An open framework for polarimetric radar measurement processing

The Department of Energy Atmospheric Radiation Measurement (ARM) program has a radar network which is widely geographically distributed and operates over a number of wavelengths. In order to maximize radar measurement utility, data collected at the radar must undergo routine quality control and calibration. This presentation outlines ARM's methodology for post-collection dual-polarization radar data correction, the Corrected Moments in Antenna Coordinates 2.0 (CMAC2.0). CMAC2.0, as implemented in the openly distributed Python ARM Radar Toolkit (Py-ART), performs an initial gate ID useful for applying subsequent corrections and microphysical retrieval algorithms in a consistent manner. Subsequent CAMC2.0 radar processing stages include mean Doppler velocity and dual-polarization differential phase unfolding, extraction of propagation phase using a linear programming method, corrections for attenuation in rain, and significant echo detection mask. CMAC2.0 is the first step in ARM's radar processing chain that includes Cartesian mapped moments, precipitation rate estimates and multi-Doppler wind field retrievals. In response to the wide range of formats that can be ingested using the larger Py-ART suite and the data model driven approach employed by CMAC2.0, this methodology can be easily applied to other radar network datastreams. Due to Py-ART's modular nature, more reliable or advanced techniques for the aforementioned algorithms can be substituted simply, implying a sustainable processing chain that can evolve with the changing science needs.