR (average on 5 mn) Complementarity with Mode-S EHS Downlink for Wind at high altitude (altitude > 500 m) in all weather conditions at low altitude (altitude < 500 m) in case of: foggy weather conditions Scattered Rain conditions Diurnal Cycle of EDR: has been observed by Radar/Lidar Profilers could be perturbed in case of high wind conditions Lessons Learnt At Low altitude, EDR generated by obstacles and buildings cannot be neglected

63 63 / References UFO (Wind/EDR Monitoring) Website: Flyer: s%26publications/ufo-official-flyer-sept-2013.pdf Video: s%26publications/thales%20ufo%20master%20 FINAL%20MUET%20LQ.mp4 Wake-Vortex Hazards Mitigation Video: Flyer: et/document/brochure-wake-vortex.pdf