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.

Measurements

Locations

  • Fixed
  • AMF1
  • AMF2
  • AMF3

Active Instrument Locations

Facility Name Instrument Start Date
Central Facility, Lamont, OK 1996-06-03

2020

Weckworth TM, S Spuler, and DD Turner. 2020. Micropulse Differential Absorption Lidar (MPD) Network Demonstration Field Campaign Report. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-20-002.

2019

Stillwell R, S Spuler, M Hayman, K Repasky, and C Bunn. 2019. "Demonstration of a combined differential absorption and high spectral resolution lidar for profiling atmospheric temperature." Optics Express, 28(1), 10.1364/OE.379804.

Lareau N. 2019. "Subcloud and Cloud-Base Latent Heat Fluxes during Shallow Cumulus Convection." Journal of the Atmospheric Sciences, , 10.1175/JAS-D-19-0122.1. ONLINE.

Osman M, D Turner, T Heus, and V Wulfmeyer. 2019. "Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large‐Eddy Simulations." Journal of Geophysical Research: Atmospheres, 124(20), 10.1029/2019JD030653.

Newsom R, D Turner, R Lehtinen, C Münkel, J Kallio, and R Roininen. 2019. "Evaluation of a Compact Broadband Differential Absorption Lidar for Routine Water Vapor Profiling in the Atmospheric Boundary layer." Journal of Atmospheric and Oceanic Technology, 37(1), 10.1175/JTECH-D-18-0102.1.

Chand D, R Newsom, T Thorsen, E Cromwell, C Sivaraman, C Flynn, J Shilling, and J Comstock. 2019. Aerosol and Cloud Optical Properties from the ARM Raman Lidars: The Feature Detection and Extinction (FEX) Value-Added Product. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-TR-224.

Liu Y. 2019. "Introduction to the Special Section on Fast Physics in Climate Models: Parameterization, Evaluation, and Observation." Journal of Geophysical Research: Atmospheres, 124(15), 10.1029/2019JD030422.

Fast J, L Berg, Z Feng, F Mei, R Newsom, K Sakaguchi, and H Xiao. 2019. "The Impact of Variable Land‐Atmosphere Coupling on Convective Cloud Populations Observed During the 2016 HI‐SCALE Field Campaign." Journal of Advances in Modeling Earth Systems, 11(8), 10.1029/2019MS001727.

Lin G, B Geerts, Z Wang, C Grasmick, X Jing, and J Yang. 2019. "Interactions Between a Nocturnal MCS and the Stable Boundary Layer, as Observed by an Airborne Compact Raman Lidar During PECAN." Monthly Weather Review, 147(7), 10.1175/MWR-D-18-0388.1.

Tridon F, C Planche, K MROZ, S Banson, A Battaglia, J Van Baelen, and W Wobrock. 2019. "On the realism of the rain microphysics representation of a squall line in the WRF model. Part I: Evaluation with multi-frequency cloud radar Doppler spectra observations." Monthly Weather Review, 147(8), 10.1175/MWR-D-18-0018.1.


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