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.