scholarly journals Pyrad: A Real-Time Weather Radar Data Processing Framework Based on Py-ART

2020 ◽  
Vol 8 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Martin Lainer ◽  
Zaira Schauwecker ◽  
Jacopo Grazioli ◽  
Urs Germann
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Data Processing ◽  
Processing Framework

scholarly journals Weather radar data processing on graphic cards

2017 ◽  
Vol 74 (2) ◽  
pp. 868-885 ◽  
Author(s):  
Mónica Denham ◽  
Enrico Lamperti ◽  
Javier Areta
Keyword(s):  
Data Processing ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Data Processing

scholarly journals Facilitating radar precipitation data processing, assessment and analysis: a GIS-compatible python approach

2019 ◽  
Vol 21 (4) ◽  
pp. 652-670 ◽  
Author(s):  
Jennifer Kreklow
Keyword(s):  
Data Processing ◽  
Data Exchange ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Raw Data ◽  
Automated Processing ◽  
Point Data ◽  
Radar Data Processing

Abstract A review of existing tools for radar data processing revealed a lack of open source software for automated processing, assessment and analysis of weather radar composites. The ArcGIS-compatible Python package radproc attempts to reduce this gap. Radproc provides an automated raw data processing workflow for nationwide, freely available German weather radar climatology (RADKLIM) and operational (RADOLAN) composite products. Raw data are converted into a uniform HDF5 file structure used by radproc's analysis and data quality assessment functions. This enables transferability of the developed analysis and export functionality to other gridded or point-scale precipitation data. Thus, radproc can be extended by additional import routines to support any other German or non-German precipitation dataset. Analysis methods include temporal aggregations, detection of heavy rainfall and an automated processing of rain gauge point data into the same HDF5 format for comparison to gridded radar data. A set of functions for data exchange with ArcGIS allows for visualisation and further geospatial analysis. The application on a 17-year time series of hourly RADKLIM data showed that radproc greatly facilitates radar data processing and analysis by avoiding manual programming work and helps to lower the barrier for non-specialists to work with these novel radar climatology datasets.

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