mwr > Microwave RadiometerInstrument Type(s) > Baseline • External • Guest

The microwave radiometer (MWR) provides time-series measurements of column-integrated amounts of water vapor and liquid water. The instrument itself is a sensitive microwave receiver that detects the microwave emissions of the vapor and liquid water molecules in the atmosphere at two frequencies: 23.8 and 31.4 GHz.

Integrated water vapor and liquid water path are derived from radiance measurements with a statistical retrieval algorithm that uses monthly derived and location-dependent linear regression coefficients.



  • Fixed
  • AMF1
  • AMF2
  • AMF3

Active Instrument Locations

Facility Name Instrument Start Date
Morrison, OK (Extended) 2019-05-23
Peckham, OK (Extended) 2019-09-18
Central Facility, Lamont, OK 1993-07-21
Central Facility, Barrow AK 1998-01-02


Khanal S, Z Wang, and J French. 2020. "Improving middle and high latitude cloud liquid water path measurements from MODIS." Atmospheric Research, 243, 10.1016/j.atmosres.2020.105033.

Dorsey K and D Feldman. 2020. Surface Atmosphere Integrated Field Laboratory. Ed. by Rolanda Jundt, ARM user facility. DOE/SC-ARM-20-016.

Serra Y, A Rowe, D Adams, and G Kiladis. 2020. "Kelvin Waves during GOAmazon and Their Relationship to Deep Convection." Journal of the Atmospheric Sciences, 77(10), 10.1175/JAS-D-20-0008.1.
Research Highlight

Tridon F, A Battaglia, and S Kneifel. 2020. "Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars." Atmospheric Measurement Techniques, 13(9), 10.5194/amt-13-5065-2020.

Marquardt Collow A, M Miller, L Trabachino, M Jensen, and M Wang. 2020. "Radiative heating rate profiles over the southeast Atlantic Ocean during the 2016 and 2017 biomass burning seasons." Atmospheric Chemistry and Physics, 20(16), 10.5194/acp-20-10073-2020.
Research Highlight

Tang S, S Xie, and M Zhang. 2020. Description of the Three-Dimensional Large-Scale Forcing Data from the 3D Constrained Variational Analysis (VARANAL3D). Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-TR-253.

Vilà‐Guerau de Arellano J, X Wang, X Pedruzo‐Bagazgoitia, M Sikma, A Agustí‐Panareda, S Boussetta, G Balsamo, L Machado, T Biscaro, P Gentine, S Martin, J Fuentes, and T Gerken. 2020. "Interactions Between the Amazonian Rainforest and Cumuli Clouds: A Large‐Eddy Simulation, High‐Resolution ECMWF, and Observational Intercomparison Study." Journal of Advances in Modeling Earth Systems, 12(7), e2019MS001828, 10.1029/2019MS001828.

Joshil S, V Chandrasekar, J Chiu, and Y Blanchard. 2020. "Separating cloud and drizzle signals in radar Doppler spectra using a parametric time domain method." Journal of Atmospheric and Oceanic Technology, 37(9), 10.1175/JTECH-D-20-0061.1.

Chase R, S Nesbitt, and G McFarquhar. 2020. "Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow." Atmosphere, 11(6), 10.3390/atmos11060619.

Maahn M, D Turner, U Löhnert, D Posselt, K Ebell, G Mace, and J Comstock. 2020. "Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know." Bulletin of the American Meteorological Society, 101(9), 10.1175/BAMS-D-19-0027.1.
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