International Conference on Opportunistic Sensing of Precipitation (OpenSense) - Book of Abstracts

International Conference on Opportunistic Sensing of Precipitation Wednesday, June 25, 2025 - Thursday, June 26, 2025 German Weather Service, Offenbach, Germany https://indico.kit.edu/event/4624/ Book of Abstracts www.opensenseaction.eu This publication is based upon worm from COST Action CA20136, supported by COST (European Cooperation in Science and Technology. COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.eu ii Book of abstracts from 1st International Conference on Opportunistic Sensing of Precipitation - OpenSense organized by Deutscher WetterDienst, Offenbach, Germany. Editors: Vojtěch Bareš, Maxmilian Graf, Jonatan Ostrometzky iii iv Contents Program and local organizing committees ........................... 1 Keynotes ............................................. 2 Opportunistic weather observations in EUMETNET: past, present and future .............. 2 Enhancing Numerical Weather Prediction with Opportunistic and Crowdsourced Observations: Insights from NetAtmo and Microwave Link Assimilation ............................ 2 AI in hydrometeorological applications: From now-casting to climate modeling . . . . . . . . . . . . . 3 Opening and welcome session ................................. 4 Oral Session #1: Processing methods .............................. 5 Enhancing Quantitative Precipitation Estimation in Urban Areas Using IoT Sensors and Radar Data . . . 5 Enhanced quantitative rainfall estimation using dual-channel TV-SAT microwave links: Progress and Experimentation ........................................... 7 Improved CML-derived rainfall maps at city scale by introducing quality control algorithms ....... 8 Rain Estimation Over a Region Using CycleGan ............................ 9 Oral Session #2: Bridging the gap ............................... 10 Commercial Microwave Links for Precipitation Monitoring: The Experience of Arpae-SIMC in Emilia-Romanga 10 Potential applications of opportunistic sensing data in operational precipitation products at Deutscher Wetterdienst - from first steps to visions ............................... 11 Oral Session #3: OS data merging ............................... 12 Disaggregating path-averaged rain rate estimates from commercial microwave links with multiplicative random cascade model ....................................... 12 Temporal Super-Resolution, Ground Adjustment and Advection Correction of Radar Rainfall using 3D-Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Weather radar adjustment with commercial microwave links at DWD ................. 14 Merging weather radar fields with data from commercial microwave links using mergeplg ........ 15 The EURADCLIM gauge-adjusted radar precipitation dataset ..................... 16 Oral Session #4: Comparative performance analysis and uncertainty assessment . . . . . 17 Comparison between terrestrial and satellite microwave links as opportunistic rainfall sensors . . . . . . 17 v Exploring Rain Scintillation Spectra from Microwave Links for Raindrop Size Distribution Retrieval . . . 18 Insights in the rainfall dynamics preceding and during the 29 October 2024 Valencia floods using rainfall observations from personal weather stations ............................ 19 Do citizen science data improve the reconstruction of heavy rainfall events? . . . . . . . . . . . . . . 20 Oral Session #5: Application of OS rainfall data ........................ 21 Applications of opportunistic rainfall observations: a review ...................... 21 Precipitation field reconstruction and tracking using opportunistic rain sensors: the Summer 2021 catastrophic event in Germany as a case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Exploiting a dense Commercial Microwave Link (CML) network in Nigeria for high-resolution near-surface rainfall estimates ........................................ 23 Runoff predictions in combined sewer system of the city of Prague using raw attenuation data from commercial microwave links ....................................... 24 Large-scale hydrological modeling with CML rainfall data ....................... 25 Flood forecasting based on personal weather station rainfall data .................... 26 Poster Session #1 ......................................... 27 ISRaCML ............................................. 27 OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City ..... 28 Rainfield Monitoring and Extracting Natural Phenomena Combining CML with Graph Signal Processing . 29 Unsupervised Fault Detection and Classification in Microwave Links for Opportunistic Weather Detection: Differentiating Weather-Induced and Non-Weather Faults ..................... 30 Optimizing Wet Antenna Attenuation Models for Improved Rainfall Estimation Using Commercial Microwave Links .............................................. 31 The OPENSENSE software ecosystem ................................ 32 Citizen observations via the RMI smartphone app in Belgium: data collection and applications . . . . . . 33 Sensing from the Grassroots: How Environmental Measurement Software is Being Developed by Citizen Scientists ............................................ 34 Satellite microwave link open data for rainfall estimation ....................... 35 Refining baseline and wet antenna models for improved rainfall estimation from CML data . . . . . . . 36 Beyond Cellular Networks: Rainfall Estimation Using Low-Frequency and Short-Distance Commercial Microwave Links .......................................... 37 Improving Precipitation Estimates from Commercial Microwave Links Using Deep Learning: A Comparative Study on OpenMRG Data .................................... 38 . GNN-Based Data Fusion for Precipitation Estimation Using Opportunistic Sensors ........... 39 A toolbox for real time data acquisition and quality control of personal weather station rainfall data . . . 40 Publicly available four-year CML dataset for the Netherlands ..................... 41 pypwsqc: A new tool for quality control of personal weather station data rainfall data . . . . . . . . . . 42 Poster Session #2 ......................................... 43 vi Periodic Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 What you should be aware of when nowcasting rainfall in the tropics using CML-based rainfall estimates only .............................................. 44 Relationship between Precipitable Water Vapor and heavy rainfall over Lombardy region in Northern Italy using GNSS and CML sensors network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Evaluation of the Dutch real-time radar precipitation product ..................... 46 Can we estimate the amount of rain water during disastrously large flood events with high resolution? . . 47 Commercial Microwave Link research at the Climate and Earth Lab (Ghent University) ......... 48 Added value of Personal Weather Stations for Precipitation Estimates in the Lazio Region, Italy . . . . . 49 Using Commercial Microwave Links to Estimate Rainfall Intensity and Variability in Rwanda . . . . . . 50 E-band rainfall observation: uncertainties in quantitative measurements and long-term statistics of outages 51 Enhancing Dry-Wet Classification in CML/SML Time Series by Integrating NWC-SAF PC Products and Commercial Microwave Links with Cloud Microphysical Satellite Data .................. 52 Opportunistic rainfall observations in Gothenburg, Sweden: open data and real-time service . . . . . . . 53 Including PWS gauge data in radar merging improves real-time precipitation estimates: Methodology and 1-year evaluation for the Netherlands ............................... 54 Hydro-Climatological Thresholds to Enhance Early Warning Systems for Landslides in Rwanda . . . . . 55 vii viii OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Program and local organizing committees Program Committee Chair Vojtěch Bareš (Czech Technicl University, Prague, Czech Republic) Vice-Chairs Hagit Messer-Yaron (Tel Aviv University, Tel Aviv, Isreal) Tanja Winterrath (Deutscher Wetterdienst, Offenbach, Germany) Members Christian Chwala (Karlsruhe Institue of Technology, Garmisch-Partenkirchen, Germany) Martin Fencl (Czech Technicl University, Prague, Czech Republic) Filippo Giannetti (University of Pisa, Pisa, Italy) Marielle Gosset (IRD/CNRS/Université Toulouse III/CNES, Toulouse, France) Maxmilian Graf (Deutscher Wetterdienst, Offenbach, Germany) Congzheng Han (Institute of Atmospheric Physics Chinese Academy of Sciences, Beijing, China) Roberto Nebuloni (National Research Council of Italy, Milano, Italy) Jonatan Ostrometzky (Tel Aviv University, Tel Aviv, Isreal) Aart Overeem (Royal Netherlands Meteorological Institute, De Bilt, The Netherlands) Magdalena Pasierb (National Research Institute, Warsaw, Poland) Jochen Seidel (University of Stuttgart, Stuttgart, Germany) Flavia Tauro (University of Tuscia, Viterbo, Italy) Remko Uijlenhoet (TU Delft, Delft, The Netherlands) Remco Van de Beek (Swedish Meteorological and Hydrological Institute, Norrköping, Sweden) Local Organizing Committee Matthias Gottschalk (Deutscher Wetterdienst, Offenbach, Germany) Maxmilian Graf (Deutscher Wetterdienst, Offenbach, Germany) Natalia Hanna (TU Vienna, Vienna, Austria) Petra Koudelová (Czech Technical University, Prague, Czech Republic) Tanja Winterrath (Deutscher Wetterdienst, Offenbach, Germany) Page 1 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Improved CML-derived rainfall maps at city scale by introducing quality control algorithms Author: Xin Zheng1 Co-authors: Anna Špačková 2; Martin Fencl 2; Vojtěch Bareš 2 1Hohai University, Nanjing, China 2Czech Technical University In Prague, Czech Republic Corresponding Author: [email protected] The use of opportunistic sensing (OS) devices for rainfall monitoring, such as commercial microwave links (CMLs), has attracted the attention of urban hydrologists and drainage engineers in the recent decade. However, the devices are neither originally designed for rainfall monitoring nor properly maintained or installed. Therefore, they can be affected by various factors other than rainfall, such as environmental conditions and technical issues and thus appropriate quality control (QC) is essential. By implementing QC, potential errors and uncertainties in the data can be identified and corrected, and the accuracy and precision of the estimated rainfall fields can be improved. Previous studies suggested QC algorithms for single links (SL) removing artefacts from the time series of their signal levels leading to improved rain rate estimates and more accurate reconstructed rainfall maps. In this study, we propose a QC method relying on neighbouring links (NL) and compare it with SL algorithm at city scale with high density of CMLs and short correlation lengths. We also investigate the synergy of simultaneously utilizing both QC methods for short time steps of 5 minutes. A dataset of CML measurement was obtained from the T-Mobile network in Prague, Czech Republic. The selected period is between July 2014 and September 2014. The CML network has 173 links. Two datasets were used as the reference. The first reference dataset was generated by interpolating the measurements from 23 local RGs using the inverse distance weighting. The second dataset consisted of C-band radar data that was adjusted using the measurements from these 23 RGs. The overall results show clear improvements of performance metrics when combining both QC algorithms. For validation period RMSE decreased from 2.38 to 1.57 mm/h and Pearson correlation increased from 0.45 to 0.74. The results demonstrate that NL method can be effectively applied for CML-derived rainfall estimates, resulting in larger data availability compared to SL method. NL performs better than SL when the CML density is high, and the superiority diminishes as the CML density decreases. Simultaneously applying both types of QC methods can further reduce errors in the results, but there may be a trade-off with data availability. The results also indicate that performing simple QC operations on the CML measurement before applying NL can retain more data while achieving favourable results. The findings of this study provide guidance for improving the accuracy of retrieval and rain field reconstruction results. Page 8 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Rain Estimation Over a Region Using CycleGan Authors: Hagit Messer1; Jonatan Ostrometzky1; Sagi Timinsky1 1School of Electrical and Computer Engineering, Tel Aviv University Corresponding Author: [email protected] Accurately measuring rainfall is essential for weather forecasting, flood prediction, and water resource management. Traditional methods rely on rain gauges for direct measurements, radar systems for broader coverage and satellites. However, these methods face challenges due to sparse sensor distribution and data coverage. A promising alternative is using wireless commercial microwave links (CMLs)—the infrastructure behind cellular networks. CMLs experience signal attenuation when it rains, allowing them to serve as cost-effective, high-resolution rainfall virtual sensors. However, current training machine learning models require paired CML-rain gauge data, which limits their applicability due to missing or misaligned measurements. To overcome this limitation, we propose a CycleGAN-based framework that enables rainfall estimation without requiring paired datasets. Instead of relying on direct matches between CMLs and rain gauges, our method learns the relationship between the two through an unpaired training strategy. We introduce two mapping functions: • G:A→R (Converts attenuation to rain rate). • F:R→A (Converts rain rate to attenuation). By enforcing cycle consistency, the model ensures that translating between the two domains preserves data structure, even in the absence of direct pairing between a CML and a gauge. Our method offers several key advantages: • Works with missing or sparse data. • Adapts to different regions without direct alignment. • Enhances rain estimation accuracy with a built-in detector. We evaluated our approach on real-world CML datasets and rain gauge data from Israel and the Netherlands, demonstrating high accuracy in estimating accumulated rainfall, especially in heavy rain events. This framework expands the capabilities of deep learning for rainfall estimation by enabling models to learn from unpaired datasets. It provides a scalable and flexible solution that overcomes the limitations of traditional supervised approaches. Page 9 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Oral session #2: Bridging the gap Session Chair: Remko Uihlenhoet Commercial Microwave Links for Precipitation Monitoring: The Experience of Arpae-SIMC in Emilia-Romagna Author: Elia Covi1 Co-author: Giacomo Roversi 2 1Arpae-SIMC, Bologna (IT) 2Ca’ Foscari University of Venice and CNR-ISAC Rome (IT) Corresponding Authors: [email protected], g.r[email protected].it Commercial Microwave Links (CML) offer advantageous properties compared to conventional sensors: they provide greater spatial representativeness than individual rain gauges and are positioned closer to the ground than the atmospheric volumes observed by weather radars. Moreover, CMLs are monitored in real time, and a well-designed opportunistic dataset can achieve latencies of less than 15 minutes. These factors make a CML network a valuable operational tool for precipitation measurement. This objective is being pursued in Emilia Romagna (Italy) by Arpae-SIMC, through the activities of the EU LIFE CLIMAXPO project and the MODMET agreement between Arpae-SIMC and the Italian Civil Protection Department. Real-time CML data are acquired and stored from the Hydro-Meteorological and Climate Structure of the Region (Arpae-SIMC). The data are shared by the company Lepida ScpA and consist of couples of instantaneous transmitted (TSL) and received (RSL) signal power levels (expressed in dBm) at one minute resolution, integrated by metadata about the locations of the antennas and the signal properties. The purpose of this presentation is to report the experience developed within our regional weather service regarding the use of CMLs. In detail, it is intended to show the details of CML acquisition and related difficulties; the activities of the projects involved, in relation to the techniques developed within the COST action OPENSENSE. Page 10 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Potential applications of opportunistic sensing data in operational precipitation products at Deutscher Wetterdienst –from first steps to visions Author: Tanja Winterrath1 Co-authors: Maximilian Graf 1; Hannes Konrad 1; Markus Ziese 1 1Deutscher Wetterdienst Corresponding Authors: [email protected], [email protected], [email protected], [email protected] High-quality precipitation analyses represent key operational products of national meteorological services that serve several applications spanning from weather prediction, flood forecasting, and drought monitoring to disaster management and climate change studies. In Germany, the Deutscher Wetterdienst (DWD) plays a major role in providing regional to global scale quantitative precipitation estimates (QPE) for real-time applications as well as climatological analyses. All these products rely on ground-based precipitation measurements for either interpolation, adjustment, or validation. Opportunistic sensors (OS) - not originally designed for high-quality hydrometeorological observations - such as commercial microwave links (CML) and private weather stations (PWS) increase the density of ground-based sensors and may therefore constitute an important additional source of information that is still neglected in most operational data products. We will introduce exemplary DWD precipitation products and discuss the yet identified and potential benefits, respectively, of considering OS data: a radar-based QPE for real-time and climate applications (RADOLAN/RADKLIM), the Global Precipitation Climatology Centre’s (GPCC) gridded products based on interpolated station data, and the solely satellite-retrieved precipitation estimate GIRAFE. The radar-based QPE products RADOLAN and RADKLIM use ground-based observations to adjust remotely-sensed indirect information to quantitative precipitation values. Classical pluviometers, however, due to the relatively low network density miss a significant fraction of local heavy precipitation events. Within the project HoWa-PRO, DWD has established a multi-source data merge including CML data to overcome this limitation and provide improved QPE to the flood forecasting centers of the German federal states in charge. GPCC operates under the auspices of the World Meteorological Organization (WMO) collecting and archiving world-wide station data, performing quality control, and providing interpolated gridded precipitation fields and derived products for monitoring as well as climate change studies. Together with an increasing demand for higher temporal and spatial resolution a high spatial density of observations becomes more important - especially in data-sparse regions. A complementary inclusion of OS data is a promising option to support this plan. GIRAFE is a solely satellite-based precipitation estimate by the EUMETSAT Climate Monitoring Satellite Application Facility (CM-SAF) operating at DWD. GIRAFE uses ground-based precipitation data for validation purposes. As validation is challenging in data-sparse regions like e.g. on the African continent, OS data may serve as additional ground truth for product quality measures. Page 11 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Oral session #3: OS data merging Session Chair: Jonas Olsson Disaggregating path-averaged rain rate estimates from commercial microwave links with a multiplicative random cascade model Author: Martin Fencl1 1Czech Technical University in Prague Corresponding Author: [email protected] Commercial microwave links (CMLs) measure total attenuation along their path. Thus when used as opportunistic sensors, they provide path-averaged rainfall estimates. This poses a challenge for rainfall map reconstruction and potentially for rainfall estimation itself, as the conversion of attenuation to rain rate implicitly assumes uniform rainfall along the CML path. We propose a new algorithm called CLEAR (CML Segments with Equal Amount of Rainfall) for disaggregating path-integrated rain rates along a CML path using a multiplicative cascade model. This model redistributes rain rates by successively dividing the CML into segments with equal rainfall amounts but varying segment lengths. The redistribution is driven by a cascade generator, with variance dependent on the rain rate of the parent segment and its length. Spatial consistency of the disaggregated rain rates across the entire network is ensured by a spatial coherence rule, which determines which segment receives a higher rain rate using information from neighbouring CMLs. The algorithm is tested on CML path-averaged rain rates obtained from 210 virtual rainfall fields, simulated for a real network topology in Prague, CZ, consisting of 67 CMLs. In addition, the performance of the algorithm is demonstrated in a case study where real observations from the same CML network are used. The CLEAR algorithm is efficient in estimating rainfall maxima and minima along a CML path, achieving RMSE values of 2.8 and 1.3 mm/h, respectively, compared to 4.7 and 3.5 mm/h for the original path-averaged rain rates. It also outperforms the benchmark GMZ algorithm, which has for estimated rain rate maxima and minima along a CML path RMSE values of 6.8 and 1.8 mm/h, respectively. While the results are slightly worse when considering the exact position of the disaggregated rain rates, CLEAR still outperforms GMZ in this aspect. Additionally, due to the stochastic nature of multiplicative cascades, CLEAR is capable of providing uncertainty estimates. The evaluation shows that CLEAR tends to underestimate uncertainty, as reflected in the width of the uncertainty bands. This is partly due to shortcomings in reproducing rainfall intermittency and partly because the ensemble variance is driven by a cascade generator model that does not account for uncertainties in the spatial coherence rule. Multiplicative cascades used in the CLEAR algorithm have proven to be efficient for 1D disaggregation and are applicable even for sparse CML networks. However, further research is needed to enhance CLEAR’s uncertainty estimation and improve the estimation of rainfall intermittency. Page 12 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Temporal Super-Resolution, Ground Adjustment and Advection Correction of Radar Rainfall using 3D-Convolutional Neural Networks Author: julius polz1 Co-authors: Christian Chwala 2; Harald Kunstmann 2; Hiob Gebisso 2; Luca Glawion 2; Lukas Altenstrasser 2; Maximilian Graf 3; Stefanie Vogl 4 1KIT/IMK-IFU 2KIT (IMK-IFU) 3Deutscher Wetterdienst 4OTH Regensburg Corresponding Authors: [email protected], [email protected], [email protected], [email protected], [email protected], stefanie.v[email protected], [email protected], [email protected] Ground-adjustment of weather-radar derived precipitation information is a common practice to correct for a variety of errors related to for example advection, size sorting or melting processes. Historically, this is mainly achieved by using point-like rain gauge observations which have a high temporal resolution and accuracy, but lack spatial representativeness and observation density, e.g. with one rain gauge per 330km² in Germany. In this study, we combine two novel approaches to enhance quantitative precipitation estimation (QPE) with weather radars. First, we use Commercial Microwave Links (CMLs) as an additional source of information. CMLs provide path-integrated attenuation estimates close to the ground which yields a higher spatial representativeness than rain gauges. Due to their large abundance, they also largely increase the density of near-ground observations. Second, we present a novel probabilistic deep-learning-based framework to combine radar, rain gauge and CML data. The presented perceiver architecture is generic and can easily be extended by additional input modalities. Our study is based on the RADOLAN-RY precipitation product, derived from the C-band radar network of the German Weather Service (DWD), and a German-wide CML network with 3900 link paths. The results are compared to ResRadNet 1, a deep-learning model that only relies on radar input for ground-adjustment. Page 13 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Weather radar adjustment with commercial microwave links at DWD Author: Maximilian GrafNone Co-authors: Christian Chwala 1; Malte Wenzel ; Matthias Gottschalk 2; Tanja Winterrath ; julius polz 3 1KIT (IMK-IFU) 2Deutscher Wetterdienst 3KIT/IMK-IFU Corresponding Authors: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] Weather radars provide high-resolution precipitation data but are subject to uncertainties due to their indirect measurement high above ground. To improve data quality, national meteorological services calibrate radar observations using ground-based station measurements for both operational and climatological applications. The emergence of opportunistic sensors (OS), data sources not originally designed for high-quality hydrometeorological observations, such as commercial microwave links (CML) and private weather stations (PWS), offers the potential to increase the density of groundbased sensors for the radar adjustment. As part of the HoWa-PRO project, the Deutscher Wetterdienst, in collaboration with Ericsson, has established a real-time CML data flow for radar adjustment. To facilitate this, the Python framework pyRADMAN was developed, enabling low-latency merging of radar, station, and CML data. Built upon the existing RADOLAN methodology, pyRADMAN extends its capabilities by incorporating CML observations and testing advanced methods such as kriging with external drift, conditional merging, and radar pre-correction techniques. These enhancements improve precipitation estimates and reduce latency compared to traditional RADOLAN products, tested up to a temporal resolution of 15 minutes. Looking ahead, the modular architecture of pyRADMAN enables the seamless implementation of future calibration techniques. The integration of opportunistic sensor data gives opportunities for accurate, high-resolution precipitation estimation, both in an operational and research setting. Page 14 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Merging weather radar fields with data from commercial microwave links using mergeplg Authors: Erlend Øydvin1; Maximilian.[email protected] GrafNone; Christian ChwalaNone; Elia CoviNone 1NMBU Corresponding Authors: [email protected], [email protected], [email protected], elia.c.co[email protected] Quantitative precipitation estimates are important for monitoring the water balance. Consequently, there exists a wide range of rainfall measurement methods. Weather radar has good spatial coverage, but the estimates can be biased. Ground observations, like rain gauges and commercial microwave links (CMLs), provide more accurate estimates, but have less good spatial coverage. Adjusting the weather radar field to fit the ground observations (merging) can provide more accurate rainfall precipitation fields. Within OPENSENSE we have developed a python package, mergeplg, that implements different methods for merging weather radar fields to ground observations. In this work we provide results and insights from merging weather radar data to ground observations, with a focus on CML data, using two open access datasets OpenMRG and OpenRainER. In general, CMLs improve the raw radar estimates for both datasets. Page 15 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts The EURADCLIM gauge-adjusted radar precipitation dataset Author: Aart Overeem1 Co-authors: Else van den Besselaar 1; Gerard van der Schrier 1; Jan Fokke Meirink 1; Emiel van der Plas 1; Irene Garcia-Marti 1; Lotte de Vos 1; Hidde Leijnse 1 1Royal Netherlands Meteorological Institute Corresponding Authors: irene[email protected], emiel.van.der[email protected], gerard.van.der[email protected], [email protected], [email protected], lotte.de[email protected], [email protected], else[email protected] EURADCLIM is a publicly available climatological dataset of 1-h and 24-h precipitation accumulations covering Europe at a 2-km grid over the period 2013 –2022. It is based on the surface rain rate composites from the EUMETNET programme OPERA. Algorithms are applied to remove remaining non-meteorological echoes as much as possible. The 1-h accumulations are merged with rain gauge accumulations from the European Climate Assessment & Dataset (ECA&D). Details on the employed datasets and algorithms are presented. The quality and shortcomings of EURADCLIM version 2 (https://doi.org/10.21944/ymrk-mr24 ) are assessed by comparisons to (independent) rain gauge data and are presented by means of scatter density plots, a spatial verification, and case studies. EURADCLIM clearly has a higher quality than the original OPERA product. The potential of EURADCLIM for deriving a pan-European precipitation climatology is shown. EURADCLIM could serve as a reference dataset for precipitation estimates from opportunistic sensing. We demonstrate this by comparing a merged dataset based on radar (the OPERA product) and rain gauge data from personal weather stations (PWSs) over Europe to version 1 of EURADCLIM. The results show a better agreement of the merged dataset with the EURADCLIM ground truth than the version without PWS data, thus highlighting the potential of crowdsourced rain gauge data for improving radar precipitation products. Finally, the newest findings from research and development on EURADCLIM are presented including what can be expected from version 3. Page 16 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Oral session #4: Comparative performance analysis and uncertainty assessment Session Chair: Remko Uijlenhoet Comparison between terrestrial and satellite microwave links as opportunistic rainfall sensors Authors: Filippo Giannetti1; Fabiola Sapienza1; Vincenzo Lottici1; Giovanni Scognamiglio2; Attilio Vaccaro2; Elia Covi3; Carlo De Michele4; Christian Gianoglio5; Matteo Colli6; Roberto Nebuloni7 1Department of Information Engineering, University of Pisa 2MBI 3Arpae-SIMC 4Politecnico di Milano 5Department of Naval, Electrical, Electronic and Telecommunications Engineering, University of Genoa 6Artys, Darts Engineering Srl 7CNR Corresponding Authors: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] The backhauling links of terrestrial wireless networks (Commercial Microwave Links, CMLs) and the downlink of satellite broadcasting/broadband services (Satellite Microwave Links, SML) operating in the Ku-band and above, say >10 GHz, proved effective opportunistic systems for rainfall sensing. CMLs and SMLs exhibit, indeed, features that make them suitable to complement conventional measurements carried out by rain gauge networks, weather radars, and Earth observation satellites. Based on the experience achieved by several research teams active on this topic in Italy since 2017, this paper compares CMLs and SMLs as opportunistic rainfall sensors from different perspectives. We address technical aspects (complexity of data acquisition and processing), performance (spatiotemporal resolution, accuracy, sensitivity and benefits brought by AI techniques), data accessibility and ownership, and deployment and operational costs. Finally, the perspectives of such opportunistic sensors and their potential as operational tools are also assessed in accordance with the evolution of wireless networks: in particular, the increase of fiber backhauling and the shift towards mmWave bands for CMLs will be important aspects for the future of CMLs, whereas the deployment of large and mega constellations of LEO satellites may be beneficial to SMLs. Acknowledgment - This work was supported by the following projects: SCORE, funded by European Commission’s Horizon 2020 research and innovation programme under grant agreement no. 101003534; Space It Up, funded by the ASI, the MUR –Contract no. 20245-E.0 - CUP no. I53D24000060005; FoReLab (Departments of Excellence), funded by the Italian Ministry of Education and Research (MUR); COST Action CA20136 OPENSENSE, funded by COST (European Cooperation in Science and Technology); MODMET agreement between DPC and ARPAE. Page 17 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Runoff predictions in combined sewer system of the city of Prague using raw attenuation data from commercial microwave links Authors: Ying SongNone; Martin Fencl1; Vojtěch Bareš1 1Czech Technical University in Prague Corresponding Authors: [email protected], [email protected], [email protected] Commercial microwave links (CMLs) have recently shown great potential in urban drainage modelling due to their ability to provide rainfall-runoff dynamics. Previous studies typically used mechanistic hydrodynamic models driven by quantitative precipitation estimates (QPEs) derived from CML attenuation data. Naturally, some errors are introduced, primarily related to CML rainfall retrieval model, including uncertainties in wet antenna attention correction, as well as errors originated from path-averaged character of CML QPEs. These processing steps not only generate some new uncertainties but also result in a loss of valuable information contained in raw data. Besides, mechanistic models require high-quality pre-processed input rainfall data, which adds complexity to the application. We address these issues by employing raw CML attenuation data without QPE derivation using datadriven rainfall-runoff models in the overall Prague catchment, where runoff is influenced by both rainfall and residential water use. We find that: (1) Raw CML attenuation data can be effectively used to obtain the discharges despite the additional influence of household water consumption, achieving NSE >0.7 and PCC> 0.85 over all sub-catchments. (2) Compared with rain-gauge data as inputs, CML attenuation data outperforms in heavy and long-period rain events (e.g. reducing RMSE by 17% and increasing PCC by 18% respectively). (3) CML performs better in sub-catchments A and F, where CMLs are densely distributed, and rainfall is highly concentrated within the catchment. However, its performance in sub-catchment K is poorer, likely due to its larger area, sparser CML coverage, and longer rainfall-runoff concentration time. Additionally, the influence of non-rainfall-related flows becomes more pronounced, potentially reducing the predictive advantage of CML. (4) Models using CML data as input enable runoff prediction beyond the catchment’s rainfall-runoff lag time, whereas models with rain gauge data deteriorate quickly. CML-based hydrological modelling is effective in purely rainfall-driven urban basins and adaptable to more complex systems influenced by human activities. This research underscores CML’s potential as a robust alternative to traditional rain gauges, particularly for improving real-time runoff predictions in data-scarce urban environments. Page 24 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Large-scale hydrological modeling with CML rainfall data Authors: Andrijana Todorovic1; Carlo De Michele2; Cristina Deida3; Greta Cazzaniga4; Roberto Nebuloni5 1University of Belgrade, Faculty of Civil Engineering, Institute for Hydraulic and Environmental Engineering 2Politecnico di Milano 3Vrije Universiteit Brussel 4Laboratoire des Sciences du Climat et de l’Environnement 5Cnr-Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni (CNR-IEIIT) Corresponding Authors: [email protected], [email protected], [email protected], [email protected] Accurate spatio-temporal representation of rainfall is essential for hydrological (i.e., rainfall-runof) modelling, and for the later applications of hydrological models. Rainfall data are usually obtained from official raingauge networks; however, these networks are often sparce and/or even with declining number of stations. To improve spatio-temporal representation of rainfall, various opportunistic sensors have been considered, including commercial microwave links (CML). CML-based rainfall data have been used for hydrological modelling in small urban catchments for years, but their applications in large catchments is lagging behind. Development of hydrological models in large catchments requires long rainfall series, which is not the case when it comes to CML datasets. Thus, a thorough evaluation of hydrological model transferability across different rainfall inputs is essential for wider application of the CML data. In this study, a semi-distributed hydrological model developed for the peri-urban Lambro catchment in Northen Italy (Cazzaniga et al., 2022; doi: https://doi.org/10.5194/hess-26-2093-2022) is evaluated from the standpoint of its transferability across the rainfall inputs. To this end, the model is run with conventional input obtained from the raingauge network (RG), from the CML-based rainfall, and combination thereof (CML-RG). The model performance is evaluated over 12 flood events, four of which are low-rate events (maximum rain intensity below 15 mm/h), while maximum rainfall intensity exceeds 35 mm/h during four most extreme ones. The model performance significantly varies across the events. Although the RG model yields the highest performance in most instances, it is outperformed by the model forced with CMLand/or CML-RG data during the three most extreme events. There is no strong correlation between the peak rainfall intensity and model performance; however, the RG model outperforms the CMLand CML-RG models over the low-intensity events. The CML model is outperformed by the CMLRG and, especially, RG model according to Nash-Sutcliffe efficiency, relative error in peak flows and in runoff volume, however, CML model performs best in most cases according to the coefficient of determination, which suggests that this rainfall input best captures rainfall dynamics. This study clearly indicates a great potential of CML to improve hydrological model performance in high-flow range. It also suggests that further research is needed to reduce biases in rain depth estimation at a sub-catchment level. Further research is also needed to enable optimal combination of different rainfall inputs to hydrological model, as well as to improve spatial discretisation of hydrological models to better accommodate “linear”CML rainfall data. Page 25 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Flood forecasting based on personal weather station rainfall data Author: Jisca Schoonhoven1 Co-authors: Claudia Brauer 1; Linda Bogerd 1 1Wageningen University Corresponding Author: [email protected] An increasing number of personal weather stations (PWSs) is installed by citizens, resulting in a large amount of real-time available precipitation data. This study assesses the applicability of these data for flood forecasting. We focussed on 30 catchments (total area 2474 km2) located in the management area of Water Board Rijn and IJssel, a water authority in the Netherlands which actually uses PWS data as input for their operational flood forecasting system. We compared rainfall from a network of Netatmo PWSs (after applying a quality filter) and the real-time radar product from the KNMI (Royal Netherlands Meteorological Institute). Next, we used both products as input for the rainfall-runoff model WALRUS and compared the simulated discharges. These two datasets with almost no latency were validated with the final reanalysis KNMI radar product and discharge observations, for a full year (2023), using the Kling–Gupta efficiency (KGE). For precipitation the KGE was higher for the real-time radar (0.80 for the entire area) than for the PWSs (0.68). For discharge simulations the KGE was lower for the real-time radar (median of the subcatchments: 0.46) than for the PWSs (0.70). This contrasting result can be explained by the bias, which was higher for the real-time radar than for the PWSs, and is amplified in the discharge simulations due to the memory in the hydrological system. During ten selected high discharge events, the simulations with real-time radar approached the observations more closely than with the PWSs. The results indicate a potential of these devices to be used in hydrological applications, especially when initial hydrological model conditions are improved with data assimilation in operational flood forecasting systems. Page 26 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Poster Session #1 Session Chair: Congzheng Han ISRaCML Author: Matan Antebi1 1Tel Aviv University Corresponding Author: [email protected] Accurate and continuous monitoring of precipitation and extreme weather events, such as heavy rainfall and flooding, is essential for mitigating their devastating impacts on both natural and built environments. The increasing frequency and intensity of such hydrometeorological events, exacerbated by climate change, necessitate the development of innovative and cost-effective observational methodologies to complement traditional ground-based measurement networks. Opportunistic sensors (OS), which leverage existing infrastructure for environmental monitoring, provide a valuable alternative for enhancing spatial and temporal precipitation assessments, particularly in regions with limited meteorological instrumentation. Cellular microwave links (CMLs), a prominent example of OS, offer an extensive and high-resolution dataset for rainfall estimation, making them particularly useful in diverse climatic regions such as Israel, which spans from hyper-arid zones in the south to temperate conditions in the north. In this study, we will present ISRaCML dataset which includes frequencies ranging (17 - 23GHz) KBand, path lengths varying (0.5 - 16km), presenting observations from CMLs deployed across Israel, sourced from a number of telecommunications providers, Cellcom, Pelephone and Orange, over the period of January 1, 2017, to August 31, 2017. The dataset is structured to include multiple temporal resolutions: 15-minute and 24-hour instantaneous values, min-max aggregates for 15-minute and 24-hour intervals, and min-max aggregates for 7-day 24-hour periods. Additionally, it integrates ground-based precipitation measurements from 85 rain gauges maintained by the Israel Meteorological Service (IMS), thereby furnishing a complementary dataset for validation and cross-referencing. This dataset constitutes a critical resource for enhancing precipitation retrieval algorithms, as it consists of, for the first time, CML measurements with different protocols by different network operators within the same time frame and area. Page 27 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City Author: Dror Jacoby1 1Tel Aviv University Corresponding Author: [email protected] We introduce OpenMesh, a publicly available dataset of wireless links designed for high-resolution weather monitoring in dense urban environments. Collected from NYC Mesh—an initiative primarily aimed at providing affordable internet access—this dataset demonstrates how opportunistic usage can transform existing communication infrastructure into a platform for real-time meteorological observation. Spanning eight months (November 2023–June 2024), the dataset includes measurements from 100 directional wireless links sampled at 1-minute intervals. These links operate across both lower (~5– 6GHz) and higher V-band (40–70 GHz) channels. While higher frequencies undergo substantial atmospheric attenuation—especially during precipitation—this challenge doubles as an opportunity for real-time, localized weather sensing within mesh networks. Centered on Lower Manhattan and Brooklyn in New York City, our dataset also incorporates regional meteorological records for validation, enhancing the evaluation process and advancing research in wireless-based atmospheric sensing. Aligned with ongoing efforts in opportunistic weather data, OpenMesh adheres to established environmental monitoring standards. By openly sharing these data, we invite further research and encourage practical applications leveraging 5G/6G capabilities for resilient, real-time urban-scale sensing—ultimately guiding next-generation networks (NGNs) toward more sustainable solutions. Page 28 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Rainfield Monitoring and Extracting Natural Phenomena Combining CML with Graph Signal Processing Author: Yaara PeledNone Corresponding Author: yoy[email protected] Measuring natural phenomena through opportunistic sensing is crucial for maximizing the use of existing data for research and weather warning systems. This research is an addition to existing work that has been done connecting CML information with rainfield monitoring. by integrating this signal attenuation-based rain estimation with graph signal processing techniques we can gain better rainfield monitoring and extract natural phenomena from anomalies in the graph. In this approach, graph vertices represent receiver (RX) measurements positioned at the midpoint of CML, functioning as single-point rain sensors. The edges and weights of the graph are determined by geographic proximity and correlation relations, establishing strong relationships between nearby and similar nodes. By applying graph signal processing, this research aims to: 1. Detect anomalies in the graph, where anomalous links may indicate regions with heavy rainfall potential that could lead to floods (extract natural phenomenon). 2. Improve rainfield monitoring from the graph connectivity and build better interpolation. 3. Clustering the graph into meaningful categories: 3a. Differentiating rain intensity levels. 3b. Distinguishing between urban and rural rainfall characteristics. 4. Optimize computational efficiency, selectively utilizing only the most relevant CML to reduce power consumption and filter out redundant information. This methodology has the potential to enhance existing rainfield monitoring based on CML information. Page 29 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Unsupervised Fault Detection and Classification in Microwave Links for Opportunistic Weather Detection: Differentiating WeatherInduced and Non-Weather Faults Author: Adi GreenNone Corresponding Author: [email protected] Changes in communication signals due to weather conditions are often misclassified as faults, making it challenging to differentiate between meteorological effects and actual network malfunctions, such as physical obstructions (e.g., new construction blocking the signal path) or hardware failures. In this work, we propose an unsupervised learning framework for fault detection and classification in commercial microwave links (CMLs), distinguishing between weather-related and nonweather faults. Our approach is based on an autoencoder (AE) trained on mixed data to establish a reconstruction-based threshold for identifying potential fault regions. We then extract features from the encoder’s latent space and combine them with domain-specific spatial and temporal features to enhance characterization. These enriched representations are clustered to capture both localized and regional fault patterns, allowing us to differentiate between faults caused by meteorological events —such as precipitation affecting multiple links simultaneously—and those resulting from structural or equipment-related issues. Beyond improving fault classification accuracy, our method enables opportunistic sensing of weather-induced signal variations, offering a valuable tool for both network maintenance and meteorological monitoring. Page 30 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Optimizing Wet Antenna Attenuation Models for Improved Rainfall Estimation Using Commercial Microwave Links Authors: Smit Doshi1; Carlo De Michele2; Greta Cazzaniga3; Roberto Nebuloni4 1Alfred Wegener Institute Helmholtz Center for Polar and Marine Research 2Politecnico di Milano 3LSCECEA, CNRS 4CNR Corresponding Authors: [email protected], [email protected], [email protected], [email protected] Accurate rainfall estimation using commercial microwave links (CMLs) is set back by wet antenna attenuation (WAA), which could lead to overestimation of rainfall intensity when not properly accounted. This study introduces a novel framework for minimizing WAA effects through an optimized calibration approach. The proposed methodological framework utilizes an effective distance metric to associate CML data with rain gauge (RG) assessment, integrating a weighted calibration process that gives more importance to high-intensity rainfall events. As a preliminary step, a comparative assessment of several existing WAA models - including Schleiss, Rieckermann, and Berne (SRB), Leijnse, Uijlenhoet, and Stricker (LUS), Kharadly and Ross (KR), Garcia-Rubia, Riera, Benarroch, and Garcia-del-Pino (GRBG), and Valtr, Fencl, and Bareš (VFB) was carried out. Most of the models that predict WAA from rainfall intensity, attenuation or rainfall attenuation are basically equivalent. The WAA compensation process was applied to data from 77 CMLs in the Seveso River basin (Northern Italy) across different types of rain events in 2019-2020. A modified version of VFB model (VGBm – calibrated VFB model), showcases analytically better performance across various CML lengths, frequencies, and rainfall intensities. In particular, VFBm model leads to much better results than SRB (a commonly used model that predicts a saturation of WAA to a relatively small 2.3 dB value) over all the relevant key performance indicators. These findings highlight that correcting WAA is important for accurate rainfall intensity estimates from CML data and emphasize the need to adjust the WAA models to local conditions for improved hydro-meteorological applications. Page 31 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts The OPENSENSE software ecosystem Author: Christian Chwala1 Co-authors: Maximilian Graf 2; Bas Walraven 3; Erlend Øydvin 4; Elia Covi 5; Jochen Seidel 6; Louise Louise Petersson Wårdh 7; George Schutz 8; Martin Fencl 9; Nico Blettner ; Abbas El Hachem ; Lotte de Vos 10; Hai Victor Habi 11; Aart Overeem 12 1KIT (IMK-IFU) 2German Weather Service, Germany 3Delft University of Technology 4NMBU 5Arpae-SIMC 6University of Stuttgart, Germany 71) Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, Norrköping SE-601 76, Sweden (2) Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden 8RTC4Water 9Czech Technical University In Prague, Czech Republic 10 Royal Netherlands Meteorological Institute 11 Tel Aviv University 12 Royal Netherlands Meteorological Institute (KNMI) Corresponding Authors: e[email protected], lotte.de.v[email protected], [email protected], [email protected], b[email protected], [email protected], aart.over[email protected] The focus of working group 2 of OPENSENSE is on method and software homogenisation. We have reviewed the existing software available for processing opportunistic rainfall sensor data and did provide example applications executable online in the so called OPENSENSE software sandbox. There we have identified synergies but also implementation gaps. Based on this we have set a roadmap for developing individual new software packages to create an ecosystem of packages that work well together. The foundation of our software ecosystem is the package poligrain which provides commonly used functionalities for loading data, plotting maps, comparing sensor data and for doing validation. All this is done with a focus on data provided on a grid, as point data, but also for line geometries. On top of poligrain, individual packages for processing data are being built. The existing CML processing package pycomlink was adapted to fit into this ecosystem and two new packages were created. The new package pypwsqc provides sophisticated methods for quality control of PWS data. The new package mergeplg provides different methods for merging point, line and grid data with a focus on merging weather radar and CML data. In this contribution we will given an overview of this software ecosystem and briefly present the individual packages. Page 32 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Citizen observations via the RMI smartphone app in Belgium: data collection and applications Author: Maarten Reyniers1 1Royal Meteorological Institute of Belgium Corresponding Author: [email protected] To enhance meteorological data collection and nowcasting capabilities, the Royal Meteorological Institute (RMI) of Belgium integrated a citizen observation feature into its smartphone app in August 2019. This initiative has since accumulated over 3.3 million observations, including 56,000 usersubmitted photos, significantly enriching RMI’s meteorological datasets. While the majority of user reports capture more general weather conditions such as sunny, overcast skies, or rain, the most valuable contributions focus on key weather phenomena like snow, hail, thunderstorms, and road conditions, providing real-time, localized insights. Each observation undergoes a plausibility check based on timestamp, location, and content, ensuring data reliability. A user reputation system further refines data quality, with 78% of users maintaining a reputation score of 90 or higher on a scale of 0 to 100. This dataset has proven valuable for multiple applications at RMI, including the validation of weather radar estimates for hail detection, improving snow height model forecasts, and enhancing crisis response mechanisms. Specifically, citizen observations have been leveraged by regional hydrological and road management authorities for real-time flood monitoring and hazardous road condition assessments, respectively. The integration of citizen observations into meteorological workflows marks a significant advancement in data collection, bridging gaps in traditional sensor networks and enabling better forecasting models. As the RMI continues to refine its methodologies, this approach holds promise for expanding the role of crowdsourced meteorological data across Europe. Page 33 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts A toolbox for real time data acquisition and quality control of personal weather station rainfall data Authors: Nathalie Rombeek1; Jochen Seidel2; Georges Schutz3 1Department of Water Management, Delft University of Technology, the Netherlands 2Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Germany 3RTC4Water s.à.r.l., Roeser, Luxembourg Corresponding Authors: [email protected], [email protected], [email protected] The high network density of personal weather stations (PWSs), often exceeding that of official weather stations from national meteorological agencies, offers a large potential to improve precipitation estimates. Another advantage is that PWSs have a high temporal resolution (∼5~min), are available in (near) real-time and can potentially be used for now-casting, flood forecasting or early warning system. For such purposes the latency and quality of the data are important aspects to consider. We explored the real-time potential of rainfall data from PWSs from the private company Netatmo, which can be accessed via an application programming interface (API). We analysed the real-time accessibility and latency of the data and developed concepts of how existing quality control algorithms can be applied in the context of real-time applications. First results of these analyses and a road map for implementing this as a software package are presented. Page 40 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Publicly available four-year CML dataset for the Netherlands Author: Aart Overeem1 Co-authors: Bas Walraven 2; Hidde Leijnse 1; Remko Uijlenhoet 3 1Royal Netherlands Meteorological Institute 2Delft University of Technology 3TU Delft Corresponding Authors: [email protected], [email protected], r.uijlenho[email protected], b.walrav[email protected] We present a dataset of commercial microwave link (CML) received signal levels for the Netherlands. This can be used to estimate path-average rainfall between telephone towers. It contains microwave frequency, end date & time of reading, minimum & maximum received power, path length, coordinates, link identifier, errored seconds, and severely errored seconds. The dataset consists of on average 3070 sub-links over 1818 unique link paths covering the Netherlands, having a temporal resolution of 15 min. The dataset spans the period 13 January 2011 up to and including 15 March 2015, although data gaps exist. It contains part of the network from one of the three mobile network operators in the Netherlands during this 4-year period. The data have been provided by the mobile network operator (MNO) T-Mobile NL (since 5 September 2023 called Odido). Note that the transmitted signal levels were not available and are nearly constant. No adaptive power control (ADPC) was used. The following characteristics of the dataset are presented: 1) timeseries of number of sub-links and link paths as a function of time; 2) a scatter density plot of microwave frequency versus link length; 3) map of the Netherlands with the CML locations and their availability per year; 4) an example of the application of the dataset: a 3-month rainfall map based on merged radar and CML accumulations, which is compared to two gauge-adjusted radar rainfall maps. We hope that this CML dataset will contribute to the OpenSense goal of comparing the performance of CML rainfall retrieval algorithms on common datasets and will lead to improved algorithms. Moreover, a publicly available reference dataset of gauge-adjusted radar rainfall accumulations is available covering the same period and area. Overeem, A., Walraven, B., Leijnse, H. (H., & Uijlenhoet, R. (2024). Four-year commercial microwave link dataset for the Netherlands (Version 1) [Data set]. 4TU.ResearchData. https://doi.org/10.4121/BE252844B672-471E-8D69-27269A862EC1.V1 Page 41 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts pypwsqc: A new tool for quality control of personal weather station data rainfall data Authors: Jochen Seidel1; Louise Louise Petersson Wårdh2; Nicholas Illich1; Lotte de Vos3; Christian Chwala4 1Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Germany 21) Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, Norrköping SE-601 76, Sweden (2) Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden 3Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands 4Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany Corresponding Authors: [email protected], louise[email protected], [email protected], [email protected] stuttgart.de, [email protected] The use of so-called opportunistic rainfall sensors like Personal Weather Stations (PWS) and Commercial Microwave Links has gained much attention over the recent year, as they clearly outnumber professional rain gauges which are operated by national weather services and other. However, the data quality of such sensors is typically low and thus their information cannot be used without thorough quality control. Various quality control algorithms for PWS rainfall data have been developed and published within the EU COST Action CA 20136 “Opportunistic Precipitation Sensing Network” (OPENSENSE) in the past years and are available on OPENSENSE’s GitHub (El Hachem et al. 2024). These QC algorithms are now available in a Python package. The new functionalities of these QC filters include (1) an improved indicator correlation filter which was originally developed by Bárdossy et al. (2019) which now provides a skill score for the accepted PWS to assess quality of the indicator correlation with neighbouring references, (2) an algorithm to correct rainfall peaks in PWS data which may be caused by connection interruptions between the rain gauge and the base station and (3) a Python implementation of the QC algorithms for identifying faulty zeroes, high influxes and station outliers originally developed in R by de Vos et al. (2019). These new functionalities areimplemented in the‘pypwsqc’Python package (https://zenodo.org/records/14177798) which is currently under development in the OPENSENSE COST Action. In this contribution we present the new features and guidelines for usage. Bárdossy, A., Seidel, J., and El Hachem, A. (2021), The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrol. Earth Syst. Sci., 25, 583–601. El Hachem, A., Seidel, J., O’Hara, T., Villalobos Herrera, R., Overeem, A., Uijlenhoet, R., Bárdossy, A., and de Vos, L.W (2024), Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations, Hydrol. Earth Syst. Sci., 28, 4715–4731. de Vos, L.W., Leijnse, H.,Overeem, A., and Uijlenhoet, R. (2019), Quality control for crowdsourced personal weather stations to enable operational rainfall monitoring. Geophysical Research Letters, 46, 8820–8829. / Page 42 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Poster Session #2 Session Chair: Jochen Seidel Periodic Noise Author: Sagi AlonNone Corresponding Author: [email protected] This work explores the phenomenon of periodicity (usually at 24-hour periods) in the received signal level observed in Commercial Microwave Links. We will present an overview based on several observations from different locations around the world (mainly in Germany, Israel, Sweden, and Italy) and from different sources (cellular backhaul commercial microwave links and smart-city wireless network of mm-wave links) which are collecting data with different characteristics (e.g. sampling methods like instantaneous and min/max samples at different sampling rates, using different quantization levels). We will share insights on how atmospheric factors (e.g., weather) as well as hardware characteristics might play a role in these signal fluctuations, relating to previously reported studies. Preliminary results suggest that fluctuations during the daily cycle can reach a few decibels at a number of locations, regardless of whether or not precipitation is present. Despite the obvious correlation with a number of daily phenomena such as temperature, air pressure, and absolute humidity, the exact causes are still not fully understood, as correlation does not mean causation. We will present key observations and show that, while a daily cycle in atmospheric conditions seems to match the pattern of signal loss, there is not yet a definitive cause-and-effect pattern for why this happens. Understanding this phenomenon is important both from an opportunistic sensing point of view and from a practical point of view. On the opportunistic sensing side, a better understanding of how atmospheric changes affect wireless signals could help fill in the gaps in current theories and allow for more accurate opportunistic weather sensing. In practice, better insight into these effects could help communication service providers design more reliable networks. Page 43 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts What you should be aware of when nowcasting rainfall in the tropics using CML-based rainfall estimates only Author: Bas Walraven1 Co-authors: Aart Overeem 2; Luuk van der Valk 1; Miriam Coenders 1; Remko Uijlenhoet 3; Rolf Hut 1; Ruben Imhoff 4 1Delft University of Technology 2Royal Netherlands Meteorological Institute (KNMI) 3TU Delft 4Deltares Corresponding Authors: rub[email protected], [email protected], r.w[email protected], b.walrav[email protected], aart.over[email protected], l.d.vanderv[email protected], r[email protected] Accurate and timely precipitation forecasts are crucial for flood early warnings and mitigating other rainfall-induced natural hazards like landslides. For forecasts up to three hours ahead, rainfall nowcasts are increasingly being used. Generally, these nowcasts statistically extrapolate real-time remotely sensed quantitative precipitation estimates, often based on weather radars. However, the global distribution of high-resolution (gauge-adjusted, ground-based) weather radar products is heavily skewed, largely favoring Europe, Northern America, and parts of East Asia. In many lowand middle-income countries, predominantly located in the tropics, weather radars are largely unavailable due to high installation and maintenance costs, and rain gauges are often scarce, poorly maintained, or not available in (near) real-time. A viable and ‘opportunistic’source of high-resolution space-time rainfall estimates is based on the rain-induced signal attenuation experienced by commercial microwave links (CMLs) in cellular communication networks. Based on received signal power levels, path-averaged rainfall intensities can be estimated, and then interpolated to produce high-resolution rainfall maps. In this study, we delve into the opportunities and constraints that arise when using these rainfall maps as only input source of rainfall information in a nowcasting algorithm. Our aim is to emulate an operational setting and as such assess the feasibility and give insights into where, when and how CML-based rainfall estimates can be used for nowcasting in Sri Lanka. We use 12 months of data from 2019 and 2020 from a Sri Lankan CML network that predominantly covers the northern half of the country, we create spatial rainfall fields at 15-minute intervals. Using the nowcasting algorithm pySTEPS, probabilistic nowcasts are created for leadtimes up to three hours for events with different durations ranging from 1 to 24 hours. The nowcasts (QPF) are evaluated against the CML rainfall fields (QPE) at the catchment scale. The performance of the nowcast is analyzed with regards to the catchment size, and the varying CML coverage and density per catchment. The results are further analyzed by season to determine the potential influence of rainfall intensity and dominant wind direction on the nowcasts accuracy. Hourly rain gauges, where available, are used as an independent (point) reference source of rainfall information. With this novel application of CML-derived rainfall fields, essentially providing a ‘weather radar’ in the tropics, we identify the major sources of uncertainty in the nowcasts and highlight the potential impact of relying solely on CMLs for operational early warning services in regions that lack dedicated rainfall sensors. Page 44 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Relationship between Precipitable Water Vapor and heavy rainfall over Lombardy region in Northern Italy using GNSS and CML sensors network Authors: Christina Oikonomou1; Roberto Nebuloni2; Giovanna Venuti3; Xiangyang Song3; Haris Haralambous4; Eugenio Realini5 1CLOUDWATER LTD & Frederick Research Center, Nicosia, Cyprus 2IEIIT, Consiglio Nazionale delle Ricerche, Milan, Italy 3Politecnico di Milano - Department of Civil and Environmental Engineering, Milan, Italy 4Frederick Research Center & Frederick University, School of Engineering, Nicosia, Cyprus 5Geomatics Research & Development (GReD) srl, Lomazzo, Italy Corresponding Authors: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] Nowcasting and understanding of locally evolving severe weather events is a demanding task that requires the combined investigation of different type (both groundand space-based) of datasets. Atmospheric water vapor (WV) which is the most abundant greenhouse gas (accounting for ~70% of global warming) comprises a significant energy source which generates severe weather and climate phenomena. GNSS (Global Navigation Satellite System) WV has been proved a valuable data source for high-resolution limited area Numerical Weather Prediction (NWP) models. The rapid spatiotemporal variations of WV in the low atmosphere poses one of the main challenges to NWP models forecasting accuracy. Abrupt increase of WV several hours before extreme rainfall has been temporally correlated with rainfall in various studies, followed by a decrease after the event. Other studies have investigated the joint effect of GNSS-WV and atmospheric pressure on extreme rainfall. Though many studies have evidenced ongoing accumulation of WV before the heavy rainfall, there is still a great difficult to determine a tight relationship between rainfall and WV, that could be reproduced by a plain, physically motivated two-layer nowcasting model. Lately, Commercial Microwave Links (CML), globally used in cellular telecommunication networks of base stations, are exploited as opportunistic sensors to estimate the average rainfall intensity along the radio path and to reconstruct rainfall maps over a region. Rainfall measured by the CML network has a vast application prospect in both densely populated and remote mountainous regions. Over tropical regions, such as Sri Lanka, the spatial comparison of CMLs with the high-quality satellite product GPM (global precipitation measurement) and with conventional rain gauge data confirmed the potential of CMLs to provide detailed monitoring of heavy rainfall events. The advantage of both the GNSS and CML opportunistic sensors networks is their high spatial and temporal resolutions. In this context, the present study attempts a first comparison of GNSS tropospheric products (Precipitable Water Vapor) with the respective CML-derived rainfall measurements with the ultimate aim to investigate the possible correlation between WV and heavy rainfall, during selected extreme precipitation events occurring at the period June 2019 –June 2020 over the Lombardy region in Northern Italy. To achieve this, we will exploit CML network, owned by Vodafone Italia S.p.A., groundbased GNSS receivers network owned by GReD srl, as well as meteorological observations available through the Lombardy-based Advanced Meteorological Predictions and Observations (LAMPO) project. Page 45 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Evaluation of the Dutch real-time radar precipitation product Author: Aart Overeem1 Co-authors: Hidde Leijnse 1; Bastiaan Anker 1; Mats Veldhuizen 1; Jouke Jacobi 1; Tim den Dulk 1; Tim Vlemmix 1; Rik Noorlandt 1 1Royal Netherlands Meteorological Institute Corresponding Authors: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], ov[email protected] The Royal Netherlands Meteorological Institute (KNMI) produces a publicly available real-time gaugeadjusted radar precipitation product of 5-min accumulations on a 1-km grid covering 450000 squared kilometres of northwestern Europe every 5 min. It is employed for various applications, including nowcasting. The quality of the product has been much improved since 31 January 2023. The employed radar and rain gauge dataset are described, as well as the applied algorithms, such as (polarimetric) fuzzy logic clutter removal and the gauge adjustment method. The performance of the product to estimate precipitation is assessed for the last year of the old and the first year of the renewed radar product. Non-meteorological echoes are much less of an issue for the renewed product. Moreover, comparison against independent rain gauge accumulations shows that underestimation decreases by about ten percentage points to 15% for the land surface of the Netherlands. Extremes are better captured. A spatial evaluation over the entire domain generally reveals improvements in precipitation estimates. Current and upcoming developments are presented. Here, the processing chain of the product and the product quality without employing opportunistic sensing data are explained. The evaluation also shows the limitations of the product. The product could be improved by employing crowdsourced rain gauge data, which is the topic of another presentation, that does not provide all the details on the processing chain. The radar dataset is available at https://doi.org/10.21944/5c23-p429 (real-time) & https://doi.org/10.21944/e7zx8a17 (archive). Page 46 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Can we estimate the amount of rain water during disastrously large flood events with high resolution? Author: Anna Jurczyk1 Co-authors: Jan Szturc 1; Katarzyna Ośródka 1; Agnieszka Kurcz 1; Magdalena Szaton 1; Mariusz Figurski 1; Robert Pyrc 1 1Institute of Meteorology and Water Management - National Research Institute Corresponding Author: [email protected] In September 2024, a very heavy and severe flood took place in the Upper and Middle Oder River basin - a mountainous area in southwestern Poland. The widespread rainfall lasted for about four days, reaching daily totals over 200 mm in some areas. Due to the necessity for real-time precipitation and runoff forecasts and subsequent analyses, an important issue is how precisely we can measure and estimate precipitation with high temporal and spatial resolution over an orographically diverse area. To answer this question, different measurement techniques were analysed: from rain gauges, weather radar-based, satellite-based, CML-based (non-conventional, currently tested at IMGW for their usefulness in real-time operational applications), and mesoscale numerical model simulations. Both data available in real and near real time, as well as reanalyses available later, were analysed. Various reanalyses based on satellite data (IMERG Final, PDIR-Now) and mesoscale simulations of ERA5 and WRF models were also examined. Data from manual rain gauges (for daily totals) and multi-source estimates (for hourly totals) were used as a reference to evaluate the results. On this basis, the reliability of various techniques for measuring and estimating precipitation was examined. Page 47 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Commercial Microwave Link research at the Climate and Earth Lab (Ghent University) Author: Kwinten Van Weverberg1 1Ghent University Corresponding Author: kwinten.vanwev[email protected] This poster presents an overview of our ongoing and future research at the Climate and Earth Lab (CLEAR) of Ghent University, where we leverage commercial microwave links (CML) to enhance precipitation monitoring. In Belgium, our efforts focus on the hilly terrain of southern regions, where we aim to improve existing radar–rain gauge merged products by integrating CML data. Using disdrometers, we are recalibrating the relationship between signal attenuation and rainfall intensity through detailed analyses of summer case studies. In the upcoming year, we will extend our investigations to winter conditions, exploring the synergistic use of CML and radar to identify surface frozen precipitation, in line with the approach proposed by Oydvin et al. (2024). Additionally, our research extends to Equatorial Africa, with a particular emphasis on Rwanda. This region is characterized by large spatiotemporal variability in rainfall and a lack of radar observations, factors that complicate early-warning systems for flash floods and landslides and complicate the evaluation of high-resolution weather and climate models. Through the recently started Sensor² project (Supporting Early-warning systems and Nature-based Solutions using Opportunistic Rainfall monitoring in Rwanda), we will seek to enhance rainfall monitoring by integrating data from microwave links, automatic rain gauges, and satellite observations. Complementing this effort, the installation of three disdrometers in Rwanda will enable more accurate calibration of rainfall intensities, with a focus on tropical precipitation regimes. Overall, our research demonstrates the potential of commercial microwave links as a complementary observational tool, promising advancements in precipitation estimation and early-warning capabilities across diverse climatic regions. Page 48 OpenSense 1st International Conference, Offenbach, June 25-26, 2025. / Book of Abstracts Added value of Personal Weather Stations for Precipitation Estimates in the Lazio Region, Italy Authors: Jochen Seidel1; Benedetta Moccia2; Elena Ridolfi2; Damaris Zulkarnaen1; Louise Petersson Wårdh3; Francesco Napolitano2; Fabio Russo2; András Bárdossy4 1Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Germany 2Dipartimento di Ingegneria Civile, Edile e Ambientale,Sapienza University of Rome, Italy 31) Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, Norrköping SE-601 76, Sweden 2) Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden 4Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Germany Corresponding Authors: [email protected], [email protected], bene[email protected] In the Lazio region, there is a dense network of 230 trustworthy rain gauges with a high temporal resolution. In addition, data from more than 300 Netatmo Personal Weather Stations (PWS) are available. However, since these PWS do not meet professional standards in terms of installation and maintenance, they must first be quality controlled (QC). For this purpose, we will apply the latest QC filters and bias corrections developed in the Opensense COST Action. After the QC, the quality of the PWS data will be evaluated by comparing them with co-located professional rain gauges. To assess the added value of PWS in capturing the spatial variability of rainfall (extremes) and on precipitation interpolation, the PWS will be included in the interpolation process. For this, a copulabased approach will be compared with conventional interpolation methods to highlight the added value of PWS in the interpolation. Page 49