Objective Analysis of Scanning Radar Observations in Python

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid. The Python-ARM Radar Toolkit (Py-ART), is a data model driven interactive architecture for working with weather radar data. Py-ART has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential) and also has the ability to filter the data during the interpolation. Py-ART also allows arbitrary formulations for the radius of influence , which are matched to particular radar scanning strategies.. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. In this presentation, we will explore the idea of testing different parameters in a Python based objective framework using a mix of real data and synthetic scan data generated from model data using Cloud Resolving Model Radar Simulator (CR-SIM). The structure of resultant gridded data is compared against model data input to CR-SIM. This presentation outlines the Python packages used to analyze the gridded storm structures. Since the problem of searching across OA parameter space is pleasantly parallel, the presentation will also discuss the HPC tools used to map the problem to clusters.