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This project is a QGIS plugin that brings precipitation data from German Weather Service (DWD) - radar data in RADOLAN/RADKLIM binary format as well as REGNIE - on a map

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sydroconsult/radolan2map

 
 

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radolan2map

QGIS plugin for DWD rasterized precipitation datasets like RADOLAN/RADKLIM and REGNIE
This project is a QGIS plugin that brings precipitation data from German Meteorological Service (DWD, https://www.dwd.de) - radar data in RADOLAN/RADKLIM binary format in any grid dimension as well as REGNIE - on a map. You also can add up RADOLAN data!
Applications: hydrometeorological and agricultural analyses.

Features

load the following data types:

  • load/display any binary file in RADOLAN / RADKLIM format (incl. gz-compression)
  • radolan summation: you can add up RADOLAN data!
  • daily, monthly and multiannual REGNIE files (incl. gz-compression)

additional functions for RADOLAN:

  • bring it to standard projection ETRS89 / LAEA Europe
  • optional cut to german border (or any other shape file)
  • status of DWD Radar Network included (see Wiki)

Installation, Usage

=> Wiki

Data info

RADOLAN

  • Overview: https://dwd.de/RADOLAN. There are hourly (RH, RW) or daily (SF) datasets.
    A RADOLAN-RW-testfile (gzipped binary) is included in directory example/sample_file/.
  • Data download: RADOLAN (recent) and RADKLIM radar climatology dataset: DWD open climate data

REGNIE

Platform info

Plugin was successfully tested with QGIS

  • 3.16 Hannover on Linux openSUSE 15.3 and Windows 10
  • 3.10 A Coruña on Linux openSUSE 15.1 and Windows 10

If you experience any problems when starting the plugin, please make sure that
you have a current version of QGIS and Python installed on your system.

QGIS

Feel invited...

to give me feedback about usability and
help improving this plugin to promote open source (GIS)software and data!

About

This project is a QGIS plugin that brings precipitation data from German Weather Service (DWD) - radar data in RADOLAN/RADKLIM binary format as well as REGNIE - on a map

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  • Python 81.3%
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