What is Pyrad?

Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is aimed at processing and visualizing data from weather radars both off-line and in real time. There is also limited support to visualize cloud radar, lidar and satellite data. It is written in the Python language. The framework is version controlled and automatic documentation is generated based on doc-strings. It is capable of ingesting data from all the weather radars in Switzerland, namely the operational MeteoSwiss C-band radar network, the MeteoSwiss X-band METEOR 50DX radar and the EPFL MXPol radar. It can also read ODIM complying files, CFRadial and NEXRAD.

The processing flow is controlled by 3 simple configuration files. Multiple levels of processing can be performed. At each level new datasets (e.g. attenuation corrected reflectivity) are created which can be stored in a file and/or used in the next processing level (e.g. creating a rainfall rate dataset from the corrected reflectivity). Multiple products can be generated from each dataset (i.e PPI, RHI images, histograms, etc.). In the off-line mode, data from multiple radars can be ingested in order to obtain products such as the inter-comparison of reflectivity values at co-located range gates. The framework is able to ingest polarimetric and Doppler radar moments as well as auxiliary data such as numerical weather prediction parameters (e.g. temperature, wind speed, etc.), DEM-based visibility and data used in the generation of the products such as rain gauge measurements, disdrometer measurements, solar flux, etc.

The signal processing and part of the data visualization is performed by a MeteoSwiss developed version of the Py-ART radar toolkit [1] which contains enhanced features. MeteoSwiss regularly contributes back to the main Py-ART branch once a new functionality has been thoroughly tested and it is considered of interest for the broad weather radar community.

The capabilities of the processing framework include various forms of echo classification and filtering, differential phase and specific differential phase estimation, attenuation correction, data quality monitoring, multiple rainfall rate algorithms, etc. In addition, time series of data in points, regions or trajectories of interest can be extracted and comparisons can be performed with other sensors. This is particularly useful when performing measurement campaigns where remote sensing retrievals are validated with in-situ airplane or ground-based measurements. The capabilities of the framework are expanded on an almost daily basis.

A certain degree of parallelization has been included. The user may choose to parallelize the generation of datasets of the same processing level, the generation of all the products of each dataset or both.

Radar volumetric data can be stored in C/F radial format or in ODIM format. Other data is typically stored as csv files. Plots can be output in any format accepted by Matplotlib. There exists several software packages that are Py-ART dependent. At the moment Pyrad wraps the software package PyTDA for turbulence computation