Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML-based rainfall maps despite the great extension of networks across the world. We present CML based rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. For homogenization we apply three increasingly effective algorithms of filtering and time series interpolation. We show that straightforward algorithms used for homogenisation strongly reduce anomalies in the results. Furthermore, we find that after the homogenization and joint processing, CML-based rainfall maps can be generated which seamlessly visualize a rainfall event traversing the German-Czech border. This displays the potential for large scale (e.g. continental) CML-based rainfall estimation.

Climate models face limitations in their ability to accurately represent highly variable atmospheric phenomena. To resolve fine-scale physical processes, allowing for local impact assessments, downscaling techniques are essential. We propose spateGAN, a novel approach for spatio-temporal downscaling of precipitation data using conditional generative adversarial networks. Our method is based on a video super-resolution approach and trained on ten years of country wide radar observations for Germany. It simultaneously increases the spatial and temporal resolution of coarsened precipitation observations from 32 km to 2 km and from 1 hour to 10 minutes. Our experiments indicate that the ensembles of generated temporally consistent rainfall fields are in high agreement with the observational data. Spatial structures with plausible advection were accurately generated. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution-dependent extreme value distribution with high skill. It showed a high Fractions Skill Score of 0.73 for rainfall intensities over 15mmh-1 and a low BIAS of 3.55%. A power spectrum analysis confirmed that the probabilistic downscaling ability of our model further increased its skill. We observed that neural network predictions may be interspersed by recurrent structures not related to rainfall climatology, which should be a known issue for future studies. We were able to mitigate them by using an appropriate model architecture and model selection process. Our findings suggest that spateGAN offers the potential to complement and further advance the development of climate model downscaling techniques, due to its performance and computational efficiency.

The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long-term research programme to investigate the effects of global environmental change on terrestrial ecosystems and their socio-economic consequences. State-of-the-art methods from the field of environmental monitoring, geophysics, remote sensing, and modelling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long-term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modelling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large-scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO’s key services and functions, presents the main lessons learned from this 15-year effort, and emphasises the need to continue long-term integrated environmental monitoring programmes in the future.

An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path-averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced total loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed three years of attenuation data from 4000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed on average twelve times more blackouts in the CML data than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. In conclusion, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures.