radproc: An ArcGIS-compatible Library for RADOLAN Composite Processing and Analysis

Date:July 19, 2018

Radproc is an open source Python library intended to faciliate precipitation data processing and analysis for ArcGIS-users. It provides functions for processing, analysis and export of RADOLAN (Radar Online Adjustment) composites and rain gauge data in MR90 format. The German Weather Service (DWD) provides the RADOLAN-Online RW composites for free in the Climate Data Center (ftp://ftp-cdc.dwd.de/pub/CDC/grids_germany/hourly/radolan/) 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.

Radproc’s Main Features

Raw Data processing

  • Support for the reanalyzed RADOLAN products RW (60 min), YW and RY (both 5 min. resolution)
  • Automatically reading in all binary RADOLAN 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.

Data Exchange with ArcGIS

  • Export of single RADOLAN 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
  • Data quality assessment
  • Comparison of RADOLAN and rain gauge data
  • In preparation: Erosivity analysis, e.g. calculation of monthly, seasonal or annual R-factors

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