Radproc - A GIS-compatible Python Package for automated RADOLAN Composite Processing and Analysis

Date:February 19, 2020

Radproc is an open source Python library intended to facilitate precipitation data processing and analysis for GIS-users. It provides functions for processing, analysis and export of RADOLAN (Radar Online Adjustment) and RADKLIM (Radar Climatology) composites and rain gauge data in MR90 format. The German Weather Service (DWD) provides many of its RADOLAN and RADKLIM data free of charge on their Open Data Portal (see Getting data) but the data processing represents a big challenge for many potential users. Radproc’s goal is to lower the barrier for using these data, especially in conjunction with ArcGIS. Therefore, radproc provides an automated ArcGIS-compatible data processing workflow based on pandas DataFrames and HDF5. Moreover, radproc’s arcgis module includes a collection of functions for data exchange between pandas and ArcGIS.


Please cite radproc as Kreklow, J. (2019): Facilitating radar precipitation data processing, assessment and analysis: A GIS-compatible python approach. Journal of Hydroinformatics, 21(4), 652–670. doi: https://doi.org/10.2166/hydro.2019.048

Radproc’s Main Features

Raw Data processing

  • Support for the RADOLAN and RADKLIM composite products RW (60 min), YW, RY and RZ (all 5 min. resolution)
  • Automatically reading in all binary RADOLAN/RADKLIM composites from a predefined directory structure
  • Optionally clipping the composites to a study area in order to reduce data size
  • Default data structure: Monthly pandas DataFrames with full support for time series analysis and spatial location of each pixel
  • Efficient data storage in HDF5 format with fast data access and optional data compression
  • Easy downsampling of time series
  • Reading in DWD rain gauge data in MR90 format into the same data structure as RADOLAN/RADKLIM.

Data Exchange with ArcGIS

  • Export of single RADOLAN/RADKLIM composites or analysis results into projected raster datasets or ESRI grids for your study area
  • Export of all DataFrame rows into raster datasets in a new file geodatabase, optionally including several statistics rasters
  • Import of dbf tables (stand-alone or attribute tables of feature classes) into pandas DataFrames
  • Joining DataFrame columns to attribute tables
  • Extended value extraction from rasters to points (optionally including the eight surrounding cells)
  • Extended zonal statistics


  • Calculation of precipitation sums for arbitrary periods of time
  • Heavy rainfall analysis, e.g. identification, counting and export of rainfall intervals exceeding defined thresholds
  • Calculation of duration sums
  • Data quality assessment
  • Comparison of RADOLAN/RADKLIM and rain gauge data
  • In preparation: Erosivity analysis, e.g. calculation of monthly and annual R-factors

Indices and tables