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https://github.com/meteoswiss-mdr/pyrad
Python Radar Data Processing
https://github.com/meteoswiss-mdr/pyrad
data-quality-monitoring data-visualization meteoswiss pyrad python-radar-processing weather-radars
Last synced: 3 months ago
JSON representation
Python Radar Data Processing
- Host: GitHub
- URL: https://github.com/meteoswiss-mdr/pyrad
- Owner: meteoswiss-mdr
- License: other
- Created: 2016-08-25T14:18:02.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2021-02-03T10:38:30.000Z (almost 4 years ago)
- Last Synced: 2024-06-21T18:10:20.844Z (5 months ago)
- Topics: data-quality-monitoring, data-visualization, meteoswiss, pyrad, python-radar-processing, weather-radars
- Language: Python
- Homepage: https://github.com/MeteoSwiss/pyrad
- Size: 89.9 MB
- Stars: 54
- Watchers: 6
- Forks: 20
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-atmos - pyrad - time data processing framework developed by MeteoSwiss. (Radar)
README
[](https://travis-ci.org/meteoswiss-mdr/pyrad)
[](https://meteoswiss-mdr.github.io/pyrad/)
[](https://anaconda.org/conda-forge/pyrad_mch)
[](https://anaconda.org/conda-forge/pyrad_mch)
[](https://anaconda.org/conda-forge/pyrad_mch)
[](https://anaconda.org/conda-forge/pyrad_mch)
[](https://anaconda.org/conda-forge/pyrad_mch)
:warning: **Please use the [new repository](https://github.com/MeteoSwiss/pyrad). This repository is not maintained anymore!**
# pyrad
Python Radar Data Processing
# What is Pyrad?
Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is
aimed at processing and visualizing polar data from individual weather radars as well as composite
Cartesian products both off-line and in real time. 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 rad4alp radar network, the MeteoSwiss X-band DX50 radar and the EPFL MXPol radar
and radar data in the OPERA file format. Additionally, it can ingest C/FRadial and NEXRAD level 2 files.
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 (for example, creating a rainfall rate
dataset from the corrected reflectivity). Multiple products can be generated from each dataset (e.g.
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. It can as well work with I/Q data, spectral data and Cartesian
data.
The signal processing and part of the data visualization is performed by a [MeteoSwiss developed version of the Py-ART radar toolkit](https://github.com/meteoswiss-mdr/pyart) which contains enhanced features. MeteoSwiss regularly contributes back to the [main Py-ART branch](https://github.com/ARM-DOE/pyart) once a new functionality has been thoroughly tested and it is considered of interest for the broad weather radar community.
The processing framework has multiple and expanding capabilities, 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.
# Installation
To install Pyrad and its submodules please have a look at the [Pyrad user manual (pdf)](./doc/pyrad_user_manual.pdf).
# Use
Before using it have a look at the [cookbook (pdf)](./doc/pyrad-framework-cookbook/DataProcessing.pdf).
For details on the implemented functions check the [pyrad library reference for users](https://pyrad-mch.readthedocs.io/en/latest/). Downloadable copies can be found in the repository:
[For users (pdf)](https://media.readthedocs.org/pdf/pyrad-mch/latest/pyrad-mch.pdf)
Example configuration files can be found in the repository directory [pyrad/config/processing/](./config/processing).
To use Pyrad for data quality monitoring check the report [pyrad_monitoring_fvj.pdf](./doc/pyrad_monitoring_fvj.pdf).
# Development
We welcome contributions, suggestions of developments and bug reports.
Suggestions of developments and bug reports should use the [Issues page of the github repository](https://github.com/meteoswiss-mdr/pyrad/issues).
The process to contribute by partners external to MeteoSwiss is described in the [user manual](./doc/pyrad_user_manual.pdf).
# Citation
The core of Pyrad is based on Py-ART. Py-ART was originally developed in the context of the [ARM Research Facility](https://www.arm.gov/). If you use Pyrad for your work, please cite BOTH Py-ART and Pyrad papers in your paper:
J.J. Helmus, S.M. Collis, (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software. 4(1), p.e25. DOI: http://doi.org/10.5334/jors.119
J. Figueras i Ventura, M. Lainer, Z. Schauwecker, J. Grazioli, U. Germann, (2020). Pyrad: A Real-Time Weather Radar Data Processing Framework Based on Py-ART. Journal of Open Research Software, 8(1), p.28. DOI: http://doi.org/10.5334/jors.330
# Disclaimer
The software is still in a development stage. Please let us know if you would like to test it.
MeteoSwiss cannot be held responsible for errors in the code or problems that could arise from its use.