Xradar Basics

Overview

Within this notebook, we will highlight one of the newer packages in the Open Radar Science ecosystem, Xradar. Here is a brief overview of the motivation of this package:

At a developer meeting held in the course of the ERAD2022 conference in Locarno, Switzerland, future plans and cross-package collaboration of the Open Radar Science community were intensively discussed.

The consensus was that a close collaboration that benefits the entire community can only be maximized through joint projects. So the idea of a common software project whose only task is to read and write radar data was born. The data import should include as many available data formats as possible, but the data export should be limited to the recognized standards, such as ODIM_H5 and CfRadial.

As memory representation an xarray based data model was chosen, which is internally adapted to the forthcoming standard CfRadial2.1/FM301. FM301 is enforced by the Joint Expert Team on Operational Weather Radar (JET-OWR). Information on FM301 is available at WMO as WMO CF Extensions.

Any software package that uses xarray in any way will then be able to directly use the described data model and thus quickly and easily import and export radar data. Another advantage is the easy connection to already existing open source radar processing software.

We will highlight how to get started with xradar, including how to read in data, apply georeferencing, and visualize the datasets.

How to Read in Datasets + Basic Structure
Creating Plan Position Indicator (PPI) Plots Using Georeferencing
Slicing/Dicing Radar Datasets

Prerequisites

Label the importance of each concept explicitly as helpful/necessary.

Concepts	Importance	Notes
Introduction to Xarray	Required	Familiarity with Xarray Data Structure
Intro to Cartopy	Helpful	Basic Use of Cartopy Projections
Understanding of NetCDF	Helpful	Familiarity with metadata structure

Time to learn: 15 minutes

Imports

import cmweather
import numpy as np
import xarray as xr
import xradar as xd
import matplotlib.pyplot as plt
import cartopy

Summary

Within this notebook, we took a look at the basics of the xradar package, and how it enables us to use the xarray ecosystem. The process from reading the data, applying filtering, subsetting, and visualizing the data was covered. We encourage users to explore the rest of the xradar user guide to continue learning how this tool is useful for their workflows.