A Python-Based Plotter for Model-Derived Polarimetric Radar Variables

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Monday, 5 January 2015
Timothy A. Supinie, CAPS/Univ. of Oklahoma, Norman, OK; and D. T. Dawson II and Y. Jung

With the advent of complex microphysics schemes, an increasingly viable verification technique for numerical models is comparison to polarimetric radar data. To that end, we have created a Python-based plotter for model-derived polarimetric radar variables. In addition to the traditional horizontal reflectivity (Zh) and radial velocity (vr), the plotter can simulate polarimetric quantities including differential reflectivity (ZDR), specific differential phase (KDP), and correlation coefficient (ρhv) derived from a numerical simulation. Additional microphysical diagnostics can also be plotted, such as hydrometeor density in variable-density schemes and mean hydrometeor diameters.

To facilitate comparison to observed quantities, the plotter can interpolate the model output to a conical radar sweep surface, in addition to planes along the three spatial axes, and plot observed radar data in CFRadial format.

The plotting program can ingest data from several common numerical models: the Weather Research and Forecasting (WRF) model, the Advanced Regional Prediction System (ARPS), Cloud Model 1 (CM1), and Collaborative Model for Multiscale Atmospheric Simulation (COMMAS). Additionally, the plotter has support for several different microphysics schemes, including the Milbrandt and Yau (MY), Ziegler Variable Density (ZVD), WRF Dual-Moment 6-Class (WDM6), Thompson, and Morrison schemes. The calculations of polarimetric variables make use of pre-existing FORTRAN code from the ARPS model, compiled using f2py. Other required external libraries include the PyNIO and NetCDF4py libraries for data ingest, NumPy and SciPy for calculations, and Matplotlib and Basemap for plotting.