IPC 15 full program
Poster Session
June 15-16, 2026
New: A new late-break poster session is open for submissions until June 5th. It will focus on student presentations, Early-career scientists, and dataset descriptions.
Title: Understanding structural relationships in precipitation trend ensembles Authors: Y. Markonis (Czech University of Life Sciences Prague), M. R. Vargas Godoy (Charles University), J. Thomson (Czech University of Life Sciences Prague), S. M. Papalexiou (Hamburg University of Technology) Corresponding author: markonis@fzp.czu.cz Abstract:Global precipitation trend assessment is challenged by the coexistence of many gauge based, satellite based, reanalysis based, and hybrid products that differ in long term trend sign, magnitude, significance, spatial extent, and regional coherence, while often sharing substantial ancestry in input data, correction pathways, and algorithmic design. Here we develop a data-driven framework to understand structural relationships within precipitation trend ensembles using 14 global precipitation datasets over land at annual resolution during 2000 to 2025. We apply a data-driven, trend topology framework to classify the roles of datasets within the ensemble, and combine complexity aware diagnostics of agreement and disagreement, including sign concurrence, significance concurrence, and rank structure, with genealogy-based information to distinguish genuine observational support from similarity arising among closely related products. Self-Organizing Maps reveal emergent families of datasets and regions with similar trend behaviour, while Random Forests identify the features that best explain dataset roles and uncertainty structure. The framework helps bridge observations and models by moving beyond single dataset benchmarking toward a more structured interpretation of ensemble behaviour. Muhammad Usman Liaqat1, Stefania Camici1,Francesco Leopardi12, Jaime Gaona1,Luca Brocca1 1Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy. 2Department of Civil and Environmental Engineering, University of Perugia, Italy. Correspondence: muhammadusman.liaqat@cnr.it Satellite observations from the GRACE mission and GRACE-FO have significantly advanced the monitoring of terrestrial water storage (TWS) at regional to global scales. However, their coarse spatial and temporal resolution limits their applicability for representing precipitation variability required in hydrological applications. Emerging next-generation gravity mission concepts, including NGGM and MAGIC, aim to provide enhanced spatio-temporal observations of mass change, offering new opportunities for improving precipitation estimation and its use in hydrological modelling. The primary objective of this work to access how improving the spatial and temporal resolution of future gravity missions impacts precipitation estimation by developing a number of global synthetic experiments. The precipitation data used as forcing of ESM will be compared with the “true” precipitation for testing the reliability of the SM2RAIN approach (Brocca et al., 2014) using as input EWH data (in the past it was implemented by using surface soil moisture data). A suite of global synthetic experiments is conducted to evaluate multiple gravity mission configurations (GRACE-C-like, NGGM, and MAGIC), including both filtered and unfiltered scenarios, to assess the impact of improved spatial and temporal resolution on precipitation estimation. The global correlation analysis shows median and mean correlation coefficients of 0.67 and 0.63, respectively, indicating satisfactory performance of the EWH based SM2RAIN framework across most terrestrial regions. Stronger correlations are observed over Northern Hemisphere mid-latitudes, including Europe, northern Asia, and North America, reflecting robust performance in temperate climates, while reduced performance is evident in several tropical regions such as central Africa, parts of the Amazon Basin, and Southeast Asia. Ongoing work also includes validation against independent observational datasets, the results of which will be presented. The results of the study clearly highlight the added value of next generation gravity missions such as runoff simulation, soil moisture dynamics, and the monitoring of extreme events including floods and droughts. The proposed framework contributes to emerging high resolution hydrological modelling efforts and supports next generation earth system initiatives led by the European Commission (Destination Earth), ESA (Digital Twin Earth), and NASA (Earth System Digital Twin), aiming to improve early warning systems and water resource management under climate change. References Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., & Levizzani, V. (2014). Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research: Atmospheres, 119(9), 5128–5141.
Title: High‑Resolution WRF–LISFLOOD Modelling of the July 2021 Luxembourg Flood Event Using GNSS‑Enhanced Atmospheric Data Authors: Haseeb ur Rehman (University of Luxembourg), Félicia Norma Rebecca TEFERLE (University of Luxembourg), Guy Schumann (RSS Hydro SARL), Jens Wickert (GFZ Potsdam), Florian Zus (GFZ Potsdam) Corresponding author: haseeb.rehman@uni.lu Abstract: Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. In this study we aim to develop, a high-resolution numerical weather prediction (NWP) model for effective local heavy rainfall prediction in a nowcasting scenario and provide real-time for flood simulation. The modeling relies on the Weather Research and Forecasting (WRF) model, which incorporates local Global Navigation Satellite System (GNSS) zenith total delays and precipitation observations to simulate small-scale, high-intensity convective precipitation. As part of this, we will also test run the LISFlood flood model in an operational inundation forecast mode, meaning that the flood model will be run with the WRF precipitation forecasts as inputs. The WRF model was configured for the Greater Region and Luxembourg using a three‑nested domain setup with horizontal grid resolutions of 12 km, 4 km, and 1.3 km, incorporating high‑resolution static datasets. Meteorological forcing for the period 20 June–20 July 2021 was obtained from the Global Forecast System (GFS) and the ERA5 reanalysis (ECMWF), which were used as initial and boundary conditions. Zenith Total Delay (ZTD) observations from 245 GNSS stations across greater region were assimilated into the model, together with additional observational datasets including Surface Synoptic Observations (SYNOP), upper‑air measurements, radiosonde profiles (TEMP), and Tropospheric Airborne Meteorological Data Reporting (TAMDAR). Following data assimilation, a sensitivity analysis of key meteorological variables—such as precipitation and 2‑m air temperature (T2)—was conducted. The WRF output from the innermost domain (NWPLux, 1.3 km resolution) was subsequently used as rainfall input for LISFLOOD‑FP to simulate the flood event. For comparison, simulations were also performed using ECMWF rainfall. All flood simulations were run over a 72‑hour period at 6-hr interval, from 13 July 2021 00:00 to 16 July 2021 00:00.
Title: Social perception of heavy rainfall and Coastal El Niño events in Peru through YouTube comments (2017–2026) Authors: Daphne Lisbeth Arenas Yance (Universidad Nacional Agraria La Molina - UNALM) Corresponding author: dalhis09@gmail.com Abstract: Peru exhibits a high climatic diversity, with 38 recognized climate types that influence the spatial distribution of rainfall across the country. In recent years, intense rainfall events along the northern coast have frequently been associated with Coastal El Niño episodes, characterized by sea surface temperature warming in the Niño 1+2 region rather than in the central Pacific. These events gained particular attention after the severe impacts of the 2017 Coastal El Niño and the recent alerts issued in 2026. Understanding how society perceives these phenomena is essential for improving climate risk communication. This study analyzes public perception of extreme rainfall and Coastal El Niño events in Peru using YouTube comments from 2017 to 2026. Data were collected from the first 100 videos and up to 500 comments per video for three keyword searches: “Heavy rainfall Peru,” “Coastal El Niño Peru,” and “Heavy rainfall Coastal El Niño Peru.” After removing duplicate comments, a total of 2,517 comments were analyzed using the pysentimiento library to identify sentiment, emotions, and potential hate speech. Results reveal a predominance of negative sentiment (61.4%), followed by neutral (28.6%) and positive comments. Comment activity increased notably during January and February 2026, coinciding with official Coastal El Niño alerts, although peaks were also observed in 2023 during another Coastal El Niño episode. Emotional analysis shows the presence of anger (28.7%) and sadness (11.7%). The most frequent words in the comments include “God”, “El Niño”, “ people”, “community”, “authorities,” and “rain.” News channels and the national meteorological service appear as the most referenced sources of information. Hate speech remained relatively limited, representing 10.5% of the comments. These findings highlight the importance of incorporating social perception into climate communication strategies and strengthening clear and accessible dissemination of scientific information to reduce misinformation and public anxiety during extreme rainfall events.Authors: Haseeb ur Rehman (University of Luxembourg), Félicia Norma Rebecca TEFERLE (University of Luxembourg), Guy Schumann (RSS Hydro SARL), Jens Wickert (GFZ Potsdam), Florian Zus (GFZ Potsdam) Corresponding author: haseeb.rehman@uni.lu Abstract: Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. In this study we aim to develop, a high-resolution numerical weather prediction (NWP) model for effective local heavy rainfall prediction in a nowcasting scenario and provide real-time for flood simulation. The modeling relies on the Weather Research and Forecasting (WRF) model, which incorporates local Global Navigation Satellite System (GNSS) zenith total delays and precipitation observations to simulate small-scale, high-intensity convective precipitation. As part of this, we will also test run the LISFlood flood model in an operational inundation forecast mode, meaning that the flood model will be run with the WRF precipitation forecasts as inputs. The WRF model was configured for the Greater Region and Luxembourg using a three‑nested domain setup with horizontal grid resolutions of 12 km, 4 km, and 1.3 km, incorporating high‑resolution static datasets. Meteorological forcing for the period 20 June–20 July 2021 was obtained from the Global Forecast System (GFS) and the ERA5 reanalysis (ECMWF), which were used as initial and boundary conditions. Zenith Total Delay (ZTD) observations from 245 GNSS stations across greater region were assimilated into the model, together with additional observational datasets including Surface Synoptic Observations (SYNOP), upper‑air measurements, radiosonde profiles (TEMP), and Tropospheric Airborne Meteorological Data Reporting (TAMDAR). Following data assimilation, a sensitivity analysis of key meteorological variables—such as precipitation and 2‑m air temperature (T2)—was conducted. The WRF output from the innermost domain (NWPLux, 1.3 km resolution) was subsequently used as rainfall input for LISFLOOD‑FP to simulate the flood event. For comparison, simulations were also performed using ECMWF rainfall. All flood simulations were run over a 72‑hour period at 6-hr interval, from 13 July 2021 00:00 to 16 July 2021 00:00.
Title: Bringing GPM to the Classroom: An Educational Program for Teachers on Precipitation Measurement and Climate Awareness Authors: Lisa Milani (University of Maryland, College Park, Maryland, USA and NASA Goddard Space Flight Center, Greenbelt, Maryland, USA), Vasco Mantas (University of Coimbra), Andrea Portier (Science Systems and Applications Inc., Lanham, Maryland, USA and NASA Goddard Space Flight Center, Greenbelt, Maryland, USA), Joana Aldino (UC), Margarida Anastácio (UC), Rui Andrade, Laura Besco, Ennio Cantoresi, Giulia Ciantra, Valeria Cuccato, Laura Insogna, Manuela Interlandi, Ilaria Piccioni, SIlvia Raisi, Elisabetta Ricci, Valeria Romani, Michela Zanella Corresponding author: lmilani@umd.edu Abstract: NASA's Global Precipitation Measurement (GPM) Mission Mentorship Program includes an educational initiative targeting teachers and educators. Through this spin-off program, GPM specialists collaborate with educators to explore the water cycle, climate change, and precipitation within the context of the GPM mission. The primary objective is to equip teachers with information and resources that enable them to effectively transmit scientific knowledge to their students. Following three instructional sessions covering the water cycle, weather and climate, and the GPM mission, teachers work alongside GPM specialists to create hands-on projects for students. The initiative is designed to fit within local educational and institutional contexts by conforming to national educational standards, meeting teacher professional development needs, and incorporating subjects of regional and professional relevance. The initiative employs an interdisciplinary approach centered on precipitation study, ranging from direct measurement through rain gauges installed on school grounds to analytical work comparing field measurements with GPM satellite data. Student classroom dialogues about precipitation patterns and variability foster climate change awareness and connect this initiative to broader school-based sustainability programs. This paper outlines the program structure, summarizes the teacher-led practical projects, and provides links to classroom-ready resources available in Italian, Portuguese and English. The program continues to evolve, with plans to expand internationally, making educational materials accessible in multiple languages to minimize language barriers and enhance younger generations' access to Earth observation data.
Title: Estimation of fresh snow density from two optical disdrometers located at two different altitudes Authors: Brice Boudevillain (Université Grenoble Alpes), Victoire Nzanzu (Université Grenoble Alpes), Frédéric Cazenave (IRD), Ludovic Bouilloud (Météo-France) Corresponding author: brice.boudevillain@univ-grenoble-alpes.fr Abstract: The density of fresh snow is essential information for several applications, such as quantitative precipitation estimation and snowpack modelling. The density of fresh snowflakes can be estimated manually on an ad hoc basis immediately after a snowfall using a board and scales, following a rigorous protocol. Estimates can also be made continuously at local level, for example using a combination of instruments that measure both snow depth and melted water equivalent (heated tipping bucket or weighing rain gauge) in the field. However, this type of method is limited by the rapid settling of snow on the ground, which changes its density. Other methods use video-disdrometers or attempt to use radar remote sensing combined with in situ instruments to estimate snow density. This study proposes to estimate snow density just before the hydrometeores fall at the ground using two optical disdrometers located at two different altitudes but close enough to be subject to the same precipitation system. One of the two disdrometers must be located at the bottom in the liquid part, while the other, located at the top, may be in a snowy, sleety or rainy part of the precipitation system. The method assumes a constant flow of water between the high and low points (no evaporation) and a good horizontal homogeneity of precipitation between the instruments. These assumptions are frequently encountered during winter. The method uses an original observation network in which three optical disdrometers are located less than 10 km apart at altitudes of 220 m, 980 m and 1650 m. The first disdrometer, located in the valley, is co-located with an X-band polarimetric radar and a micro rain radar (MRR, K-band vertical Doppler pointer), which enables critical analysis of the results. For example, the Doppler spectrum of hydrometeor fall velocity observed with the MRR makes it possible to distinguish between rain and snow, and even between different types of snow. Such as distinctions maybe also be performed thanks to fuzzy logic methods using polarimetric X-band radar data. Finally, a few manual and spot measurements of fresh snow density in situ are also available to validate the estimates. The poster will present the method used, the observation system and initial results with the aim of demonstrating the potential and limitations of this method, as well as its value in refining methods for identifying different snow hydrometeors using radar remote sensing.
Title: Uncertainty Characterization of PMW Precipitation Regime Classification Authors: Veljko Petkovic (University of Maryland), Lei Ji (University of Maryland), Malarvizhi Arulraj (University of Maryland), Ralph Ferraro (University of Maryland), Huan Meng (NOAA/NESDIS/STAR) Corresponding author: veljko.petkovic@umd.edu Abstract: Convective precipitation is essential for understanding Earth's hydrological cycle, vertical thermodynamic transport, and natural hazards. Satellite passive microwave (PMW) retrievals provide the broad spatial coverage essential for global precipitation monitoring and serve as critical inputs for data assimilation and model validation. However, uncertainties in PMW-derived precipitation regime classification remain poorly quantified across varying environmental conditions, limiting their utility for model–observation integration. Using GMI–KuPR collocated observations (2015–2023), we derive environment-specific uncertainty estimates from the analysis of convective volume fraction (CVF) bias distributions across environmental conditions. Results reveal distinct, environment-dependent CVF bias patterns: GMI generally underestimates CVF over oceans (except under high moisture conditions or elevated surface temperatures) while systematically overestimating over land (except in cool, dry environments). Uncertainties peak in tropical deep convective regions and within an oceanic "transition zone" characterized by mixed precipitation regimes. CVF biases correlate strongly with ice-phase hydrometeor signatures at 89 and 166 GHz. Temporal stability analysis across two independent four-year periods confirms these patterns are robust. The resulting environment-dependent uncertainty estimates provide a quantitative framework for PMW algorithm refinement, observational error specification in precipitation data assimilation, and uncertainty propagation in downstream modeling applications.
Title: Bridging Passive Microwave Observations and Vertical Cloud Signatures through Semi-Supervised Learning Authors: Malarvizhi Arulraj (Earth System Science Interdisciplinary Center/Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, Maryland, USA), Veljko Petkovic (University fo Maryland), Shruti Upadhyaya (Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India), Huan Meng (Center for Satellite Applications and Research, NESDIS/NOAA, College Park, Maryland, USA), Ralph R. Ferraro (Earth System Science Interdisciplinary Center/Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, Maryland, USA) Corresponding author: marulraj@umd.edu Abstract: Most spaceborne precipitation-observing sensors provide only implicit information about vertical hydrometeor structure. As a result, accurately characterizing cloud and precipitation systems from satellite observations, particularly from passive microwave sensors remains a challenge. This study presents a semi-supervised learning framework to classify passive microwave (PMW) observations using cloud-type information derived from CloudSat’s Cloud Profiling Radar (CPR) and reflectivity profile characteristics from the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) mission. The objective of this work is to supplement passive microwave brightness temperature observations with physically informed vertical profile information derived from radar measurements. We leverage NASA Level-2B GPM–CloudSat collocation product to construct labeled training dataset. A key challenge is the limited collocation among the CPR, DPR, and GMI, along with the large volume of unlabeled GMI observations. To address this, a semi-supervised learning approach is implemented to effectively utilize both labeled and unlabeled data, to provide a robust classification across diverse precipitation regimes. The framework categorizes PMW observations into structurally meaningful classes, including cloud types, multilayered cloud configurations, and low-level enhancement signatures, thereby bridging the gap between radar observations and passive microwave measurements. The proposed framework achieves an overall F1-score of 80% in identifying associated vertical regimes. This approach provides a pathway toward physically interpretable satellite retrieval improvements, enhanced error characterization, and more accurate representation of precipitation structure in passive microwave–based products.
Title: Evolution of the vertical profile of reflectivity beneath the radar beam in complex terrain using X-band and K-band MRR observations Authors: Victoire NZANZU MWAMBALA (Université Grenoble Alpes (UGA)), Brice Boudevillain (Université Grenoble Alpes (UGA)), Ludovic Bouilloud (Centre de Météorologie Radar, Météo-France), Frédéric Cazenave (Institut de recherche pour le développement (IRD)), Dominique Faure (Centre de Météorologie Radar, Météo-France), Nan Yu Corresponding author: victoire.nzanzu-mwambala@univ-grenoble-alpes.fr Abstract: Vertical profiles of reflectivity (VPRs) and quasi-vertical profiles of reflectivity (QVPs) are widely used to characterize the vertical structure of precipitation and to mitigate bright-band effects in radar-based quantitative precipitation estimation (QPE). However, their use remains challenging in mountainous regions due to the strong spatial variability of precipitation and the difficulty of retrieving the lower part of the vertical profile beneath the beam of operational weather radars installed at high altitude, as is the case in this study. Microphysical processes occurring beneath the radar beam, such as evaporation or precipitation enhancement, may substantially modify reflectivity before hydrometeors reach the ground. A key question is therefore how vertically pointing profilers and scanning weather radars can be combined to better document the evolution of reflectivity beneath the operational radar beam. The ultimate objective of this study is to provide a climatological analysis of the VPR below the beam of a high-altitude operational radar using long-term Micro Rain Radar (MRR) observations. This analysis aims to improve the understanding of reflectivity evolution both below the radar beam and below the melting layer, which is frequently intersected by the beam when the radar is installed at high elevation. A climatological analysis of recurrent precipitation events will be performed to quantify the frequency and magnitude of VPR-related biases and to evaluate their impact on surface rainfall estimates. The final goal is to develop a VPR/QVP-based correction strategy adapted to complex terrain and applicable to operational X-band radar processing without requiring permanent vertical profiling. This poster presents an initial analysis based on observations collected in the Grenoble valley (220 m a.s.l.) from an X-band polarimetric radar (XPORT) which has been operational during certain events and a collocated K-band (MRR) which has been operational almost continuously for 8 years. The dataset also includes measurements from an operational X-band radar of the ARAMIS network located approximately 10 km away at 1900 m a.s.l. on the Moucherotte mountain. This operational radar constitutes the primary target for reconstructing the VPR beneath its beam down to the surface using observations from the valley-based instruments. In this context, the co-located XPORT radar and MRR profiler are used to help characterize the local vertical structure of precipitation and provide a reference to supplement missing VPR information beneath the operational radar beam. QVPs are constructed from PPI scans at 7.5°, 9°, and 25° by removing near-range noise and ground clutter and projecting the observations onto a regular vertical grid. These profiles are compared with temporally averaged MRR reflectivity profiles during stratiform precipitation events in order to assess inter-instrument consistency and the assumption of spatial homogeneity underlying VPR-based corrections, despite the 10 km separation between the valley instruments and the operational radar. Preliminary results from several stratiform rain cases reveal systematic differences of 2–3 dB between XPORT-derived QVPs and MRR profiles in the lowest layers, with a reversal of the bias in the vicinity of the melting layer. This behavior suggests a significant contribution from non-Rayleigh scattering and differential attenuation at K-band frequency. Attenuation along the radar beam path therefore should be estimated and corrected using state-of-the-art methods prior to further analysis.
Title: An evaluation of sub-daily precipitation simulations from a new high-resolution regional atmospheric reanalysis ALADIN Authors: Vojtech Bliznak (Institute of Atmospheric Physics CAS, Department of Meteorology, Prague, Czech Republic), Petr Zacharov (Institute of Atmospheric Physics CAS, Department of Meteorology, Prague, Czech Republic), Robert Kvak (Institute of Atmospheric Physics CAS, Department of Meteorology, Prague, Czech Republic), Aart Overeem (R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, Netherlands) Corresponding author: bliznak@ufa.cas.cz Abstract: Atmospheric reanalysis is a powerful tool to obtain three-dimensional information about the historical state of the atmosphere by combining observations and numerical weather prediction (NWP) modelling using different data assimilation schemes. Recently, several high-resolution regional atmospheric reanalyses have been produced for the European region. However, most of them are calculated using NWP models, where convective processes are parameterized, which leads to a major source of errors and uncertainties. For this reason, limited-area convection-permitting models have been used to reproduce high-resolution regional atmospheric reanalysis. One of them is a new ALADIN reanalysis providing a coherent description of the state of the atmosphere and near-surface layers at high horizontal (2.3 km) and temporal (1 hour) resolutions, covering a large part of Europe from 1989 to the present. This contribution evaluates the performance of the ALADIN reanalysis to simulate sub-daily precipitation totals in the Czech Republic, Germany and, the Netherlands during 18 warm seasons (April–October) from 2002 to 2019. The evaluation is performed separately for two numerical weather prediction (NWP) model runs. The first, ALADIN/Reanalysis, includes the full assimilation of the observed data every 6 h using a digital upper-air filter, which combines the high-resolution ALADIN guess with ERA5, the result of a global 4D variational analysis. The second, ALADIN/Evaluation Run, is also driven by ERA5 but uses no surface data assimilation. NWP model outputs are verified against gauge-adjusted radar precipitation datasets. Results show that both configurations systematically overestimate seasonal precipitation totals by approximately 20–23%, mainly due to an excess frequency of very low hourly precipitation amounts (
Title: Urban influence on precipitation in the Paris region: a spatio-temporal analysis of rainfall systems from weather radar Authors: Darellis Nathan (LATMOS/IPSL, CNRS UVSQ Universite Paris Saclay, Guyancourt, France), Bastin Sophie (LATMOS/IPSL, CNRS UVSQ Universite Paris Saclay, Guyancourt, France), Barthès Laurent (Université de Versailles Saint-Quentin, UMR8190 – CNRS/INSU, LATMOS-IPSL, Guyancourt, France), Viltard Nicolas (LATMOS/IPSL, CNRS UVSQ Universite Paris Saclay, Guyancourt, France), Corresponding author: nathan.darellis@latmos.ipsl.fr Abstract: In an increasingly urbanized world, urban water management is becoming critical, as illustrated by the Île-de-France region where growing demand and seasonal variations put major pressure on water resources (Barles et al., 2021). Urban environments, with their impervious surfaces and anthropogenic heat, potentially alter precipitation patterns (Liu and Niyogi, 2019; Sui et al., 2024), although no scientific consensus yet exists on this phenomenon (Lalonde et al., 2023). Isolating this urban signal remains a major scientific challenge, as cities are embedded in complex environments shaped by orography, land cover heterogeneity, and coastal influences. This calls for city-specific investigations, as dominant mechanisms likely vary depending on local environmental characteristics (Lalonde et al., 2023). Therefore, understanding the specific dynamics of the Paris region is essential to isolate the urban signal on precipitation. This work aims to characterize the spatio-temporal variability of precipitation systems and explore their potential relationships with key environmental features — including land cover, rivers, forests and topography. Precipitation cells were detected, tracked and characterized over the period 2007–2023 using the Tobac algorithm (Heikenfeld et al., 2019) applied to 5-minute radar accumulation data from the Météo-France network. Initial results reveal enhanced precipitation intensity over the Paris metropolitan region compared to surrounding areas. This pattern is consistent with a potential urban influence on local rainfall, though the underlying forcing mechanisms remain to be fully attributed.
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