remote sensing of multi-level wind fields with high-energy airborne scanning coherent doppler lidar

Remote sensing of multi-level wind fieldswith high-energy airborne scanning

coherent Doppler lidarJeffry Rothermel

NASA Marshall Space Flight Center, Global Hydrology and Climate Center, Huntsville, AL 35806 USA

[email protected]

Lisa D. Olivier, Robert M. Banta, R. Michael Hardesty, and James N. HowellNOAA Environmental Research Laboratories, Environmental Technology Laboratory, Boulder, CO 80303 USA

Dean R. CuttenUniversity of Alabama in Huntsville, Global Hydrology and Climate Center, Huntsville, AL 35806 USA

Steven C. JohnsonAstrionics Laboratory, NASA Marshall Space Flight Center, Huntsville, AL 35812 USA

Robert T. Menzies and David M. TrattJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA

Abstract: The atmospheric lidar remote sensing groups of NOAAEnvironmental Technology Laboratory, NASA Marshall Space FlightCenter, and Jet Propulsion Laboratory have developed and flown ascanning, 1 Joule per pulse, CO2 coherent Doppler lidar capable ofmapping a three-dimensional volume of atmospheric winds and aerosolbackscatter in the planetary boundary layer, free troposphere, and lowerstratosphere. Applications include the study of severe and non-severeatmospheric flows, intercomparisons with other sensors, and the simulationof prospective satellite Doppler lidar wind profilers. Examples of windmeasurements are given for the marine boundary layer and near thecoastline of the western United States.© 1997 Optical Society of AmericaOCIS code: (280.3340) Laser Doppler velocimetry; (280.3640) Lidar;(290.1350) Backscattering; (290.5820) Scattering measurements

References and links1. Post, M. J., R. E. Cupp, “Optimizing a pulsed Doppler lidar,” Appl. Opt., 29, 4145-4158 (1990).2. Targ, R., et al., “Coherent lidar airborne wind sensor II: flight test results at 2 and 10 µm,” Appl. Opt., 35, 7117-

7127 (1996).3. Richmond, R., D. Jewell, “U.S. Air Force ballistic winds program,” Preprints 9th Conf. Coherent Laser Radar,

Linköping, Sweden, (Swedish Defence Research Establishment, Stockholm, 1997), pp. 304-307.4. Werner, C., P. Flamant, G. Ancellet, A. Dolfi-Bouteyre, F. Köpp, H. Herrmann, C. Loth, J. Wildenauer, “WIND:

An advanced wind infrared Doppler lidar for mesoscale meteorological studies,” Proc. 5th Conf. Coherent LaserRadar, Munich, (Deutsche Forschungsanstalt fur Luft- und Raumfahrt, Munich, 1989), pp. 35-38.

5. Bilbro, J. W., G. H. Fichtl, D. E. Fitzjarrald, M. Krause, “Airborne Doppler lidar wind field measurements,” Bull.Amer. Meteorol. Soc., 65, 348-359 (1984).

6. Bilbro, J. W., C. A. DiMarzio, D. E. Fitzjarrald, S. C. Johnson, W. D. Jones, “Airborne Doppler lidarmeasurements,” Appl. Opt., 25, 3952-3960 (1986).

7. Rothermel, J., MACAWS World Wide Web page, http://wwwghcc.msfc.nasa.gov/macaws.html.

#4016 - $10.00 US Received November 21, 1997; Revised January 10, 1998

(C) 1998 OSA 19 January 1998 / Vol. 2, No. 2 / OPTICS EXPRESS 40

http://wwwghcc.msfc.nasa.gov/macaws.html

8. Howell, J. N., R. M. Hardesty, J. Rothermel, R. T. Menzies, “Overview of the first Multicenter Airborne CoherentAtmospheric Wind Sensor (MACAWS) experiment,” Proc. SPIE, 2833, 116-127 (1996).

9. Menzies, R. T., D. M. Tratt, “Airborne CO2 coherent lidar for measurements of atmospheric aerosol and cloudbackscatter,” Appl. Opt., 33, 5698-5711 (1994).

10. Amirault, C. T., C. A. Dimarzio, “Precision pointing using a dual-wedge scanner,” Appl. Opt., 24, 1302-1308(1985).

11. Rye, B. J., R. M. Hardesty, “Spectral matched filters in coherent laser radar,” J. Mod. Opt., 41, 2131-2144(1994).

12. Lee, R. W., K. A. Lee, “A poly-pulse-pair signal processor for coherent Doppler lidar,” Coherent Laser Radar forthe Atmosphere, OSA Technical Digest Series, (Optical Society of America, Washington, DC, 1980), WA2, 1-4.

13. Rothermel, J., D. R. Cutten, R. M. Hardesty, J. N. Howell, S. C. Johnson, D. M. Tratt, L. D. Olivier, R. M. Banta,“The Multi-center Airborne Coherent Atmospheric Wind Sensor,” Bull. Amer. Meteorol. Soc., accepted (1998).

14. Browning, K. A., R. Wexler, “The determination of kinematic properties of a wind field using Doppler radar,” J.Appl. Meteorol., 7, 105-113 (1961).

15. Rothermel, J., D. A. Bowdle, J. M. Vaughan, M. J. Post, “Evidence of a tropospheric aerosol backscatterbackground mode,” Appl. Opt., 28, 1040-1042 (1989).

16. Kavaya, M. J., R. T. Menzies, “Lidar aerosol backscatter measurements: systematic, modeling, and calibrationerror considerations,” Appl. Opt., 24, 3444-3453 (1985).

17. Winant, C. D., C. E. Dorman, C. A. Friehe, R. C. Beardsley, “The marine layer off northern California: Anexample of supercritical channel flow,” J. Atmos. Sci., 45, 3588-3605 (1988).

18. Mohr, C. G., L. J. Miller, “CEDRIC - A software package for Cartesian space editing, synthesis, and display ofradar fields under interactive control,” Preprints 21st Radar Meteorological Conference, Edmonton, Alta., Canada,(American Meteorological Society, Boston, 1983), pp. 569-574.

19. Zhang, Z., T.N. Krishnamurti, “Ensemble forecasting of hurricane tracks,” Bull. Amer. Meteorol. Soc., 78, 2785-2796 (1997).

20. Emanuel, K. B., et al., “Report of the first prospectus development team of the U.S. Weather Research Program toNOAA and the NSF,” Bull. Amer. Meteorol. Soc., 76, 1194-1208 (1995).

21. Huffaker, R. M., M. J. Post, J. T. Priestley, F. F. Hall, Jr., R. A. Richter, R. J. Keller, “Feasibility studies for aglobal wind measuring satellite system (WINDSAT): Analysis of simulated performance,” Appl. Opt., 22, 1655-1665 (1984).

22. Kavaya, M. J., G. D. Spiers, E. S. Lobl, J. Rothermel, V. W. Keller, “Direct global measurements of troposphericwinds employing a simplified coherent laser radar using fully scalable technology and technique,” Proc. SPIE,2214, 237-249 (1994).

23. Baker, W. E., et al., “Lidar-measured winds from space: a key component for weather and climate prediction,”Bull. Amer. Meteorol. Soc., 76, 869-888 (1995).

1. Introduction

Many atmospheric flows occur in the absence of precipitation and optically-dense cloud.Therefore these flows are amenable to study by active optical remote sensing techniquesutilizing backscattered signals from naturally-occurring aerosols that passively trace theflow. Flows accompanied by precipitation and thick cloud, which can be probed using othertechnologies such as radar, often contain important aspects that are amenable to lidar remotesensing as well, such as the environment of a severe thunderstorm. Over the past twodecades coherent Doppler lidar has been demonstrated to be an effective tool for atmosphericresearch under a variety of meteorological conditions. The versatility of deployment ofcoherent Doppler lidar is enhanced significantly when it is placed on an airborne platform.One can not only study flows and other atmospheric features that are inaccessible orinadequately sampled by more conventional sensors, but also study properties of surfacetargets. It is also possible to simulate aspects of prospective satellite Doppler wind lidar thatcannot be addressed using ground-based measurements.

The atmospheric lidar remote sensing groups of NASA Marshall Space FlightCenter (MSFC), NOAA Environmental Technology Laboratory (ETL), and the JetPropulsion Laboratory (JPL) together have developed an airborne Doppler lidar systemcapable of mapping the wind and aerosol backscatter distribution over large volumes of theplanetary boundary layer (PBL), free troposphere and lower stratosphere in regions ofadequate backscatter. This instrument, the Multi-center Airborne Coherent AtmosphericWind Sensor (MACAWS), was developed during 1992-5 in large part by using hardware



resources and expertise acquired in the course of previous atmospheric research programs.The transmitter/receiver is the CO2 coherent Doppler lidar from the highly successful NOAAWindvan, which is capable of emitting nearly 1 J/pulse at 10.6 µm and which can achievemeasurement coverage of as much as 30 km [1]. This approach permitted a more-straightforward development and considerable savings, while resulting in the mostsophisticated and versatile airborne coherent Doppler lidar to date. Although other airbornecoherent Doppler lidars have been developed [2,3], or are being developed [4], to ourknowledge MACAWS is the only system specifically designed to measure high-resolutionfields of horizontal wind velocities over a three-dimensional volume.

The concept of wind field measurements with airborne Doppler lidar wasdemonstrated in 1981 using a 14 mJ/pulse coherent lidar capable of achieving 6-10 kmcoverage in the lower troposphere [5]. A pseudo-dual Doppler technique was employedwherein the lidar beam was scanned sequentially to infer the two-dimensional horizontalwind field relative to the aircraft. The scanning capability was subsequently enhanced topermit measurement of wind fields at several vertical levels [6]. The same measurementtechnique is used for MACAWS, but with substantially greater coverage--and scientificutility--which is made possible by a significantly more-powerful transmitter. The balance ofthis article describes the MACAWS instrument, examples from field programs in 1995-6,and potential research applications. A MACAWS World Wide Web page has beenestablished which contains additional details and examples [7].

2. Instrument description

The MACAWS hardware system consists principally of the laser transmitter, receiver,telescope, optical table, scanner, inertial measurement unit, and computer (Fig. 1). The

telescope

aircraft datadistrib. sys.

window

aircraftstructure

scanner

transmitter

receiver

optical table

aircraftinterface unit

inertialnav. system

displays

datarecorder

DATA ACQUISITION& SYSTEM CONTROL

VME

BUS

computer

signalprocessor

JPLNASA MSFCNOAA ETL

Signal/data

ControlMechanical

NASA ARC

Fig. 1. Block diagram of MACAWS subsystems and environment.

primary operating characteristics are summarized in Table 1; the optical layout issummarized in Ref. [8]. The transmitter is a frequency-stable, transverse-excitedatmospheric pressure (TEA) CO2 laser [1]; several modifications were required to ensurereliability, safety, and compatibility with the aircraft environment [8]. The receiver consistsof a cryogenically-cooled infrared detector and supporting optics and electronics configuredfor coherent signal detection [1,9]. The folded telescope consists of a 0.3 m diameter off-axisparaboloidal primary mirror and secondary mirror shared by the transmitter and receiver in amonostatic configuration. The table assembly consists of a ruggedized optical table,



TABLE 1. MACAWS primary operating characteristics

Characteristic Nominal Rangevalue

Wavelength (µm) 10.6 9 - 11Transmitter TEA CO2 gas laserEnergy per pulse (J, long-pulse mode) 0.8 0.6 - 1.0Beamwidth to e-2 power points 20 cmPulse duration (µs) 3 0.4, 3a

Laser linewidth (kHz) ~300Pulse repetition frequency PRF (s-1) 20 0.1 - 30Telescope diameter (m) 0.3Line-of-sight resolution (m) 300 150 - 1200Number of scan planes 5 1 - 5Vertical resolution (km)b 0 -12.5Wind velocity accuracy (m s-1) ~1Nyquist radial velocity (m s-1)c 75Coverage (km)d 10 - 30aDuration in which 80 percent of pulse energy is emittedbDependent on range and angular separation between scan planescActual ground-relative velocity limits may differ depending on relationship between line-of-sight components of airspeedand ground velocitydDependent on distribution of aerosol backscatter and extinction

separable into two sections to facilitate integration; the table itself is upheld by a three-pointsupport structure which is fastened to the aircraft seat tracks. A large portion of the opticaltable was developed previously by JPL for a program to survey the global aerosol backscatterdistribution [9]. The scanner is composed of two computer-controlled, independently-rotating germanium wedges which refract the transmitted beam in the desired direction [10].The scanner was developed for atmospheric research programs by NASA MSFC for the firstairborne coherent Doppler lidar system (ADLS) [5]. The original scanner had the capabilityto refract the beam anywhere within a full cone angle of ~40 deg. In the presentconfiguration the wedges have been replaced with elements of greater angular thicknesswhich permit scanning anywhere within a full cone angle of ~64 deg. A dedicated inertialmeasurement unit (IMU), mounted beneath the scanner, senses aircraft attitude and speed ata rate of 20 s-1 [6]. The IMU and telescope were developed for the first ADLS as well. Thecomputer consists of a Unix-based operations control system (OCS) which orchestrates thefunctioning of each subsystem. The OCS also processes, displays, and stores raw lidar data(in limited quantities) and processed data, along with scanner settings, IMU data, locationand ground speed derived from the Global Positioning System (GPS), and aircrafthousekeeping data. MACAWS is presently configured for the NASA DC-8 research aircraft,which has a service ceiling of 12.5 km and a range of over 9400 km.

During flight laser pulses are transmitted to the atmosphere through the scanner,which is mounted within the left side of the aircraft ahead of the wing. Aerosols, clouds, orthe surface scatter a small portion of the incident radiation backward along the line-of-sight(LOS) to the receiver. In order to maintain precise beam pointing, IMU measurements areinput to the OCS, which in turn issues commands to the receiver and scanner to compensatefor aircraft attitude and speed changes. Using the same IMU measurements during signalprocessing, the OCS and receiver calculate and subtract the frequency contribution to theDoppler-shifted signal representing the component of aircraft motion along the line of sight.The resulting, range-resolved LOS velocities represent the component of wind motion withrespect to the earth. Measurement coverage varies with lidar system settings (laser outputenergy, pulse repetition frequency, LOS resolution, number of pulses averaged) andatmospheric conditions (aerosol backscatter distribution, and attenuation by water vapor,carbon dioxide, and clouds). Atmospheric signal processing is done in real time; a poly-



pulse-pair velocity estimation algorithm, implemented digitally as a matched-filter frequencydomain estimator [11], is used to calculate LOS velocities [12]. For each range gate, the fastFourier transform (FFT) of the truncated autocorrelation function is calculated fromdigitized, complex samples of the time-varying output of the signal detector. The peak of thefrequency spectrum is then found with high resolution by fitting a quadratic curve to thethree points nearest the peak of the FFT. The peak of the fitted function corresponds to theLOS velocity estimate. Pulses with excessive frequency variation due to system anomalies,such as transient optical misalignments due to turbulence, are flagged, are excluded fromsignal processing and wind estimation, and are termed “bad” pulses. On-board displays ofLOS velocity, two-dimensional wind fields, and backscattered signal intensity provide in-flight mission guidance as well as a means to assess subsystem performance and overall dataquality.

Before research flights commence, the IMU is physically aligned with the aircraft sothat measurements of roll, pitch, and pointing direction agree with those of the aircraftinertial navigation system (INS). Scanner pointing is then calibrated relative to the IMU-indicated aircraft orientation [10]. Finally, the intensity response of the lidartransmitter/receiver is calibrated by comparing backscattered signals from a target of knownreflectance with expected signal intensity calculated from the lidar signal-to-noise (SNR)equation using measured system parameters [1]. The resulting calibration factor permitsconversion of relative signal intensity (dB) to units of absolute backscatter (m-1 sr-1).

The manner in which the atmosphere is sampled with the lidar beam is determinedby the science objective(s), three-dimensional distribution of the feature or process ofinterest, aircraft altitude, aerosol backscatter distribution, attenuation, and range to target.Fig. 2(a-c) illustrates the possible sampling geometries. In the vertical profiling mode, Fig.2(a), the beam is maximally refracted to 32 deg up or down relative to the aircraft. A quasi-vertical cross section of LOS velocity and aerosol backscatter may then be obtained bymaintaining a constant flight heading [13]. Alternatively, the aircraft may fly one or moreorbits; each orbit permits the lidar to sample the atmosphere in a pattern resembling thefamiliar velocity-azimuth-display (VAD) common to ground-based radar and lidar. Twoprincipal applications of this approach are possible. First, a single horizontal wind profileabove (clockwise orbit) or below (counterclockwise) the aircraft may be calculated usingtechniques originally developed for ground-based radar, e.g., [14]. Second, by conducting theorbits at different roll angles, the angular dependence of surface scattering may be measured.

The second, and unique, capability of MACAWS is that of remotely sensing two-dimensional (2-D) wind fields. Fig. 2(b) illustrates a plan view of the sampling pattern oflidar beams. Each beam may be composed of three or more pulses that are combined duringsignal processing to improve LOS velocity accuracy and coverage. During scanning thebeam is alternately directed ~20 deg forward and aft of normal relative to the aircraftheading. At each “point” of intersection, a 2-D velocity can be calculated due to the angularseparation between perspectives. The velocity field thus represents the component of windwithin the scan plane. Without rapid and precise compensation for aircraft motions,turbulence experienced by the aircraft could cause the scanner to misdirect one or morebeams outside of the measurement plane. Using the IMU measurements of aircraft pitch,roll, and track angle, the OCS attempts to compensate for turbulent motion by issuingappropriate commands to the scanner. The angular thickness of the germanium wedgeslimits the 2-D measurement capability to ±25 deg in the vertical, beyond which there isinsufficient angular separation between fore and aft beams to calculate 2-D velocityaccurately.

An important extension of the 2-D scanning capability is three-dimensional (3-D)coverage that can be achieved by generating multiple scan planes, Fig. 2(c). The present



Fixed angle

NASADC-8

a

Range

Aircraft Heading

Aft scancomponent

Forward scancomponent

Wind

b

Multiple scan planes

NASADC-8

c

Fig. 2. MACAWS scanning capabilities: (a) Lidar beam orientation is fixed, relative either toaircraft or to surface, to obtain quasi-vertical profiles of line-of-sight velocity and aerosolbackscatter, or for studies of surface angular scattering dependence; beam elevation angle relativeto aircraft may be fixed over maximum range of +32 deg. (b) Co-planar scanning is performed tomeasure a single wind field, with 40 deg in-plane angular separation between forward and aftscans. (c) Co-planar scanning is performed at up to five elevation angles with arbitrary angularspacing to achieve volumetric coverage, over a vertical range of +25 deg.

OCS configuration permits up to five scan planes with arbitrary vertical angular spacing. Ingeneral the extent over which measurable signals may be obtained is a function of: 1) aircraftaltitude, subject to air traffic control restrictions and aircraft service ceiling; 2) angularseparation between uppermost and lowermost scan planes, subject to the refractive limit ofthe scanner; 3) aerosol backscatter distribution, a function of the aerosol physical, chemical,and optical properties; and 4) attenuation of the incident and scattered laser radiation,depending on concentrations of water vapor, CO2, aerosols, and optical thickness of cloud (ifpresent). Coverage is generally more-extensive in the PBL, where higher concentrations oflarger aerosols are responsible for larger aerosol backscatter values hence stronger signal-to-noise ratios, e.g., [15].

Resolution along the flight track ∆x in each scan plane may be approximated by:

( )∆x n d angn bn

Pd Va g≅ − +

+

+

2 1 2 21 2 (1)

where d1 is the delay in repositioning the scanner wedges to an adjacent elevation angle inthe fore or aft direction (~0.1 s), d2 is the scanner delay between the fore and aft pointingdirections (~0.6 s), na is the number of scan planes, ng is the number of pulses averagedduring signal processing, nb is the number of pulses rejected during signal processing, P is



the laser pulse repetition frequency (s-1), and Vg is the aircraft ground speed. In practiceturbulence slightly increases the time required to reposition the scanner wedges. Turbulencecan also cause brief periods of laser mode degradation or frequency jitter; the on-board pulsequality discriminator rejects these pulses. Both effects degrade both the along-track andcross-track resolution. For the case of measurements in the PBL assuming Vg = 125 m s-1, P= 20 s-1, na = 5, nb = 2, and ng = 10, Eq. (1) yields ∆x ≅ 1.0 km.

During scanning the measured LOS velocity with respect to the aircraft isdominated by aircraft motion. Therefore, this large velocity component must becharacterized accurately in order to determine the residual, ground-relative wind motion.This requires accurate knowledge of the scanner settings, aircraft attitude and speed, andfrequency distribution of the outgoing pulse [1]. Analysis of the ground calibrationmeasurements yielded a 0.1 deg root-mean-square (rms) uncertainty in scanner pointingangle, which is equivalent to a spatial uncertainty of 17 m at 10 km range. This uncertaintywas confirmed by analysis of ground strikes. Corresponding velocity errors were less than0.4 m s-1 for ground speeds of 232 m s-1 (450 kt). The largest source of velocity uncertainty isattributed to the IMU estimates of ground speed, which can amount to 0.5 - 4 m s-1. Thissource of uncertainty varied from flight to flight, but can be reduced in-flight or during post-processing by using ground speed data from the aircraft INS or GPS. Wind velocities derivedfrom the aircraft INS may differ from lidar winds at close range by ~1 m s-1 or less when amore-accurate source for ground speed is used. Measurement of aerosol backscattercoefficients is carried out by inverting the equation for the coherent lidar signal-to-noiseratio after computing the SNR [1,16]. This estimate requires knowledge of severalparameters including system range response, optical efficiency, atmospheric extinction, shotnoise level and pulse energy. For MACAWS, system optical efficiency parameters aremeasured while the aircraft is stationary; these values are then applied to the flight data.Given an accurate measurement of the system parameters, backscatter coefficients aremeasurable to within ~3 dB if the key system parameters do not change significantly betweencalibrations. By comparison, atmospheric aerosol backscatter at 10.6 µm wavelength mayvary over five orders of magnitude or more, e.g., [15]. More details on measurementuncertainties are found in Ref. [13].

3. Results

Laboratory integration and ground tests were conducted at JPL, Pasadena, California, duringMarch 1 - July 19, 1995; the first atmospheric returns were obtained on May 18, 1995. Thefirst flight tests were conducted during 13-26 September 1995 over the western US andeastern Pacific Ocean; subsequent flight experiments were conducted 31 May - 2 July 1996over the western and central US and eastern Pacific Ocean. Between flight programs,modifications were made to improve performance, especially under turbulent flowconditions. This section contains examples from the 1995 and 1996 flight experiments.

Accurate mapping of the marine PBL wind structure is important to understandingthe interaction between the atmosphere and ocean. During the 1995 flights, MACAWS wasused to characterize the PBL wind regime near the coast of Oregon as part of a fieldexperiment to study oceanic internal waves. Fig. 3 illustrates the 3-D wind distributionobserved as the aircraft flew just above the top of the PBL. Weaker wind speeds and a shift inwind direction were measured by the aircraft INS; this feature is characteristic of weak windshear across the top of the PBL associated with the flow transition to the free troposphere. Atthe lowest elevation angle the wind field was measured down to the ocean surface, belowwhich the lidar beam is extinguished. “Clustering” of wind vectors, and variations in along-track and LOS resolution, are due to signal processor settings and the effect of turbulence on



Fig. 3. (QuickTime animation 142 KB) Marine PBL wind structure measured over five scanplanes during 23 September 1995, 1827-1831 UTC (see Fig. 2(b)). Aircraft was on a southerlyheading ~30 km west of the coast of northern Oregon at 908 m altitude over the site of the CoastalOcean Probing Experiment (COPE). Along-track distance is 23.5 km. Row of vectors at bottom ofeach wind field shows winds derived from aircraft inertial navigation system. Vectors point intothe wind. Bottom panel shows corresponding vertical distribution of scan planes (see Fig. 2(c)).

scanner and laser performance (Eq. (1)). Clustering arises when LOS resolution isconsiderably finer than along-track resolution, in this case 300 m and ~730 m, respectively.

Surface winds off the California coast are northwesterly to northerly in summer,resulting from flow around the east side of the subtropical high-pressure system over thePacific Ocean. This high is accompanied by subsidence and a strong marine temperatureinversion usually a few hundred meters deep. The coastal mountain-range topographyinteracts with the northerly flow in this marine-inversion layer to produce a variety ofinteresting flow phenomena. For example, when this flow passes one of the many capes andpoints that protrude into the wind along the California coast, structures referred to as“hydraulic expansion fans” have been found [17]. Such features are marked by strongvariation along the vertical and cross-shore directions. To study this variability the aircraftflew sets of parallel line segments just offshore past Point Arena on 30 June 1996 during1950-2110 UTC at an altitude of 0.49 km. Fig. 4 shows the wind distribution observedwithin five scan planes. Figs. 3 and 4 also serve to illustrate the wind vector displays that areproduced in real time. The data from all elevation angles in Fig. 4 were reanalyzed alongconstant-height levels as shown in Fig. 5. Evident in the marine PBL is the northerly flow,the strong variability in the cross-shore direction (especially at 150 m ASL), and thestructural changes in the vertical. Grid spacing in the east-west and north-south directionswas 300 m, and 25 m in the vertical. The National Center for Atmospheric Research’s(NCAR) software package ‘Custom Editing and Display of Reduced Information inCartesian space’ (CEDRIC) was used to determine the 2-D wind velocities using the two-



http://epubs.osa.org/oevideo/330.mov

Fig. 4. (QuickTime animation 236 KB) Example of full-resolution wind field measurements atmultiple elevation angles, obtained near Pt. Arena, California on 30 June 1996, 171134 - 171500UTC, at ~0.49 km height ASL. Along-track distance is 21.5 km. Wind fields were measured at 5elevation angles from 0 to -2.77 deg. Along-track and LOS resolution are ~590 m and 300 m,respectively. Lidar beam intersected sea surface at lowest elevation angle. Small-scale windvariation indicates presence of secondary atmospheric circulations and turbulence, as well asresidual errors.

Fig. 5. (QuickTime animation 136 KB) Interpolated wind fields based on full-resolutionmeasurements shown in Fig. 4. Terrain contours are in 200-m increments. Left-hand side showshorizontal slice through Cartesian grid volume at different heights above sea level (ASL). Right-hand side is enlargement of area marked by the dashed box. Wind barbs point downwind.





equation solution [18]. Interpolation of the raw wind fields to a Cartesian grid is an essentialstep toward incorporation of MACAWS data into atmospheric numerical forecast models.

4. Summary and conclusions

In 1992 the MACAWS team set out to develop what has become perhaps the most powerfuland sophisticated airborne coherent Doppler lidar system in the scientific history ofatmospheric remote sensing. We have successfully achieved that goal, and can nowconcentrate our attention on the many scientific applications of which the instrument iscapable. With spatial resolution of order 1 km, ~1 m s-1 velocity accuracy, a Joule-class lasertransmitter for extended propagation in optically-clear air, and the range and durationafforded by a large multi-engine aircraft, MACAWS provides a unique means to study avariety of atmospheric processes and features that may be poorly sampled by existing orplanned sensors such as radar wind profiles or Doppler radars.

Numerous research applications for MACAWS have been identified [13], includingbut not limited to the following. New measurements are needed within tropical cyclones inorder to improve forecasts of intensification and tracking. Numerical modeling techniquescan now identify regions of the hurricane where more observations are needed in order toreduce forecast uncertainties [19]. Plans are underway to employ MACAWS in such amanner during the 1998 Atlantic hurricane season. In particular, MACAWS has thepotential to measure uniquely the winds within the optically-clear eye in the freetroposphere; conventional airborne weather radars currently require the addition of reflectivematerial (chaff) in order to visualize the flow. Observations in the eye, at low levels near therain bands, and elsewhere in the cyclone where observations are not precluded by optically-dense cloud, hold the potential to guide forecast model improvements.

Research into coastal processes is hampered by a lack of detailed observations.Interaction of marine boundary layer flows with coastal topography may strongly influencecoastal meteorological conditions, as illustrated in Figs. 4 and 5. Another example is the“southerly surge” phenomenon which can affect the meteorology of the southern Californiacoast, in extreme cases affecting flight operations at the Los Angeles International Airport,e.g., [20]. Data from a weak southerly surge case were obtained during the 1996 flights andare currently being analyzed. The strengths of the MACAWS platform in investigatingcoastline meteorological systems make it ideal for probing the structure of these surges, andthey are expected to be an important target for future research flights.

In addition to atmospheric research, MACAWS has applications to the design,performance simulation, and validation of existing or planned satellite sensors. The conceptof direct measurement of global tropospheric winds from space with Doppler lidar has beenstudied for some time, e.g., [21-23]. Such measurements would fundamentally improve ourunderstanding of global and climate change, as well as global and regional-scalehydrological cycles [23]. In the absence of a heritage of satellite Doppler lidar windmeasurements, performance simulations with measured--rather than simulated--data arehighly desirable to reduce uncertainties in lidar simulation models and to begin to developthe necessary interpretive skills. Some issues can only be addressed from the airborneperspective, such as utilization of the frequency distribution and backscattered intensity ofground returns from land or ocean surface during signal processing. MACAWS has thecapability to simulate a number of scanning strategies, as well as to assess satellite Dopplerlidar in the presence of clouds and organized atmospheric flow structures.

Our experience has demonstrated that the technology necessary for the reliableoperation of high-power, frequency-stable CO2 lidar is mature and presents no technologicalrisks. Moreover, use of a large, high-energy CO2 laser poses no special integration problems.Finally, the sharing of existing hardware, software, and expertise, the minimization of new



hardware development, and the identification of mutually-compatible research interests, hasresulted in both a substantial cost savings and the successful development and application ofa world-class airborne coherent Doppler lidar system.

Acknowledgments

We gratefully acknowledge Dr. Ramesh K. Kakar, Atmospheric Dynamics and RemoteSensing Program, Office of Mission to Planet Earth, NASA Headquarters, and theAtmospheric Lidar Division, Environmental Technology Laboratory, NOAA EnvironmentalResearch Laboratories, without whose support this program would not be possible. Weacknowledge the assistance of the DC-8 pilots and ground support team of the NASA AmesResearch Center. We also acknowledge Diane Samuelson, NASA Marshall Space FlightCenter, who helped prepare the QuickTime animations.



remote sensing of multi-level wind fields with high-energy airborne scanning coherent doppler lidar

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