dl > Doppler LidarInstrument Type(s) > Baseline • Guest

The Doppler lidar (DL) is an active remote-sensing instrument that provides range- and time-resolved measurements of the line-of-sight component of air velocity (i.e., radial velocity) and attenuated aerosol backscatter. The DL operates in the near-infrared and is sensitive to backscatter from atmospheric aerosol, which are assumed to be ideal tracers of atmospheric wind fields.

The DL works by transmitting short pulses of infrared laser light into the atmosphere. Atmospheric aerosols scatter a small fraction of that light energy back to the transceiver, where it is collected and recorded as a time-resolved signal. From the delay between the outgoing pulse and the backscattered signal, the instrument infers the distance to the scattering volume.

Coherent detection is used to measure the Doppler frequency shift of the backscatter signal. This is accomplished by mixing the backscatter signal with a reference laser beam (i.e., local oscillator) of known frequency. The onboard signal processor then determines the Doppler frequency shift from the spectrum of the mixed signal. The Doppler frequency shift and thus the radial air velocity is determined from the peak of the Doppler spectrum. The attenuated backscatter is determined from the energy content of the Doppler spectra.

The DL provides accurate measurements of radial velocity in regions of the atmosphere where aerosol concentrations are high enough to ensure good signal-to-noise ratio. Thus, valid data are usually limited to the atmospheric boundary layer where aerosol is ubiquitous. Valid measurements can also be obtained in elevated aerosol layers or in optically thin clouds above the boundary layer. Most of the ARM DLs have full upper-hemispheric scanning capability, enabling 3D mapping of turbulent flows in the atmospheric boundary layer. With the scanner pointed vertically, the DL provides height- and time-resolved measurements of vertical velocity.

Measurements

Locations

  • Fixed
  • AMF1
  • AMF2
  • AMF3

2020

Ghate V, M Cadeddu, and R Wood. 2020. "Drizzle, Turbulence, and Density Currents Below Post Cold Frontal Open Cellular Marine Stratocumulus Clouds." Journal of Geophysical Research: Atmospheres, 125(19), 10.1029/2019JD031586.
Research Highlight

Griewank P, T Heus, N Lareau, and R Neggers. 2020. "Size dependence in chord characteristics from simulated and observed continental shallow cumulus." Atmospheric Chemistry and Physics, 20(17), 10.5194/acp-20-10211-2020.

McMichael L, F Yang, T Marke, U Löhnert, D Mechem, A Vogelmann, K Sanchez, M Tuononen, and J Schween. 2020. "Characterizing subsiding shells in shallow cumulus using Doppler lidar and large‐eddy simulation." Geophysical Research Letters, 47(18), e2020GL089699, 10.1029/2020GL089699.
Research Highlight

Tai S, J Fast, W Gustafson, D Chand, B Gaudet, Z Feng, and R Newsom. 2020. "Simulation of Continental Shallow Cumulus Populations using an Observation‐Constrained Cloud‐System Resolving Model." Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002091, 10.1029/2020MS002091.
Research Highlight

Newsom RK and R Krishnamurthy. 2020. Doppler Lidar (DL) Instrument Handbook. Ed. by Robert Stafford, U.S. Department of Energy. DOE/SC-ARM/TR-101.

kazemirad M and M Miller. 2020. "Summertime Post-Cold-Frontal Marine Stratocumulus Transition Processes over the Eastern North Atlantic." Journal of the Atmospheric Sciences, 77(6), 10.1175/JAS-D-19-0167.1.

Shapiro A, CK Potvin, JG Gebauer, AK Theisen, and NA Dahl. 2020. Improving Vertical Velocity Retrievals from Doppler Radar Observations of Convection Field Campaign Report. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-19-015.

Pentikäinen P, EJ O'Connor, A Manninen, and P Ortiz-Amezcua. 2020. "Methodology for deriving the telescope focus function and its uncertainty for a heterodyne pulsed Doppler lidar." Atmospheric Measurement Techniques, 13(5), 10.5194/amt-13-2849-2020.

Carneiro R and G Fisch. 2020. "Observational analysis of the daily cycle of the planetary boundary layer in the central Amazon during a non-El Nino year and El Nino year (GoAmazon project 2014/5)." Atmospheric Chemistry and Physics, 20(9), 10.5194/acp-20-5547-2020.

Bodini N and M Optis. 2020. "The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds." Wind Energy Science, 5(2), 10.5194/wes-5-489-2020.


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