rl > Raman LidarVAP Type(s) > Baseline • Guest

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

2020

Balmes K and Q FU. 2020. "The diurnally-averaged aerosol direct radiative effect and the use of the daytime-mean and insolation-weighted-mean solar zenith angles." Journal of Quantitative Spectroscopy and Radiative Transfer, 257, 107363, 10.1016/j.jqsrt.2020.107363.

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.

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.

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.

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.

Dawson K, R Ferrare, R Moore, M Clayton, T Thorsen, and E Eloranta. 2020. "Ambient Aerosol Hygroscopic Growth From Combined Raman Lidar and HSRL." Journal of Geophysical Research: Atmospheres, 125(7), e2019JD031708, 10.1029/2019JD031708.

Roy R, M Lebsock, L Millán, and K Cooper. 2020. "Validation of a G-band differential absorption cloud radar for humidity remote sensing." Journal of Atmospheric and Oceanic Technology, 37(6), 10.1175/JTECH-D-19-0122.1.

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, 77(3), 10.1175/JAS-D-19-0122.1.
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