Abstract
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.
Highlights
Rainfall and especially heavy and extreme rainfall events are a major trigger for floods and flash floods (Gaume et al ; Bouilloud et al ; Alfieri et al ; Wright et al ), soil erosion (Wischmeier & Smith ; Panagos et al, ; Steinhoff-Knopp & Burkhard ), mud flows (Hänsel et al ) and landslides (Guzzetti et al ; Segoni et al ) causing costly damage or even casualties
These projects, such as the radar climatology RADKLIM (Winterrath et al a, b) provided by the German Weather Service (Deutscher Wetterdienst, (DWD)) will open up new climatological application fields for radar data (Keupp et al ; Winterrath et al ) which include the characterisation of the spatial variability of long-term rainfall patterns, seasonal variations in rainfall, durations of dry periods and the study of rainfall extremes and their impacts (Overeem et al ; Smith et al ; Wright et al )
This paper presents the open source library radproc (Kreklow ) written in Python as a possible solution to the bottlenecks in current weather radar data processing and assessment in Germany and beyond
Summary
Rainfall and especially heavy and extreme rainfall events are a major trigger for floods and flash floods (Gaume et al ; Bouilloud et al ; Alfieri et al ; Wright et al ), soil erosion (Wischmeier & Smith ; Panagos et al , ; Steinhoff-Knopp & Burkhard ), mud flows (Hänsel et al ) and landslides (Guzzetti et al ; Segoni et al ) causing costly damage or even casualties. Beyond the outlined uncertainties regarding data quality, several technical barriers exist that can prevent potential users from working with radar data These include different file formats for exchange and storage, provision in proprietary binary file formats, a scarcity of easy-to-use and free-of-charge processing software, spatial visualisation and clipping tools, missing compatibility or interfaces to Geographic Information Systems (GIS) and the vast amount of data (Heistermann et al , ; Fischer et al ). Due to a law change in July 2017 (Deutscher Bundestag ), radar data are subject to an open access policy, which is why both RADKLIM products are provided free of charge in the DWD Climate Data Centre (CDC) This makes it a very interesting and promising dataset for various applications, for instance in hydrology, meteorology and geography. The following review is structured according to the software distribution model since the availability, costs and customisability of a tool are factors strongly influencing a user’s choice of software
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