A Continuously Operating Python Framework for Country-Wide Near-Real-Time Weather Radar Adjustment in Germany Using Rain Gauges and Commercial Microwave Links

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially continuous rainfall information but are affected by systematic and random uncertainties, e.g. calibration errors, beam blockage, vertical profile effects, and range-dependent biases. A well-established approach to mitigate these limitations is the adjustment of radar-based precipitation using ground-based reference observations. While rain gauges remain the most common reference, commercial microwave links (CMLs) from cellular communication networks offer a promising complementary source of near-surface rainfall information with high spatial coverage and temporal resolution.

Here, we present the development of the flexible Python-based framework pyRADMAN designed to support operational and research-oriented radar adjustment using multiple types of ground sensors at the Deutscher Wetterdienst. The framework enables preprocessing, configurable selection, and combination of different observations, including both rain gauges and CML attenuation-derived rainfall estimates, with radar data. The system ingests radar data from 17 radar sites, approximately 1500 rain gauges available at DWD, and attenuation data from about 4500 CMLs. A continuous CML data transfer from Ericsson to DWD has been established with a latency of less than 2 minutes, enabling the generation and assessment of near-real-time CML-adjusted radar products. pyRADMAN can be operated in routine mode to provide adjusted QPE products, or in recalculation mode for systematic evaluation and method development.

The applied adjustment approach follows the established principles of the operational RADOLAN adjustment scheme. Additional experiments with radar preprocessing, CML processing strategies and adjustment methods were conducted. We demonstrate the feasibility and performance of radar adjustment that goes beyond the recent operational system by using different sensor configurations including CMLs and a fine temporal resolution. Results are presented for an evaluation period covering July 2023 to December 2024, highlighting the potential benefits and challenges of incorporating CML data for near-real-time radar QPE adjustment in an quasi-operational environment.