Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

kazr > Ka ARM Zenith RadarInstrument Type(s) > Baseline • Guest

The Ka-band ARM Zenith Radar (KAZR) remotely probes the extent and composition of clouds at millimeter wavelengths. The KAZR is a zenith-pointing Doppler radar that operates at a frequency of approximately 35 GHz. The main purpose of this radar is to determine the first three Doppler moments (reflectivity, vertical velocity, and spectral width) at a range resolution of approximately 30 meters from near-ground to nearly 20 kilometers in altitude.

The KAZR replaces the millimeter-wavelength cloud radar (MMCR) and uses a new digital receiver that provides higher spatial and temporal resolution than the MMCR. In addition, spectral artifacts in the data are significantly reduced in the KAZR, allowing researchers to study cloud dynamics much more closely than with the MMCR.

KAZR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.


The main purpose for generating this data set is that ground clutter was contaminating the KAZR spectra preventing the standard ARM processing from being applied to the raw spectra. The ground clutter needed to be removed before moments could be estimated. Since the ARSCL and micro-ARSCL teams were busy on other projects, the Oliktok Point Team (from the University of Colorado) was tasked to clean up the KAZR spectra and estimate high-order moments.


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Data Details

Developed By Christopher Williams
Contact Christopher Williams
Resource(s) Data Directory
Data format Data files are in netCDF format. Images are in TIF format.
Site OLI
Content time range 6 January 2011 - 9 August 2022
Attribute accuracy No formal attribute accuracy tests were conducted.
Positional accuracy No formal positional accuracy tests were conducted.
Data Consistency and Completeness Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.
File naming convention (this can be broken down as: oli = oliktok point, kazr = KAZR, ge = general mode, 15sec = 15 second averaged spectra, shift = spectra are shifted before averaging, mom = moments, cwilliams = creator's name, yyyy = year, mm = month, dd = day, v15 = version number
Directory Organization No directory structure
Citations This is the data set described in the article: Christopher Williams, Maximilian Maahn, Joseph Hardin and Gijs de Boer, 2018: Clutter Mitigation, Multiple Peaks, and High-Order Spectral Moments in 35-GHz Vertically Pointing Radar Velocity Spectra, J. Atmos. Meas. Tech., 11, 4963-4980, doi: 10.5194/amt-11-4963-2018.


Zheng Q and M Miller. 2022. "Summertime Marine Boundary Layer Cloud, Thermodynamic, and Drizzle Morphology over the Eastern North Atlantic: A Four-Year Study." Journal of Climate, 35(14), 10.1175/JCLI-D-21-0568.1.

Sterzinger L, J Sedlar, H Guy, R Neely III, and A Igel. 2022. "Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations." Atmospheric Chemistry and Physics, 22(13), 10.5194/acp-22-8973-2022.

Daub B and N Lareau. 2022. "Observed Covariations in Boundary Layer and Cumulus Cloud Layer Processes." Journal of Applied Meteorology and Climatology, , 10.1175/JAMC-D-21-0213.1. ACCEPTED.

ZHU Z, P Kollias, E Luke, and F Yang. 2022. "New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra." Atmospheric Chemistry and Physics, 22(11), 10.5194/acp-22-7405-2022.

Wagner D, M Shupe, C Cox, O Persson, T Uttal, M Frey, A Kirchgaessner, M Schneebeli, M Jaggi, A Macfarlane, P Itkin, S Arndt, S Hendricks, D Krampe, M Nicolaus, R Ricker, J Regnery, N Kolabutin, E Shimanshuck, M Oggier, I Raphael, J Stroeve, and M Lehning. 2022. "Snowfall and snow accumulation during the MOSAiC winter and spring seasons." The Cryosphere, 16(6), 10.5194/tc-16-2373-2022.

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