rl: Raman Lidar

The Raman lidar (RL) is an active, ground-based, laser remote-sensing instrument that provides height- and time-resolved measurements of water-vapor mixing ratio, temperature, aerosol, and cloud optical properties. The RL operates in the ultraviolet (UV) and is sensitive to both molecular and aerosol backscatter.

The RL works by transmitting short pulses of UV laser light into the atmosphere. As the light propagates, a small fraction of the light energy is scattered back to the lidar 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.

As the name implies, the RL makes use of the Raman effect in which light is inelastically scattered by atmospheric N2, O2 and H2O molecules. The ARM RL uses a number of narrow-band detection channels specifically tuned to sense the Raman backscatter from these molecules. Several other detection channels are configured to measure elastically backscattered light from atmospheric aerosol. The raw signals from these detection channels are combined and processed to yield measurements of water vapor mixing ratio, temperature, aerosol backscatter coefficient, extinction, and depolarization ratio.



  • Fixed
  • AMF1
  • AMF2
  • AMF3

Related Publications


Klingebiel M, V Ghate, A Naumann, F Ditas, M Pöhlker, C Pöhlker, K Kandler, H Konow, and B Stevens. 2019. "Remote sensing of sea salt aerosol below trade wind clouds." Journal of the Atmospheric Sciences, , 10.1175/JAS-D-18-0139.1. ONLINE.

Balmes K, Q Fu, and T Thorsen. 2019. "Differences in Ice Cloud Optical Depth From CALIPSO and Ground-Based Raman Lidar at the ARM SGP and TWP Sites." Journal of Geophysical Research: Atmospheres, 124(3), 10.1029/2018JD028321.

Wagner T, P Klein, and D Turner. 2019. "A new generation of ground-based mobile platforms for active and passive profiling of the boundary layer." Bulletin of the American Meteorological Society, 100(1), 10.1175/BAMS-D-17-0165.1.

Han B, J Fan, A Varble, H Morrison, C Williams, B Chen, X Dong, S Giangrande, A Khain, E Mansell, J Milbrandt, J Shpund, and G Thompson. 2019. "Cloud-Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties." Journal of Geophysical Research: Atmospheres, 124(2), 10.1029/2018JD029596.


Newsom R and C Sivaraman. 2018. Raman Lidar Water Vapor Mixing Ratio and Temperature Value-Added Products. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-TR-218.

Balmes K and Q FU. 2018. "An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars." Atmosphere, 9(11), 10.3390/atmos9110445.

Stith J, D Baumgardner, J Haggerty, R Hardesty, W Lee, D Lenschow, P Pilewskie, P Smith, M Steiner, and H Vömel. 2018. "100 Years of Progress in Atmospheric Observing Systems." Meteorological Monographs, 59, 10.1175/AMSMONOGRAPHS-D-18-0006.1.

Johnson A, X Wang, K Haghi, and D Parsons. 2018. "Evaluation of Forecasts of a Convectively Generated Bore Using an Intensively Observed Case Study from PECAN." Monthly Weather Review, 146(9), 10.1175/MWR-D-18-0059.1.

Lv M, Z Wang, Z Li, T Luo, R Ferrare, D Liu, D Wu, J Mao, B Wan, F Zhang, and Y Wang. 2018. "Retrieval of Cloud Condensation Nuclei Number Concentration Profiles From Lidar Extinction and Backscatter Data." Journal of Geophysical Research: Atmospheres, 123(11), 10.1029/2017JD028102.

Osman M, D Turner, T Heus, and R Newsom. 2018. "Characteristics of Water Vapor Turbulence Profiles in Convective Boundary Layers During the Dry and Wet Seasons over Darwin." Journal of Geophysical Research: Atmospheres, 123(10), 10.1029/2017JD028060.
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