14 Mar 2025
| 14 Mar 2025
Flash-flood Alert System using Ensemble Radar Prediction and Rainfall-runoff Simulation
Frédéric G. Jordan
Hydrique Ingénieurs, Le Mont-sur-Lausanne, Switzerland
Clément Cosson
Hydrique Ingénieurs, Le Mont-sur-Lausanne, Switzerland
Marco Gabella
MeteoSuisse, Switzerland
Ioannis V. Sideris
MeteoSuisse, Switzerland
Adrien Liernur
MeteoSuisse, Switzerland
Environmental Remote Sensing Laboratory [LTE], École Polytechnique Fédérale de Lausanne [EPFL], Switzerland
Alexis Berne
Environmental Remote Sensing Laboratory [LTE], École Polytechnique Fédérale de Lausanne [EPFL], Switzerland
Urs Germann
MeteoSuisse, Switzerland
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Pierre Grzegorczyk, Lea Raillard, Valentin Wiener, Étienne Vignon, Christophe Genthon, and Alexis Berne
EGUsphere, https://doi.org/10.5194/egusphere-2026-4473, https://doi.org/10.5194/egusphere-2026-4473, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Evaluating cloud representation in Atmospheric General Circulation Models (AGCMs) is important for surface precipitation and the Earth radiative balance. The LORIS (LMDZ Offline Radar Instrument Simulator) is a new tool designed to evaluate clouds in LMDZ (the atmospheric component of the IPSL Coupled Model). LORIS offers flexible options for subgrid representation, scattering assumptions, and microphysics properties, enabling the evaluation and improvement of cloud and precipitation in LMDZ.
Romanos Foskinis, Anne-Claire Billault-Roux, Henrik Skov, Lise Lotte Sørensen, Sven-Erik Gryning, Athanasios Nenes, and Alexis Berne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-549, https://doi.org/10.5194/essd-2026-549, 2026
Preprint under review for ESSD
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We present a dataset collected with the WProf cloud radar during the CleanCloud Arctic Villum ExpeRiment campaign in northeast Greenland in spring 2024. It includes Doppler spectra, hydrometeor classification, and cloud observations that reveal the microphysical structure of Arctic clouds. The data show that multimodality, where different cloud particle populations coexist, is common and support studies of aerosol–cloud interactions and improvements in weather and climate models.
Ioannis Chaniotis, Paraskevi Georgakaki, Romanos Foskinis, Marilena Gidarakou, Maria Gini, Platon Patlakas, Konstantinos Eleftheriadis, Alexandros Papagiannis, Elissavet Bossioli, Nicole Clerx, Alexis Berne, Helena Flocas, and Athanasios Nenes
EGUsphere, https://doi.org/10.5194/egusphere-2026-4645, https://doi.org/10.5194/egusphere-2026-4645, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This study implements three secondary ice production mechanisms in the RAMS-ICLAMS model to better represent mixed-phase clouds. Validated against CHOPIN campaign data and for two characteristic alpine cloud formations, these mechanisms improve ice crystal concentrations, cloud structure, and reflectivity. However, opposing precipitation responses have been predicted, highlighting a non linear response, which aligns with independent previous findings.
A. V. Sreenath, Peter T. May, Steven Siems, Heather Corden, and Alexis Berne
EGUsphere, https://doi.org/10.5194/egusphere-2026-4159, https://doi.org/10.5194/egusphere-2026-4159, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We investigated how synoptic weather regimes control cloud growth and surface precipitation at Dumont d’Urville, East Antarctica. Multi-instrument observations show that synoptic forcing determines hydrometeor growth and precipitation formation aloft, while boundary-layer conditions, including local downslope flow and near-surface moisture, control how much precipitation reaches the surface.
Gionata Ghiggi, Kim Candolfi, Anne-Claire Billault-Roux, Régis Longchamp, Son Pham-Ba, Charlotte Weil, Remko Uijlenhoet, and Alexis Berne
Atmos. Meas. Tech., 19, 4943–4987, https://doi.org/10.5194/amt-19-4943-2026, https://doi.org/10.5194/amt-19-4943-2026, 2026
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Heather Corden, Julien Delanoë, Massimo Del Guasta, and Alexis Berne
EGUsphere, https://doi.org/10.5194/egusphere-2026-3964, https://doi.org/10.5194/egusphere-2026-3964, 2026
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In Antarctica, intrusions of warm, moist air from the coast to the high plateau are responsible for a significant proportion of the snow accumulating on the ice sheet each year. We present a case study of an intrusion in East Antarctica in February 2025. Meteorological radars along a 1100-km transect were used to investigate the processes leading to precipitation in the intrusion. The processes vary according to the changing temperature, humidity and wind dynamics between the coast and plateau.
Matteo Guidicelli, Alfonso Ferrone, Gionata Ghiggi, Marco Gabella, Urs Germann, and Alexis Berne
EGUsphere, https://doi.org/10.5194/egusphere-2026-594, https://doi.org/10.5194/egusphere-2026-594, 2026
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We developed a radar-based method to estimate the number and size of hailstones across Switzerland using multiple weather radars. The approach shows reasonable agreement with independent observations (automatic hailsensors, drone-based, and crowd-sourced) and provides high-resolution information on hail events. This method can support monitoring, forecasting, and assessing hail-related damages.
Andrea Francesco Battaglia and Marco Gabella
EGUsphere, https://doi.org/10.5194/egusphere-2026-2097, https://doi.org/10.5194/egusphere-2026-2097, 2026
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Weather radars must be checked regularly to ensure reliable measurements of atmospheric conditions. We used the Sun as a natural radio source to compare observations from European radars and radio astrophysical telescopes with models. These models are needed as the reference and radar frequencies differ. Using observations at both frequencies simultaneously, we developed a new model that better captures the varying flux of the Sun. This helps reduce calibration bias and its uncertainty.
Talia Rosin, Francesco Marra, Marco Gabella, Urs Germann, Daniel Wolfensberger, and Efrat Morin
EGUsphere, https://doi.org/10.5194/egusphere-2026-1800, https://doi.org/10.5194/egusphere-2026-1800, 2026
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Extreme rainfall in mountainous regions is difficult to assess as it varies strongly across space and time, and rain gauges are often too sparse to capture it. Using 9 years of radar data, we analyse summer rainfall extremes across Switzerland for different durations and areas. We show that rainfall intensity depends strongly on scale and topography, and that radar better captures local extremes, improving flood hazard assessment, as demonstrated for three recent major flood-producing storms.
Jenna Ritvanen, Martin Aregger, Dmitri Moisseev, Urs Germann, Alessandro Hering, and Seppo Pulkkinen
Atmos. Meas. Tech., 19, 1853–1874, https://doi.org/10.5194/amt-19-1853-2026, https://doi.org/10.5194/amt-19-1853-2026, 2026
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Convective storms pose several hazards, like heavy rainfall, but operational short-term forecasting (nowcasting) suffers from limited models of storm development. Cell tracking, commonly used for nowcasting of convective storms and analyzing storm evolution, is complicated by splits and merges. We show how splits and merges can be integrated into cell track analysis, using case studies and analysis of split and merge events with operational data from the Swiss weather radar network.
Kevin Ohneiser, Patric Seifert, Willi Schimmel, Fabian Senf, Tom Gaudek, Martin Radenz, Audrey Teisseire, Veronika Ettrichrätz, Teresa Vogl, Nina Maherndl, Nils Pfeifer, Jan Henneberger, Anna J. Miller, Nadja Omanovic, Christopher Fuchs, Huiying Zhang, Fabiola Ramelli, Robert Spirig, Anton Kötsche, Heike Kalesse-Los, Maximilian Maahn, Heather Corden, Alexis Berne, Majid Hajipour, Hannes Griesche, Julian Hofer, Ronny Engelmann, Annett Skupin, Albert Ansmann, and Holger Baars
Atmos. Chem. Phys., 25, 17363–17386, https://doi.org/10.5194/acp-25-17363-2025, https://doi.org/10.5194/acp-25-17363-2025, 2025
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Kunfeng Gao, Franziska Vogel, Romanos Foskinis, Stergios Vratolis, Maria I. Gini, Konstantinos Granakis, Anne-Claire Billault-Roux, Paraskevi Georgakaki, Olga Zografou, Prodromos Fetfatzis, Alexis Berne, Alexandros Papayannis, Konstantinos Eleftheridadis, Ottmar Möhler, and Athanasios Nenes
Atmos. Chem. Phys., 24, 9939–9974, https://doi.org/10.5194/acp-24-9939-2024, https://doi.org/10.5194/acp-24-9939-2024, 2024
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Ice nucleating particle (INP) concentrations are required for correct predictions of clouds and precipitation in a changing climate, but they are poorly constrained in climate models. We unravel source contributions to INPs in the eastern Mediterranean and find that biological particles are important, regardless of their origin. The parameterizations developed exhibit superior performance and enable models to consider biological-particle effects on INPs.
Jérôme Kopp, Alessandro Hering, Urs Germann, and Olivia Martius
Atmos. Meas. Tech., 17, 4529–4552, https://doi.org/10.5194/amt-17-4529-2024, https://doi.org/10.5194/amt-17-4529-2024, 2024
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We present a verification of two products based on weather radars to detect the presence of hail and estimate its size. Radar products are remote detection of hail, so they must be verified against ground-based observations. We use reports from users of the Swiss Weather Services phone app to do the verification. We found that the product estimating the presence of hail provides fair results but that it should be recalibrated and that estimating the hail size with radar is more challenging.
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris, Urs Germann, and Isztar Zawadzki
Nonlin. Processes Geophys., 31, 259–286, https://doi.org/10.5194/npg-31-259-2024, https://doi.org/10.5194/npg-31-259-2024, 2024
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We compared two ways of defining the phase space of low-dimensional attractors describing the evolution of radar precipitation fields. The first defines the phase space by the domain-scale statistics of precipitation fields, such as their mean, spatial and temporal correlations. The second uses principal component analysis to account for the spatial distribution of precipitation. To represent different climates, radar archives over the United States and the Swiss Alpine region were used.
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024, https://doi.org/10.5194/amt-17-2539-2024, 2024
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This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail on the ground to estimate hail size distributions (HSDs). Traditional hail sensors' limited areas complicate the full HSD retrieval. The HSD of a supercell event on 20 June 2021 is retrieved and contains > 18 000 hailstones. The HSD is compared to automatic hail sensor measurements and those of weather-radar-based MESHS. Investigations into ground hail melting are performed by five drone flights.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
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This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Sophie Erb, Elias Graf, Yanick Zeder, Simone Lionetti, Alexis Berne, Bernard Clot, Gian Lieberherr, Fiona Tummon, Pascal Wullschleger, and Benoît Crouzy
Atmos. Meas. Tech., 17, 441–451, https://doi.org/10.5194/amt-17-441-2024, https://doi.org/10.5194/amt-17-441-2024, 2024
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In this study, we focus on an automatic bioaerosol measurement instrument and investigate the impact of using its fluorescence measurement for pollen identification. The fluorescence signal is used together with a pair of images from the same instrument to identify single pollen grains via neural networks. We test whether considering fluorescence as a supplementary input improves the pollen identification performance by comparing three different neural networks.
Alfonso Ferrone, Étienne Vignon, Andrea Zonato, and Alexis Berne
The Cryosphere, 17, 4937–4956, https://doi.org/10.5194/tc-17-4937-2023, https://doi.org/10.5194/tc-17-4937-2023, 2023
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In austral summer 2019/2020, three K-band Doppler profilers were deployed across the Sør Rondane Mountains, south of the Belgian base Princess Elisabeth Antarctica. Their measurements, along with atmospheric simulations and reanalyses, have been used to study the spatial variability in precipitation over the region, as well as investigate the interaction between the complex terrain and the typical flow associated with precipitating systems.
Marco Gabella, Martin Lainer, Daniel Wolfensberger, and Jacopo Grazioli
Atmos. Meas. Tech., 16, 4409–4422, https://doi.org/10.5194/amt-16-4409-2023, https://doi.org/10.5194/amt-16-4409-2023, 2023
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A still wind turbine observed with a fixed-pointing radar antenna has shown distinctive polarimetric signatures: the correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The differential reflectivity and the radar reflectivity factors were also stable in time. Over 2 min (2000 Hz, 128 pulses were used; consequently, the sampling time was 64 ms), the standard deviation of the differential backscattering phase shift was only a few degrees.
Anne-Claire Billault-Roux, Paraskevi Georgakaki, Josué Gehring, Louis Jaffeux, Alfons Schwarzenboeck, Pierre Coutris, Athanasios Nenes, and Alexis Berne
Atmos. Chem. Phys., 23, 10207–10234, https://doi.org/10.5194/acp-23-10207-2023, https://doi.org/10.5194/acp-23-10207-2023, 2023
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Secondary ice production plays a key role in clouds and precipitation. In this study, we analyze radar measurements from a snowfall event in the Jura Mountains. Complex signatures are observed, which reveal that ice crystals were formed through various processes. An analysis of multi-sensor data suggests that distinct ice multiplication processes were taking place. Both the methods used and the insights gained through this case study contribute to a better understanding of snowfall microphysics.
Jérôme Kopp, Agostino Manzato, Alessandro Hering, Urs Germann, and Olivia Martius
Atmos. Meas. Tech., 16, 3487–3503, https://doi.org/10.5194/amt-16-3487-2023, https://doi.org/10.5194/amt-16-3487-2023, 2023
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We present the first study of extended field observations made by a network of 80 automatic hail sensors from Switzerland. The sensors record the exact timing of hailstone impacts, providing valuable information about the local duration of hailfall. We found that the majority of hailfalls lasts just a few minutes and that most hailstones, including the largest, fall during a first phase of high hailstone density, while a few remaining and smaller hailstones fall in a second low-density phase.
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
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This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
Matteo Guidicelli, Matthias Huss, Marco Gabella, and Nadine Salzmann
The Cryosphere, 17, 977–1002, https://doi.org/10.5194/tc-17-977-2023, https://doi.org/10.5194/tc-17-977-2023, 2023
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Spatio-temporal reconstruction of winter glacier mass balance is important for assessing long-term impacts of climate change. However, high-altitude regions significantly lack reliable observations, which is limiting the calibration of glaciological and hydrological models. We aim at improving knowledge on the spatio-temporal variations in winter glacier mass balance by exploring the combination of data from reanalyses and direct snow accumulation observations on glaciers with machine learning.
Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne
Atmos. Meas. Tech., 16, 911–940, https://doi.org/10.5194/amt-16-911-2023, https://doi.org/10.5194/amt-16-911-2023, 2023
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Better understanding and modeling snowfall properties and processes is relevant to many fields, ranging from weather forecasting to aircraft safety. Meteorological radars can be used to gain insights into the microphysics of snowfall. In this work, we propose a new method to retrieve snowfall properties from measurements of radars with different frequencies. It relies on an original deep-learning framework, which incorporates knowledge of the underlying physics, i.e., electromagnetic scattering.
Claudia Mignani, Lukas Zimmermann, Rigel Kivi, Alexis Berne, and Franz Conen
Atmos. Chem. Phys., 22, 13551–13568, https://doi.org/10.5194/acp-22-13551-2022, https://doi.org/10.5194/acp-22-13551-2022, 2022
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We determined over the course of 8 winter months the phase of clouds associated with snowfall in Northern Finland using radiosondes and observations of ice particle habits at ground level. We found that precipitating clouds were extending from near ground to at least 2.7 km altitude and approximately three-quarters of them were likely glaciated. Possible moisture sources and ice formation processes are discussed.
Étienne Vignon, Lea Raillard, Christophe Genthon, Massimo Del Guasta, Andrew J. Heymsfield, Jean-Baptiste Madeleine, and Alexis Berne
Atmos. Chem. Phys., 22, 12857–12872, https://doi.org/10.5194/acp-22-12857-2022, https://doi.org/10.5194/acp-22-12857-2022, 2022
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The near-surface atmosphere over the Antarctic Plateau is cold and pristine and resembles to a certain extent the high troposphere where cirrus clouds form. In this study, we use innovative humidity measurements at Concordia Station to study the formation of ice fogs at temperatures <−40°C. We provide observational evidence that ice fogs can form through the homogeneous freezing of solution aerosols, a common nucleation pathway for cirrus clouds.
Alfonso Ferrone, Anne-Claire Billault-Roux, and Alexis Berne
Atmos. Meas. Tech., 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, https://doi.org/10.5194/amt-15-3569-2022, 2022
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The Micro Rain Radar PRO (MRR-PRO) is a meteorological radar, with a relevant set of features for deployment in remote locations. We developed an algorithm, named ERUO, for the processing of its measurements of snowfall. The algorithm addresses typical issues of the raw spectral data, such as interference lines, but also improves the quality and sensitivity of the radar variables. ERUO has been evaluated over four different datasets collected in Antarctica and in the Swiss Jura.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022, https://doi.org/10.5194/gmd-15-4529-2022, 2022
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This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Jussi Leinonen, Ulrich Hamann, Urs Germann, and John R. Mecikalski
Nat. Hazards Earth Syst. Sci., 22, 577–597, https://doi.org/10.5194/nhess-22-577-2022, https://doi.org/10.5194/nhess-22-577-2022, 2022
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We evaluate the usefulness of different data sources and variables to the short-term prediction (
nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.
Paraskevi Georgakaki, Georgia Sotiropoulou, Étienne Vignon, Anne-Claire Billault-Roux, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys., 22, 1965–1988, https://doi.org/10.5194/acp-22-1965-2022, https://doi.org/10.5194/acp-22-1965-2022, 2022
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The modelling study focuses on the importance of ice multiplication processes in orographic mixed-phase clouds, which is one of the least understood cloud types in the climate system. We show that the consideration of ice seeding and secondary ice production through ice–ice collisional breakup is essential for correct predictions of precipitation in mountainous terrain, with important implications for radiation processes.
Monika Feldmann, Urs Germann, Marco Gabella, and Alexis Berne
Weather Clim. Dynam., 2, 1225–1244, https://doi.org/10.5194/wcd-2-1225-2021, https://doi.org/10.5194/wcd-2-1225-2021, 2021
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Mesocyclones are the rotating updraught of supercell thunderstorms that present a particularly hazardous subset of thunderstorms. A first-time characterisation of the spatiotemporal occurrence of mesocyclones in the Alpine region is presented, using 5 years of Swiss operational radar data. We investigate parallels to hailstorms, particularly the influence of large-scale flow, daily cycles and terrain. Improving understanding of mesocyclones is valuable for risk assessment and warning purposes.
Hélène Barras, Olivia Martius, Luca Nisi, Katharina Schroeer, Alessandro Hering, and Urs Germann
Weather Clim. Dynam., 2, 1167–1185, https://doi.org/10.5194/wcd-2-1167-2021, https://doi.org/10.5194/wcd-2-1167-2021, 2021
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In Switzerland hail may occur several days in a row. Such multi-day hail events may cause significant damage, and understanding and forecasting these events is important. Using reanalysis data we show that weather systems over Europe move slower before and during multi-day hail events compared to single hail days. Surface temperatures are typically warmer and the air more humid over Switzerland and winds are slower on multi-day hail clusters. These results may be used for hail forecasting.
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, https://doi.org/10.5194/amt-14-6851-2021, 2021
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Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.
