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CAMEX-3 Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) Table of Contents
The Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) is an airborne, pulsed, scanning, coherent Doppler laser radar (lidar) that remotely senses the distribution of wind velocity and aerosol backscatter within three-dimensional volumes in the troposphere and lower stratosphere. MACAWS, presently configured to fly on the NASA DC-8 research aircraft, was developed jointly by the atmospheric lidar remote sensing groups of NASA Global Hydrology and Climate Center, NASA Marshall Space Flight Center (MSFC), NOAA Environmental Technology Laboratory (ETL), and the Jet Propulsion Laboratory (JPL). Nearly all of the MACAWS hardware components were developed for previous atmospheric research programs. The re-use of these field-tested components has resulted in considerable cost savings to the Government. Interagency cooperation among the atmospheric lidar remote sensing groups also ensures that research activities are both scientifically synergistic and cost-effective. For example, the MACAWS laser transmitter is that of the highly successful mobile ground-based Doppler lidar ("Windvan") developed by NOAA ETL and deployed for a number of experiments. Included at that site are descriptions of MACAWS participation in previous experiments, detailed analysis of data collection methods, and an outlook for future experiments. A copy of the full MACAWS homepage documentation, as well as this document is bundled with the CAMEX-3 dataset. This block diagram illustrates the major components of MACAWS, their interrelationship, and the institutional responsibilities for providing and maintaining them. Most of the components were developed and used extensively for previous airborne and ground-based research programs. MACAWS is presently configured to fly on the NASA DC-8 research aircraft, and consists of the following major components:
During flight, laser pulses are generated and transmitted to the atmosphere through a scanner mounted within the left side of the aircraft ahead of the wing. Aerosols, clouds, or the Earth's surface scatter a small portion of the incident radiation backward along the line-of-sight (LOS) to the receiver. INS measurements of aircraft pitch, roll, and velocity are continually input to the OCS, which in turn issues commands to the scanner to compensate for aircraft attitude and speed changes in order to maintain precise beam pointing. Using the same INS measurements, the OCS calculates and subtracts the frequency contribution to the Doppler-shifted signal due to the component of aircraft motion along the line-of-sight. The resulting LOS velocities are with respect to Earth coordinates. On-board displays of LOS velocity and backscattered intensity are available for real-time assessment of lidar performance and overall data quality, and for in-flight mission guidance. Raw digitized in-phase and quadrature components of the amplified detector output (in limited quantities), processed data, aircraft housekeeping data, scanner settings, and INS measurements are stored on digital audio tape for subsequent analysis. For additional in-flight guidance, visible and infrared satellite imagery may be acquired directly from polar orbiting satellites using an aircraft facility resource. Two-dimensional Scanning Lidar uses laser radiation and a telescope/scanner similar to the way radar uses radio frequency emissions and a dish antenna. Lidar scatterers sensed by MACAWS are typically aerosols with diameter of order 1 micron (0.00004 inch) or less, which act as passive wind tracers. Optically thick cloud and precipitation can attenuate the lidar beam. On the other hand, radar scatterers may consist of clouds and hydrometeors (e.g., rain or frozen precipitation, which have a definite fall velocity). In optically clear air, radar return signals may be obtained from insects and birds, and from radio refractive index variations due to humidity, temperature, or pressure fluctuations. Lidar beam divergence is two to three orders of magnitude smaller compared to conventional 5 and 10 cm wavelength radars. For example, the MACAWS lidar beam diameter for a single pulse is only ~1 m at 10 km slant range. This characteristic permits unambiguous velocity measurements near clouds and surface features, without susceptibility to velocity bias due to ground clutter and side lobe contamination sometimes experienced by radar under marginal reflectivity conditions and strong reflectivity gradients. CAMEX-3 Flight Program and Sample ResultsWith the feasibility of Doppler lidar wind coverage having been confirmed in 1995 during a science demonstration flight through tropical cyclone Juliette (Rothermel et al. 1997), MACAWS was included in CAMEX III. MACAWS observations fell into three general categories: storm environment, eyewall transects, and low-level inflow. This figure shows a portion of eyewall winds mapped by MACAWS in the northwest quadrant during an eyewall transect of hurricane Bonnie during landfall in the Carolinas on 26 August 1998. The approximate location of the analysis is indicated on a visible satellite image obtained ~20 minutes earlier. Gridded two-dimensional wind velocities were calculated using the publicly-available NCAR Doppler radar analysis package CEDRIC (Mohr and Miller 1983). Owing to the vertical distribution of the five scan planes (-20, -10, 0, 10, and 20 deg), horizontal wind fields were calculated from 10.1 - 10.4 km in 100 m vertical intervals. Results show an extremely tight velocity gradient and curvature along the inner edge of the eyewall, in good agreement with flight-level winds derived from the aircraft INS (Rothermel et al. 1999). These results represent the first Doppler lidar wind field measurements within a hurricane. Selected ReferencesThe following references provide additional information on MACAWS and on the airborne Doppler lidar system that was flown in the 1980's. Selected papers based on the latter have been included as additional examples of research applications; those results are based on a much lower-energy system with less coverage compared to MACAWS. Amirault, C.T. and C.A. DiMarzio, "Precision pointing using a dual-wedge scanner," Appl. Opt., 24, 1302-1308 (1985). Baker, W.E., G.D. Emmitt, F. Robertson, R.M. Atlas, J.E. Molinari, D.A. Bowdle, J. Paegle, R.M. Hardesty, R.T. Menzies, T.N. Krishnamurti, R.A. Brown, M.J. Post, J.R. Anderson, A.C. Lorenc, and James McElroy, "Lidar-measured winds from space: a key component for weather and climate prediction," Bull. Amer. Meteor. Soc., 76, 869-888 (1995). Bilbro, J.W., G.H. Fichtl, D.E. Fitzjarrald and M. Krause, "Airborne Doppler lidar wind field measurements," Bull. Amer. Meteor. Soc., 65, 348-359 (1984). Bilbro, J.W., C.A. Dimarzio, D.E. Fitzjarrald, S.C. Johnson and W.D. Jones, "Airborne Doppler lidar measurements," Appl. Opt., 25, 3952-3960 (1986). Blumen, W. and J.E. Hart, "Airborne Doppler lidar wind field measurements of waves in the lee of Mount Shasta," J. Atmos. Sci., 45, 1571-1583 (1988). Carroll, J.J., "Analysis of airborne Doppler lidar measurements of the extended California sea breeze," J. Atmos. Oceanic Tech., 6, 820-831 (1989). Emmitt, G.D., "Convective storm downdraft outflows detected by NASA/MSFC's 10.6 micron pulsed Doppler lidar system," NASA CR-3898, Marshall Space Flight Center, Huntsville, AL, 46 pp. (1985). Howell, J.N., R.M. Hardesty, J. Rothermel, and R.T. Menzies, 1996: Overview of the first Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) experiment: Conversion of ground-based lidar for airborne applications. Proc. Soc. Photo. Instrum. Eng., 2833, 116-127. McCaul, E.W., Jr., H.B. Bluestein and R.J. Doviak, "Airborne Doppler lidar observations of convective phenomena in Oklahoma," J. Atmos. Oceanic Tech., 4, 479-497 (1987). Mohr, C.G. and L.J. Miller, 1983: CEDRIC - A software package for Cartesian space editing, synthesis, and display of radar fields under interactive control. Preprints, 21st Conf. Radar Meteor., Edmonton, Alta., Canada, Amer. Meteor. Soc., 569-574. Rothermel, J., C. Kessinger, and D.L. Davis, "Dual-Doppler lidar measurement of winds in the JAWS experiment," J. Atmos. Oceanic Tech., 2, 138-147 (1985). Rothermel, J., R.M. Hardesty, and R.T. Menzies, "Characterizing sub-grid scale processes and assessing satellite Doppler wind lidar with MACAWS," Preprints, Sixth Symp. Global Change Studies, Jan. 15-20, Dallas, Amer. Meteor. Soc., 118-121 (1995). Rothermel, J., D.R. Cutten, R.M. Hardesty, J.N. Howell, R.T. Menzies, D.M. Tratt, and S.C. Johnson, "Application of airborne Doppler laser radar to hurricane research," Preprints, 22nd Conf. Hurricanes and Tropical Meteorology, May 18-23, Ft. Collins, CO, Amer. Meteor. Soc., 57-58 (1997). Rothermel, J., D.R. Cutten, R.M. Hardesty, R.T. Menzies, J.N. Howell, S.C. Johnson, D.M. Tratt, L.D. Olivier and R.M. Banta, 1998: "The Multi-center Airborne Coherent Atmospheric Wind Sensor," Bull. Amer. Meteor. Soc., 79, 581-599. Rothermel, J., L.D. Olivier, R.M. Banta, R.M. Hardesty, J.N. Howell, D.R. Cutten, S.C. Johnson, R.T. Menzies, and D.M. Tratt, 1998: "Remote sensing of multi-level wind fields with high-energy airborne scanning coherent Doppler lidar." Optics Express, 2 40-50 (1998).[Accessible at uniform resource locator http://epubs.osa.org/opticsexpress/ To order these data or for further information, please contact:
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