Comparison of dual-Doppler lidar measurements of wind

with helicopter measurementsHironori Iwai1, Shoken Ishii1, Nobumitsu Tsunematsu1, Kohei Mizutani1, Yasuhiro Murayama1, Toshikazu Itabe1, Izumi Yamada2, Naoki Matayoshi3, Dai Matsushima4, Weiming Sha5, Takeshi Yamazaki5, Toshiki Iwasaki5

1National Institute of Information and Communications Technology (NICT)2Electronic Navigation Research Institute (ENRI)3Japan Aerospace Exploration Agency (JAXA)4Chiba Institute of Technology 5Tohoku University

AcknowledgmentsThis research was funded by the Japan Ministry of Education, Culture, Sports, Science and Technology (PI: Prof. T. Iwasaki, 19204046). The authors are grateful to the staff of the Sendai Aviation Weather Station for providing the data from the surface meteorological sensors.

Outline

IntroductionDual-Doppler lidar observations

Presentation objectivesExperimental setup & method of dual-

Doppler lidar observation and analysisResultsComparison of dual-Doppler lidar

measurements of wind with helicopter measurements on 18 June 2007

Summary

15th Coherent Laser Radar Conference, Toulouse, France, 23 June 2009

Radiosonde

Wind profiler

Introduction : Dual-Doppler lidar observationsDrechsel et al. (2009) Calhoun et al. (2006)

They compared the extracted vertical wind profiles retrieved from the dual lidar measurements to vertical wind profiles of other instruments.

• 3D wind fields retrieved from multiple PPI scans of two Doppler lidars by Multiple Doppler Synthesis and Continuity Adjustment Technique (MUSCAT)

• Comparison of MUSCAT wind fields with radiosonde/wind profiler

• “virtual towers” : vertical profiles of horizontal wind retrieved from intersecting RHI scans of two Doppler lidars

• Comparison of virtual tower with radar/sodar profiler

Presentation objectives

15th Coherent Laser Radar Conference, Toulouse, France, 23 June 2009

•To show the experimental setup during Sendai Airport experiment campaign and method of dual-Doppler lidar observation and analysis method.

•To present results of comparison of extracted horizontal wind profiles retrieved from the dual-Doppler analyses with horizontal wind profilesof helicopter measurements.

N38°15′

central district of Sendai

Sendai Airport

OouMountainRange

Tohoku Region of Honshu

Sendai BayPacific Ocean

N38°00′

E140°30′ E140°45′ E141°00′ E141°15′

Sendai Airport experiment campaign Sendai Airport experiment campaing

9 – 20 June 2007 Dual-Doppler lidar observations

14 – 19 June 2007 JAXA helicopter observations

18 – 19 June 2007Japan Sea

Installation site

Pacific Ocean

terminal building

Wind speed and direction sensorsTemperature and Dew point sensor

NICT lidar & Tohoku Univ.

Sensors

ENRI lidarTemperature and Dew point sensor

RWY09

RWY27

Surface meteorological sensors of Sendai Aviation Weather Station

MID

W

N

S

Eindustrial complex

cultivated land

cultivated land

15th Coherent Laser Radar Conference, Toulouse, France, 23 June 2009

Transceiver unit : Mitsubishi Electric Co.Scanner control & data processing system :

Mitsubishi Electric Co.wavelength 1.54 μmpulse repetition frequency 4000 Hzaccumulated shot number 500range resolution 29.9 mminimum range 80.0 mmaximum range 2472.0 mscan rate 6.0 deg/sec

Transceiver unit : Coherent Technologies Inc.Scanner control & data processing system : NICT

wavelength 2.012 μmpulse repetition frequency 100 Hzaccumulated shot number 50range resolution 90 mminimum range 315 mmaximum range ～10 kmscan rate 2.0 deg/sec

NICT lidar ENRI lidar

NICT lidar & ENRI lidar

Rooftop of ENRI Iwanuma Branch

To observe local winds at various sites To observe wake vortices of airplanes

Transportable

JAXA’s research helicopter (MuPAL-ε)

Normal mode Research mode

Main Rotor Diameter

12.2 m

Maximum Takeoff Weight

4,500 kg

Payload 1,464 kg 1,226 kg

Pilot + Crew 2+8 2+3

Maximum speed 140 ktHygrometer

Static pressure holes

Measurement component

Sensor Accuracy Range Resolution Sampling frequency

3-axis air speed Sonic Co.DA-600 /TR-99HC (probe)

0.1 m/s 0 – 60 m/s 0.005 m/s 40 Hz

Temperature -10 – 40 °C 0.025 °C

3-axis ground speed

Tamagawa Seiki Co.TA7426

0.1 m/s ±128.61 m/s 0.01 m/s 50 Hz

Dewpoint EdgeTechModel 137

±1 °C -50 – 90 °C 0.05 °C 50 Hz

Pressure Rosemount542CB

±2hPa -300 – 6096 m

0.16 hPa 50 Hz

Mitsubishi Heavy Industries MH2000A

Ultrasonic anemometer / thermometer (installed at the tip of the nose boom)

Surface meteorological data on 18 June 2007

Solar radiation

Wind direction

Wind speed

Temperature/ dew point

Dual-Doppler lidar and helicopter observations were conducted from 8:00 to 9:46 JST

southeasterly sea-breeze flow

PacificOcean

-1-2 0 1 2 3 4 5

0

1

2

-1

-2

-3

-4

ENRI

NICT

Dis

tanc

e fro

m N

ICT

lidar

(km

)

Distance from NICT lidar (km)

U,X

V,Y

N

140.88 140.90 140.92 140.94Longitude (°E)

38.12

38.14

38.16

Latit

ude

(°N

)

Dual-Doppler lidar observation and analysis

NICT lidar : Sector PPI scanelevation : 0.65, 1.0, 1.4, 1.8, 2.2,

2.6, 3.0, 3.5, 4.2, 5.0 degazimuth : 60～180deg,

resolution ～1deg～10 minutes / cycle

ENRI lidar : CAPPI scanelevation : 0.5, 1.0, 1.5, 2.0, 2.5,

3.0, 3.5, 4.0, 4.5, 5.0 degazimuth : 118～478deg,

resolution ～0.4deg～10 minutes / cycle

The x direction and u component of wind are parallel to the sea-breeze flow, and the y direction and v component are normal to the sea-breeze flow. The radial velocity components for each lidar are interpolated to Cartesian grid point using the Cressman weighting function.

Dual-Doppler analysis domain & grid spacingx : 0 - 3.9 km, ∆x = 100my : -3.3 - 1.7 km, ∆y = 100mz : 0 - 200m, ∆z = 25m

Scan strategies

SDBO JJJJJ +++=

( )obsrr

kjiOO VVJ −= ∑

,,21 λ

2

,,21 DJ

kjiDD ∑= λ

( ) ( ) ( )

∇+∇+∇= ∑ ∑∑

kji kjiws

kjivsusS wλvλuλJ

,, ,,

22

,,

2222

21

zw

yv

xuD

∂∂

+∂∂

+∂∂

=ρρρ

This 3D variational method introduced by Gao et al. (1999) is a procedure that minimizes a cost function J=J(u,v,w), as follows :

Difference between the analyzed radial velocity (Vr) and the observed radial velocity (Vr

obs)

Weak anelastic mass constraint on the analyzed wind field

Smoothness constraint

( ) ( ) ( )

−+−+−= ∑∑∑ 2

,,

2

,,

2

,,21

bkji

wbbkji

vbbkji

ubB wwλvvλuuλJ

3D variational method (3DVAR)

Background term. (ub, vb, wb) is the background velocity to be derived from ENRI lidar data by a modefied VAD analysis (Banta et al. 2002).

8:00 8:128:10 8:22 8:24

8:36

8:34

8:46

8:04:37-8:05:52 8:11:02-8:12:17 8:31:09-8:32:24

8:36:43-8:37:58

150m 100m 100m

25m8:43:32-8:44:47

50m

8:48 9:008:58 9:10

8:50:24-8:51:39 8:57:15-8:58:30100m150m

9:12 9:249:22 9:34 9:36 9:46

9:15:10-9:16:25 9:27:57-9:29:12 9:34:55-9:36:1050m 150m 100m

9:43:50-9:45:0550m

Observation schedule

Dual-Doppler lidar JAXA helicopterstart end

start - endheight

Total 11 flight passes and about 500 samples

Interpolation of JAXA helicopter data to Cartesian grid point

The horizontal resolution of the JAXA heliborne sensor is about 1.25 m and differs from dual-Doppler analysis.

To eliminate the difference in the horizontal resolution, the wind data measured by the JAXA heliborne sensor are averaged on the Cartesian grid using the radii of influence for the Cressman weighting function applied in dual-Doppler analysis.

3D wind fileds Flight pass @ 150 m AGL

w(m/s)

u(m/s)

v(m/s)

Vd(deg)

Comparison of 3DVAR wind fields with helicopterDual-Doppler lidar：8:36 - 8:46JAXA helicopter：8:36:43 - 8:37:58@ 25 m AGL

Dual-Doppler lidar：8:36 - 8:46JAXA helicopter：8:43:32 - 8:44:47@ 50 m AGL

Dual-Doppler lidar：8:24 - 8:34JAXA helicopter：8:31:09 - 8:32:24@ 100 m AGL

Dual-Doppler lidar：9:36 - 9:46JAXA helicopter：9:43:50 - 9:45:05@ 50 m AGL

·The individual differences of horizontal wind speed and wind direction on each grid points are larger.→ Different temporal and spatial resolution of the measurement systems. ·There is a tendency that the differences of horizontal wind speed and wind direction on each individual grid points are larger in the area where the intersection angle of beam of two Doppler lidars is small or large.

u (m/s) v (m/s) Horizontal wind speed

(m/s)

Horizontal wind direction

(deg)standard deviation 0.65 0.77 0.75 16.0

bias 0.38 0.41 0.11 10.1mean 0.61 0.67 0.55 13.8

Statistical errors

Standard deviation, bias (average over differences), and mean (average over absolute values of differences) in wind components (u and v), horizontal wind speed, and horizontal wind direction, between dual-Doppler lidar and JAXA helicopter.

σNICT＋σENRI

σu＋σv＝ cosec2β

If σu＝σv and β＝30 or 150 degrees,σu＝σv＝0.76

Davies-Jones (1979) : The relationship between the intersection angle β of beams of two Doppler radars and error variance of horizontal wind speed (σu, σv).

σNICT= 0.2m/s , σENRI= 0.5m/s

Summary In this paper we described details of the retrieval method of 3D

wind filed from dual-Doppler lidar data. The horizontal wind profiles retrieved from the dual-Doppler

analyses were compared with those measured by the JAXA heliborne sensor.

The differences of horizontal wind speed and wind direction of less than 0.11 m/s and 10.1°, on average, with standard deviations not exceeding 0.75 m/s and 16°, respectively.

Future works The next step is to compare the vertical wind component retrieved from the dual-

Doppler analyses to those measured by the JAXA heliborne sensor. Since the order of magnitude of the vertical wind component is small and sensitive to the helicopter movement during the flight, it is necessary to carefully calibrate the vertical wind component measured by the JAXA heliborne sensor.

The comparison of 3D wind fields retrieved from dual-Doppler analysis to those measured by the JAXA heliborne sensor on other days should be performed.

15th Coherent Laser Radar Conference, Toulouse, France, 23 June 2009