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CMAC2

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cmac2 > Corrected Moments in Antenna Coordinates, Version 2VAP Type(s) > Baseline • Evaluation

Raw data from ARM precipitation radars must be corrected for atmospheric phenomena and instrument characteristics (e.g., attenuation, clutter) to retrieve precipitation properties. The Corrected Moments in Antenna Coordinates Version 2 (CMAC2) value-added product (VAP) is a set of algorithms and code that makes such corrections, and it also retrieves precipitation quantities from the radar measurements.

Starting with the X-Band Scanning ARM Precipitation Radar (XSAPR) network at ARM’s Southern Great Plains atmospheric observatory, CMAC2 provides higher-quality precipitation radar data and retrieved quantities for researchers. This VAP does the following:

  • adds new fields, in the radar’s natural coordinates of radius, azimuth, and elevation that correct for artifacts
  • provides information on how some fields are affected by attenuation—decreased radar signal strength—due to absorption of the microwave radiation by water molecules
  • provides an estimate of the rainfall rate.

CMAC2 introduces the concept of a gate identification (or ID), a pre-retrieval, pre-correction classification of which type of particle (e.g., raindrop, snowflake) is dominant in scattering power back to the radar receiver. This classification technique is based on work by Jonathan Gourley and Brenda Dolan. The gate ID is then used to determine the appropriate corrections to apply to each measurement (e.g., attenuation correction in rain, Doppler velocity dealiasing in passive tracers).

CMAC2 provides data in a community-standard-format netCDF file using CF-Radial conventions. The data are therefore compatible with new and existing National Center for Atmospheric Research (NCAR) tools, such as RadXConvert, for converting to a variety of popular file formats.

Data from CMAC2 can be analyzed and built on with the Python ARM Radar Toolkit (Py-ART), an open-source architecture for interacting with radar data in the Python programming language, and other community code. CMAC2 is in a modular format, which means that modules can be removed, changed, or added. The VAP team is always interested in improving processing and contributions to Py-ART to directly feed CMAC2.

Purpose

The purpose of this data set is to provide higher-quality precipitation radar data for researchers. The data were collected in order to provide original X-SAPR data with added fields based on the original data.

Locations

  • Fixed
  • AMF1
  • AMF2
  • AMF3

Data Details

Developed By Scott Collis | Zachary Sherman | Robert Jackson
Contact Zachary Sherman
Resource(s) Data Directory
ReadMe
Data format netcdf
Site SGP
Content time range 31 July 2017 - 5 April 2019
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 SITERADARMODEcmacFACILITY.facility.yyyymmdd.hhmmss.nc eg sgpxsaprsurcmacI5.c1.20170810.121000.nc
Directory Organization Data will be staged on Stratus. The radar data will be in /lustre/or-hydra/cades-arm/proj-shared/sgpxsaprcmacsurI5.c1/YYYYMMDD/ and figure data in /lustre/or-hydra/cades-arm/proj-shared/sgpxsaprcmacsurI5.c1.png/YYYYMMDD.hhmmss/
Citations https://github.com/ARM-DOE/openvap/blob/master/development/cmac2/reports/progress_201603/cmac2p0_progress.pdf

2021

Dorsey KS, R Jundt, CB Ireland, MR Wasem, RA Stafford, and A Hunzinger. 2021. 2020 Atmospheric Radiation Measurement (ARM) Annual Report. Ed. by Kathryn Dorsey, U.S. Department of Energy. DOE/SC-ARM-20-020.

2019

Varble A, S Nesbitt, P Salio, E Avila, P Borque, P DeMott, G McFarquhar, S van den Heever, E Zipser, D Gochis, R Houze, M Jensen, P Kollias, S Kreidenweis, R Leung, K Rasmussen, D Romps, and C Williams. 2019. Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Field Campaign Report. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-19-028.

2016

Helmus JJ and SM Collis. 2016. "The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language." Journal of Open Research Software, 4(1), 10.5334/jors.119.
Research Highlight


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Contact

Scott Collis
Translator
Argonne National Laboratory

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