Articles | Volume 17, issue 22
https://doi.org/10.5194/amt-17-6707-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-6707-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Nov 2024
Research article |
| 26 Nov 2024
| 26 Nov 2024
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Vincent Forcadell
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Descartes Underwriting, Paris, France
Clotilde Augros
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Olivier Caumont
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Direction des opérations pour la prévision, Toulouse, France
Kévin Dedieu
Descartes Underwriting, Paris, France
Maxandre Ouradou
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Cloé David
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Jordi Figueras i Ventura
Météo-France, Direction des systèmes d'observation, Toulouse, France
Olivier Laurantin
Météo-France, Direction des systèmes d'observation, Toulouse, France
Hassan Al-Sakka
Leonardo Germany GmbH, Neuss, Germany
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Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 25, 429–449, https://doi.org/10.5194/nhess-25-429-2025, https://doi.org/10.5194/nhess-25-429-2025, 2025
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The use of numerical weather prediction models enables the forecasting of hazardous weather situations. The incorporation of new temperature and relative humidity observations from personal weather stations into the French limited-area model is evaluated in this study. This leads to the improvement of the associated near-surface variables of the model during the first hours of the forecast. Examples are provided for a sea breeze case during a heatwave and a fog episode.
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
EGUsphere, https://doi.org/10.5194/egusphere-2024-668, https://doi.org/10.5194/egusphere-2024-668, 2024
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This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024, https://doi.org/10.5194/nhess-24-907-2024, 2024
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Improvements in numerical weather prediction models make it possible to warn of hazardous weather situations. The incorporation of new observations from personal weather stations into the French limited-area model is evaluated. It leads to a significant improvement in the modelling of the surface pressure field up to 9 h ahead. Their incorporation improves the location and intensity of the heavy precipitation event that occurred in the South of France in September 2021.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Felix Erdmann, Olivier Caumont, and Eric Defer
Nat. Hazards Earth Syst. Sci., 23, 2821–2840, https://doi.org/10.5194/nhess-23-2821-2023, https://doi.org/10.5194/nhess-23-2821-2023, 2023
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This work develops a novel lightning data assimilation (LDA) technique to make use of Meteosat Third Generation (MTG) Lightning Imager (LI) data in a regional, convection-permitting numerical weather prediction model. The approach combines statistical Bayesian and 3-dimensional variational methods. Our LDA can promote missing convection and suppress spurious convection in the initial state of the model, and it has similar skill to the operational radar data assimilation for rainfall forecasts.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
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Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Pauline Combarnous, Felix Erdmann, Olivier Caumont, Éric Defer, and Maud Martet
Nat. Hazards Earth Syst. Sci., 22, 2943–2962, https://doi.org/10.5194/nhess-22-2943-2022, https://doi.org/10.5194/nhess-22-2943-2022, 2022
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The objective of this study is to prepare the assimilation of satellite lightning data in the French regional numerical weather prediction system. The assimilation of lightning data requires an observation operator, based on empirical relationships between the lightning observations and a set of proxies derived from the numerical weather prediction system variables. We fit machine learning regression models to our data to yield those relationships and to investigate the best proxy for lightning.
Marc Mandement and Olivier Caumont
Weather Clim. Dynam., 2, 795–818, https://doi.org/10.5194/wcd-2-795-2021, https://doi.org/10.5194/wcd-2-795-2021, 2021
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On 14–15 October 2018, in the Aude department (France), a heavy-precipitation event produced up to about 300 mm of rain in 11 h. Simulations carried out show that the former Hurricane Leslie, while involved, was not the first supplier of moisture over the entire event. The location of the highest rainfall was primarily driven by the location of a quasi-stationary front and secondarily by the location of precipitation bands downwind of mountains bordering the Mediterranean Sea.
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021, https://doi.org/10.5194/amt-14-4929-2021, 2021
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This paper presents work towards making retrievals on the liquid water content in fog and low clouds. Future retrievals will rely on a radar simulator and high-resolution forecast. In this work, real observations are used to assess the errors associated with the simulator and forecast. A selection method to reduce errors associated with the forecast is proposed. It is concluded that the distribution of errors matches the requirements for future retrievals.
Olivier Caumont, Marc Mandement, François Bouttier, Judith Eeckman, Cindy Lebeaupin Brossier, Alexane Lovat, Olivier Nuissier, and Olivier Laurantin
Nat. Hazards Earth Syst. Sci., 21, 1135–1157, https://doi.org/10.5194/nhess-21-1135-2021, https://doi.org/10.5194/nhess-21-1135-2021, 2021
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This study focuses on the heavy precipitation event of 14 and 15 October 2018, which caused deadly flash floods in the Aude basin in south-western France.
The case is studied from a meteorological point of view using various operational numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. The peculiarities of this case compared to other cases of Mediterranean heavy precipitation events are presented.
Nadia Fourrié, Mathieu Nuret, Pierre Brousseau, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 21, 463–480, https://doi.org/10.5194/nhess-21-463-2021, https://doi.org/10.5194/nhess-21-463-2021, 2021
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The assimilation impact of four observation data sets on forecasts is studied in a mesoscale weather model. The ground-based Global Navigation Satellite System (GNSS) zenithal total delay data set with information on humidity has the largest impact on analyses and forecasts, representing an evenly spread and frequent data set for each analysis time over the model domain. Moreover, the reprocessing of these data also improves the forecast quality, but this impact is not statistically significant.
Felix Erdmann, Eric Defer, Olivier Caumont, Richard J. Blakeslee, Stéphane Pédeboy, and Sylvain Coquillat
Atmos. Meas. Tech., 13, 853–875, https://doi.org/10.5194/amt-13-853-2020, https://doi.org/10.5194/amt-13-853-2020, 2020
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This article compares lightning observations from an optical sensor onboard the International Space Station to two ground-based networks using different radio frequencies. The location and timing of coincident flashes agree well for the three instruments. Differences exist for the detected number of flashes and the characteristics. Small flashes in particular are not always detected by all three instruments. About half of the flashes at altitudes below 10 km are not seen by the satellite sensor.
Marc Mandement and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 20, 299–322, https://doi.org/10.5194/nhess-20-299-2020, https://doi.org/10.5194/nhess-20-299-2020, 2020
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The number of connected personal weather stations has dramatically increased in the last years. These weather stations produce a high number of data that need a thorough quality control to unleash their potential. A novel quality-control algorithm now allows us to take full advantage of these data and observe thunderstorms with fine-scale details that cannot be caught by standard networks. These results pave the way for tremendous advances in both understanding and forecasting thunderstorms.
Tony Le Bastard, Olivier Caumont, Nicolas Gaussiat, and Fatima Karbou
Atmos. Meas. Tech., 12, 5669–5684, https://doi.org/10.5194/amt-12-5669-2019, https://doi.org/10.5194/amt-12-5669-2019, 2019
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The estimation of surface rainfall from radars becomes less effective at long ranges or in mountainous regions where the radar beam is far from the ground. The method proposed in this paper investigates how vertical profiles simulated from high-resolution model can be used to predict the evolution of the precipitation below the radar beam. Our results show that this novel method leads to better results than the current operational methods that either use climatological or idealised profiles.
Stefano Federico, Rosa Claudia Torcasio, Elenio Avolio, Olivier Caumont, Mario Montopoli, Luca Baldini, Gianfranco Vulpiani, and Stefano Dietrich
Nat. Hazards Earth Syst. Sci., 19, 1839–1864, https://doi.org/10.5194/nhess-19-1839-2019, https://doi.org/10.5194/nhess-19-1839-2019, 2019
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This study shows the possibility to improve the weather forecast at the very short range (0–3 h) using lightning and/or radar reflectivity observations. We consider two challenging events that occurred over Italy, named Serrano and Livorno, characterized by moderate and exceptional rainfall, respectively.
The improvement given to the forecast by using the lightning and/or radar reflectivity observations is considerable. The best performance is obtained when using both data.
Nadia Fourrié, Mathieu Nuret, Pierre Brousseau, Olivier Caumont, Alexis Doerenbecher, Eric Wattrelot, Patrick Moll, Hervé Bénichou, Dominique Puech, Olivier Bock, Pierre Bosser, Patrick Chazette, Cyrille Flamant, Paolo Di Girolamo, Evelyne Richard, and Frédérique Saïd
Geosci. Model Dev., 12, 2657–2678, https://doi.org/10.5194/gmd-12-2657-2019, https://doi.org/10.5194/gmd-12-2657-2019, 2019
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The AROME-WMED (western Mediterranean) model is a dedicated version of the mesoscale Numerical Weather Prediction AROME-France model that ran in real time during the first special observation period of HyMeX. Two reanalyses were performed after the campaign. This paper depicts the main differences between the real-time version and the benefits brought by both HyMeX reanalyses. The second reanalysis is found to be closer to observations than the previous AROME-WMED analyses.
Mary Borderies, Olivier Caumont, Julien Delanoë, Véronique Ducrocq, Nadia Fourrié, and Pascal Marquet
Nat. Hazards Earth Syst. Sci., 19, 907–926, https://doi.org/10.5194/nhess-19-907-2019, https://doi.org/10.5194/nhess-19-907-2019, 2019
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The potential of W-band radar reflectivity to improve the quality of analyses and forecasts of heavy precipitation events in the Mediterranean area is investigated. The 1D + 3DVar assimilation method has been adapted to assimilate the W-band reflectivity in the Météo-France kilometre-scale NWP model AROME. The results suggest that the joint assimilation of W-band reflectivity and horizontal wind profiles lead to a slight improvement of moisture analyses and rainfall precipitation forecasts.
Mary Borderies, Olivier Caumont, Julien Delanoë, Véronique Ducrocq, and Nadia Fourrié
Nat. Hazards Earth Syst. Sci., 19, 821–835, https://doi.org/10.5194/nhess-19-821-2019, https://doi.org/10.5194/nhess-19-821-2019, 2019
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The study reports on the impact of the assimilation of wind data from airborne Doppler cloud-profiling radar in a kilometre-scale NWP model on predicting heavy precipitation events in the Mediterranean area. The positive impact of the assimilation of such data is particularly evidenced for a heavy precipitation event and results are slightly encouraging over a 45-day period. In addition, the impact of the length of the assimilation window in a 3h-3DVar assimilation system is investigated.
Christine Lac, Jean-Pierre Chaboureau, Valéry Masson, Jean-Pierre Pinty, Pierre Tulet, Juan Escobar, Maud Leriche, Christelle Barthe, Benjamin Aouizerats, Clotilde Augros, Pierre Aumond, Franck Auguste, Peter Bechtold, Sarah Berthet, Soline Bielli, Frédéric Bosseur, Olivier Caumont, Jean-Martial Cohard, Jeanne Colin, Fleur Couvreux, Joan Cuxart, Gaëlle Delautier, Thibaut Dauhut, Véronique Ducrocq, Jean-Baptiste Filippi, Didier Gazen, Olivier Geoffroy, François Gheusi, Rachel Honnert, Jean-Philippe Lafore, Cindy Lebeaupin Brossier, Quentin Libois, Thibaut Lunet, Céline Mari, Tomislav Maric, Patrick Mascart, Maxime Mogé, Gilles Molinié, Olivier Nuissier, Florian Pantillon, Philippe Peyrillé, Julien Pergaud, Emilie Perraud, Joris Pianezze, Jean-Luc Redelsperger, Didier Ricard, Evelyne Richard, Sébastien Riette, Quentin Rodier, Robert Schoetter, Léo Seyfried, Joël Stein, Karsten Suhre, Marie Taufour, Odile Thouron, Sandra Turner, Antoine Verrelle, Benoît Vié, Florian Visentin, Vincent Vionnet, and Philippe Wautelet
Geosci. Model Dev., 11, 1929–1969, https://doi.org/10.5194/gmd-11-1929-2018, https://doi.org/10.5194/gmd-11-1929-2018, 2018
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This paper presents the Meso-NH model version 5.4, which is an atmospheric non-hydrostatic research model that is applied on synoptic to turbulent scales. The model includes advanced numerical techniques and state-of-the-art physics parameterization schemes. It has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling.
Francesco De Angelis, Domenico Cimini, Ulrich Löhnert, Olivier Caumont, Alexander Haefele, Bernhard Pospichal, Pauline Martinet, Francisco Navas-Guzmán, Henk Klein-Baltink, Jean-Charles Dupont, and James Hocking
Atmos. Meas. Tech., 10, 3947–3961, https://doi.org/10.5194/amt-10-3947-2017, https://doi.org/10.5194/amt-10-3947-2017, 2017
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Modern data assimilation systems require knowledge of the typical differences between observations and model background (O–B). This work illustrates a 1-year O–B analysis for ground-based microwave radiometer (MWR) observations in clear-sky conditions for a prototype network of six MWRs in Europe. Observations are MWR brightness temperatures (TB). Background profiles extracted from the output of a convective-scale model are used to simulate TB through the radiative transfer model RTTOV-gb.
A. Hally, O. Caumont, L. Garrote, E. Richard, A. Weerts, F. Delogu, E. Fiori, N. Rebora, A. Parodi, A. Mihalović, M. Ivković, L. Dekić, W. van Verseveld, O. Nuissier, V. Ducrocq, D. D'Agostino, A. Galizia, E. Danovaro, and A. Clematis
Nat. Hazards Earth Syst. Sci., 15, 537–555, https://doi.org/10.5194/nhess-15-537-2015, https://doi.org/10.5194/nhess-15-537-2015, 2015
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
Wet-Radome Attenuation in ARM Cloud Radars and Its Utilization in Radar Calibration Using Disdrometer Measurements
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Retrieving cloud base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE CPR, ATLID and MSI
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Peering into the heart of thunderstorm clouds: Insights from cloud radar and spectral polarimetry
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
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We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Yuli Liu and Ian S. Adams
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-188, https://doi.org/10.5194/amt-2024-188, 2024
Revised manuscript accepted for AMT
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This paper presents our latest development in tomographic cloud reconstruction algorithms that use multi-angle TB observations to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomographic technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the tomography technique effectively increases detection sensitivity for small ice cloud particles.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2615, https://doi.org/10.5194/egusphere-2024-2615, 2024
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation (WRA) logarithmic behavior as a function of rainfall rates in light and moderate rain conditions. This WRA technique is applied to the measurements during the ARM TRACER campaign and reports Ze offsets that compare favorably with results from other traditional calibration methods.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-141, https://doi.org/10.5194/amt-2024-141, 2024
Revised manuscript accepted for AMT
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In satellite remote sensing, estimating cloud base height (CBH) is more challenging than estimating cloud top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI instrument on JAXA’s GCOM-C satellite to estimate CBH together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1936, https://doi.org/10.5194/egusphere-2024-1936, 2024
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Retrievals of CO2 column-averaged dry air mole fractions from space can be done with spaceborne spectrometers such as OCO-2. Clouds in the vicinity of a footprint lead to spectral perturbations that bias those retrievals well beyond the required accuracy for global assessments of CO2 sources and sinks. This paper presents two physics-based mitigation techniques for these biases based on accompanying imagery, which can be used operationally.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-99, https://doi.org/10.5194/amt-2024-99, 2024
Revised manuscript accepted for AMT
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This study introduces the JAXA EarthCARE L2 cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation will be quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Ho Yi Lydia Mak and Christine Unal
EGUsphere, https://doi.org/10.5194/egusphere-2024-1232, https://doi.org/10.5194/egusphere-2024-1232, 2024
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The dynamics of thunderclouds is studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Cited articles
Ackermann, L., Soderholm, J., Protat, A., Whitley, R., Ye, L., and Ridder, N.: Radar and environment-based hail damage estimates using machine learning, Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, 2024. a, b
Al-Sakka, H., Boumahmoud, A.-A., Fradon, B., Frasier, S. J., and Tabary, P.: A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars, J. Appl. Meteorol. Climatol., 52, 2328–2344, https://doi.org/10.1175/JAMC-D-12-0236.1, 2013. a, b, c, d, e, f, g, h, i
Amburn, S. A. and Wolf, P. L.: VIL Density as a Hail Indicator, Weather Forecast., 12, 473–478, https://doi.org/10.1175/1520-0434(1997)012<0473:VDAAHI>2.0.CO;2, 1997. a
ANELFA: ANELFA hailpad database, ANELFA, https://www.anelfa.asso.fr/-Reseaux-.html (last access: 18 November 2024), 2024. a
Barnes, S. L.: A Technique for Maximizing Details in Numerical Weather Map Analysis, J. Appl. Meteorol. Climatol., 3, 396–409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2, 1964. a
Battaglioli, F., Groenemeijer, P., Tsonevsky, I., and Púčik, T.: Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts, Nat. Hazards Earth Syst. Sci., 23, 3651–3669, https://doi.org/10.5194/nhess-23-3651-2023, 2023. a
Birant, D. and Kut, A.: ST-DBSCAN: An Algorithm for Clustering Spatial–Temporal Data, Data Knowl. Eng., 60, 208–221, https://doi.org/10.1016/j.datak.2006.01.013, 2007. a
Bouyssel, F., Berre, L., Bénichou, H., Chambon, P., Girardot, N., Guidard, V., Loo, C., Mahfouf, J.-F., Moll, P., Payan, C., and Raspaud, D.: The 2020 Global Operational NWP Data Assimilation System at Météo-France, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), edited by: Park, S. K. and Xu, L., 645–664 pp., Springer International Publishing, Cham, ISBN 978-3-030-77722-7, https://doi.org/10.1007/978-3-030-77722-7_25, 2022. a
Brook, J. P., Soderholm, J. S., Protat, A., McGowan, H., and Warren, R. A.: A Radar-Based Hail Climatology of Australia, Mon. Weather Rev., 152, 607–628, https://doi.org/10.1175/MWR-D-23-0130.1, 2024. a, b, c, d
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the Forecast of Convective Activity from the AROME-France System, Quarterly J. Roy. Meteorol. Soc., 142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016. a
Caumont, O., Mandement, M., Bouttier, F., Eeckman, J., Lebeaupin Brossier, C., Lovat, A., Nuissier, O., and Laurantin, O.: The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations, Nat. Hazards Earth Syst. Sci., 21, 1135–1157, https://doi.org/10.5194/nhess-21-1135-2021, 2021. a, b
Delobbe, L. and Holleman, I.: Uncertainties in Radar Echo Top Heights Used for Hail Detection, Meteorol. Appl., 13, 361–374, https://doi.org/10.1017/S1350482706002374, 2006. a, b, c, d
Dessens, J., Berthet, C., and Sanchez, J. L.: A Point Hailfall Classification Based on Hailpad Measurements: The ANELFA Scale, Atmos. Res., 83, 132–139, https://doi.org/10.1016/j.atmosres.2006.02.029, 2007. a
Dotzek, N., Groenemeijer, P., Feuerstein, B., and Holzer, A. M.: Overview of ESSL's Severe Convective Storms Research Using the European Severe Weather Database ESWD, Atmos. Res., 93, 575–586, https://doi.org/10.1016/j.atmosres.2008.10.020, 2009. a
Ester, M., Kriegel, H.-P., and Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, https://doi.org/10.5555/3001460.3001507, 1996. a
European Severe Storms Laboratory: ESWD European Severe Weather Database, European Severe Storms Laboratory, https://www.eswd.eu/ (last access: 18 November 2024), 2024. a
Fel, T., Hervier, L., Vigouroux, D., Poche, A., Plakoo, J., Cadene, R., Chalvidal, M., Colin, J., Boissin, T., Bethune, L., Picard, A., Nicodeme, C., Gardes, L., Flandin, G., and Serre, T.: Xplique: A Deep Learning Explainability Toolbox, https://doi.org/10.48550/arXiv.2206.04394, 2022. a
Figueras i Ventura, J., Boumahmoud, A.-A., Fradon, B., Dupuy, P., and Tabary, P.: Long-Term Monitoring of French Polarimetric Radar Data Quality and Evaluation of Several Polarimetric Quantitative Precipitation Estimators in Ideal Conditions for Operational Implementation at C-band, Q. J. Roy. Meteorol. Soc., 138, 2212–2228, https://doi.org/10.1002/qj.1934, 2012. a, b
Figureas i Ventura, J. and Tabary, P.: The New French Operational Polarimetric Radar Rainfall Rate Product, J. Appl. Meteorol. Climatol., 52, 1817–1835, https://doi.org/10.1175/JAMC-D-12-0179.1, 2013. a
Gagne, D. J., McGovern, A., Haupt, S. E., Sobash, R. A., Williams, J. K., and Xue, M.: Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles, Weather Forecast., 32, 1819–1840, https://doi.org/10.1175/WAF-D-17-0010.1, 2017. a
Gagne, D. J., Haupt, S. E., Nychka, D. W., and Thompson, G.: Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms, Mon. Weather Rev., 147, 2827–2845, https://doi.org/10.1175/MWR-D-18-0316.1, 2019. a
Giammanco, I. M., Maiden, B. R., Estes, H. E., and Brown-Giammanco, T. M.: Using 3D Laser Scanning Technology to Create Digital Models of Hailstones, B. Am. Meteorol. Soc., 98, 1341–1347, https://doi.org/10.1175/BAMS-D-15-00314.1, 2017. a
Gourley, J. J., Tabary, P., and du Chatelet, J. P.: A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations, J. Atmos. Ocean. Technol., 24, 1439–1451, https://doi.org/10.1175/JTECH2035.1, 2007. a, b
Greene, D. R. and Clark, R. A.: Vertically Integrated Liquid Water – A New Analysis Tool, Mon. Weather Rev., 100, 548–552, https://doi.org/10.1175/1520-0493(1972)100<0548:VILWNA>2.3.CO;2, 1972. a, b
Groenemeijer, P. and Kühne, T.: A Climatology of Tornadoes in Europe: Results from the European Severe Weather Database, Mon. Weather Rev., 142, 4775–4790, https://doi.org/10.1175/MWR-D-14-00107.1, 2014. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778, https://doi.org/10.48550/arXiv.1512.03385, 2015. a, b
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a, b
Helmus, J. J. and Collis, S. M.: The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language, J. Open Res. Softw., 4, e25, https://doi.org/10.5334/jors.119, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Holleman, I.: Hail Detection Using Single-Polarization Radar, 2001. a
Houze, R. A.: Mesoscale Convective Systems, in: International Geophysics, Vol. 104, 237–286 pp., Elsevier, ISBN 978-0-12-374266-7, https://doi.org/10.1016/B978-0-12-374266-7.00009-3, 2014. a
INSEE: Le Trajet Median Domicile-Travail Augmente de Moitié En Vingt Ans Pour Les Habitants Du Rural, https://www.insee.fr/fr/statistiques/7622203#:text=Insee%20%20Premi~Are%20^A%C2%B7%20%20%20Mai%202023%20^A%C2%B7%20n,pour%20les%20habitants%20du%20rural&text=En%202019,%20un%20%20%20tiers%20des,travaillent%20dans%20une%20zone%20urbaine (last access: 25 May 2023), 2023. a
Jiang, Z., Kumjian, M. R., Schrom, R. S., Giammanco, I., Brown-Giammanco, T., Estes, H., Maiden, R., and Heymsfield, A. J.: Comparisons of Electromagnetic Scattering Properties of Real Hailstones and Spheroids, J. Appl. Meteorol. Climatol., 58, 93–112, https://doi.org/10.1175/JAMC-D-17-0344.1, 2019. a
Kopp, J., Hering, A., Germann, U., and Martius, O.: A comprehensive verification of the weather radar-based hail metrics POH and MESHS and a recalibration of POH using dense crowdsourced observations from Switzerland, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-729, 2024. a, b, c
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet Classification with Deep Convolutional Neural Networks, Commun. ACM, 60, 84–90, https://doi.org/10.1145/3065386, 2017. a
Kumjian, M. R.: Principles and Applications of Dual-Polarization Weather Radar. Part I: Description of the Polarimetric Radar Variables, J. Oper. Meteorol., 1, 226–242, https://doi.org/10.15191/nwajom.2013.0119, 2013b. a, b, c
Kuster, C. M., Snyder, J. C., Schuur, T. J., Lindley, T. T., Heinselman, P. L., Furtado, J. C., Brogden, J. W., and Toomey, R.: Rapid-Update Radar Observations of ZDR Column Depth and Its Use in the Warning Decision Process, Weather Forecast., 34, 1173–1188, https://doi.org/10.1175/WAF-D-19-0024.1, 2019. a, b, c
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P.: Gradient-Based Learning Applied to Document Recognition, P. IEEE, 86, 2278–2324, https://doi.org/10.1109/5.726791, 1998. a
Météo-France: RADAR Double-Polar Radial raw Data from PAM* products, Météo-France, https://radarsmf.aeris-data.fr/description/?uuid=dea49d60-1489-458e-a8de-589d21b14ca4 (last access: 18 November 2024), 2024. a
Morel, C. and Sénési, S.: A Climatology of Mesoscale Convective Systems over Europe Using Satellite Infrared Imagery. I: Methodology, Q. J. Roy. Meteorol. Soc., 128, 1953–1971, https://doi.org/10.1256/003590002320603485, 2002. a, b, c
Nisi, L., Hering, A., Germann, U., Schroeer, K., Barras, H., Kunz, M., and Martius, O.: Hailstorms in the Alpine Region: Diurnal Cycle, 4D-characteristics, and the Nowcasting Potential of Lightning Properties, Q. J. Roy. Meteorol. Soc., 146, 4170–4194, https://doi.org/10.1002/qj.3897, 2020. a
Ortega, K. L.: Evaluating a Hail Size Discrimination Algorithm for Dual-Polarized WSR-88Ds Using High-Resolution Reports and Forecaster Feedback, in: 36th Conference on Radar Meteorology (16–20 September, 2013), AMS, https://ams.confex.com/ams/36Radar/webprogram/Paper228601.html (last access: 7 November 2024), 2013. a
Ortega, K. L., Krause, J. M., and Ryzhkov, A. V.: Polarimetric Radar Characteristics of Melting Hail. Part III: Validation of the Algorithm for Hail Size Discrimination, J. Appl. Meteorol. Climatol., 55, 829–848, https://doi.org/10.1175/JAMC-D-15-0203.1, 2016. a, b
Park, H. S., Ryzhkov, A. V., Zrnić, D. S., and Kim, K.-E.: The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS, Weather Forecast., 24, 730–748, https://doi.org/10.1175/2008WAF2222205.1, 2009. a
Pilorz, W., Zięba, M., Szturc, J., and Łupikasza, E.: Large Hail Detection Using Radar-Based VIL Calibrated with Isotherms from the ERA5 Reanalysis, Atmos. Res., 274, 106185, https://doi.org/10.1016/j.atmosres.2022.106185, 2022. a, b
Punge, H. J. and Kunz, M.: Hail Observations and Hailstorm Characteristics in Europe: A Review, Atmos. Res., 176–177, 159–184, https://doi.org/10.1016/j.atmosres.2016.02.012, 2016. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Khain, A. P.: Polarimetric Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using Spectral Microphysical Modeling, J. Appl. Meteorol. Climatol., 52, 2849–2870, https://doi.org/10.1175/JAMC-D-13-073.1, 2013a. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Zhang, P.: Polarimetric Radar Characteristics of Melting Hail. Part II: Practical Implications, J. Appl. Meteorol. Climatol., 52, 2871–2886, https://doi.org/10.1175/JAMC-D-13-074.1, 2013b. a, b, c
Shedd, L., Kumjian, M. R., Giammanco, I., Brown-Giammanco, T., and Maiden, B. R.: Hailstone Shapes, J. Atmos. Sci., 78, 639–652, https://doi.org/10.1175/JAS-D-20-0250.1, 2021. a
Shi, J., Wang, P., Wang, D., and Jia, H.: Radar-Based Hail-producing Storm Detection Using Positive Unlabeled Classification, Tehnički vjesnik, 27, 941–950, https://doi.org/10.17559/TV-20190903094335, 2020. a
Smith, P. L. and Waldvogel, A.: On Determinations of Maximum Hailstone Sizes from Hailpad Observations, J. Appl. Meteorol. Climatol., 28, 71–76, https://doi.org/10.1175/1520-0450(1989)028<0071:ODOMHS>2.0.CO;2, 1989. a
Smith, T. M., Lakshmanan, V., Stumpf, G. J., Ortega, K. L., Hondl, K., Cooper, K., Calhoun, K. M., Kingfield, D. M., Manross, K. L., Toomey, R., and Brogden, J.: Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities, B. Am. Meteorol. Soc., 97, 1617–1630, https://doi.org/10.1175/BAMS-D-14-00173.1, 2016. a
Soderholm, J. S. and Kumjian, M. R.: Automating the analysis of hailstone layers, Atmos. Meas. Tech., 16, 695–706, https://doi.org/10.5194/amt-16-695-2023, 2023. a
Soderholm, J. S., McGowan, H., Richter, H., Walsh, K., Weckwerth, T. M., and Coleman, M.: An 18-Year Climatology of Hailstorm Trends and Related Drivers across Southeast Queensland, Australia, Q. J. Roy. Meteorol. Soc., 143, 1123–1135, https://doi.org/10.1002/qj.2995, 2017. a
Steinert, J., Tracksdorf, P., and Heizenreder, D.: Hymec: Surface Precipitation Type Estimation at the German Weather Service, Weather Forecast., 36, 1611–1627, https://doi.org/10.1175/WAF-D-20-0232.1, 2021. a
Straka, J. M., Zrnić, D. S., and Ryzhkov, A. V.: Bulk Hydrometeor Classification and Quantification Using Polarimetric Radar Data: Synthesis of Relations, J. Appl. Meteorol. Climatol., 39, 1341–1372, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2, 2000. a
Tabary, P., Henaff, A. L., Vulpiani, G., Parent-du-Châtelet, J., and Gourley, J. J.: Melting Layer Characterization and Identification with a C-band Dual-Polarization Radar: A Long-Term Analysis, https://www.researchgate.net/profile/Gianfranco-Vulpiani/publication/258833606_Melting_layer _characterization_and_identification_with_a_C-band_dual-polarization_radar_A_long-term_analysis/links/59eda2150f7e9bfdeb71c2b7/Melting-layer-characterization-and-identification-with (last access: 7 November 2024), 2006. a, b
Trefalt, S., Germann, U., Hering, A., Clementi, L., Boscacci, M., Schroër, K., and Schwierz, C.: Operational radar hail detection algorithms at MeteoSwiss: quality assesssment and improvement, Tech. rep., MeteoSwiss, No. 284, https://doi.org/10.18751/PMCH/TR/284.HailClimateSwitzerland/1.0, 2023. a, b, c
Vivekanandan, J., Zrnic, D. S., Ellis, S. M., Oye, R., Ryzhkov, A. V., and Straka, J.: Cloud Microphysics Retrieval Using S-Band Dual-Polarization Radar Measurements, B. Am. Meteorol. Soc., 80, 381–388, https://doi.org/10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2, 1999. a, b, c
Waldvogel, A., Federer, B., and Grimm, P.: Criteria for the Detection of Hail Cells, J. Appl. Meteorol. Climatol., 18, 1521–1525, https://doi.org/10.1175/1520-0450(1979)018<1521:CFTDOH>2.0.CO;2, 1979. a
Wang, P., Lv, W., Wang, C., and Hou, J.: Hail Storms Recognition Based on Convolutional Neural Network*, in: 2018 13th World Congress on Intelligent Control and Automation (WCICA), 1703–1708 pp., https://doi.org/10.1109/WCICA.2018.8630701, 2018. a, b
Zrnić, D. S., Bringi, V. N., Balakrishnan, N., Aydin, K., Chandrasekar, V., and Hubbert, J.: Polarimetric Measurements in a Severe Hailstorm, Mon. Weather Rev., 121, 2223–2238, https://doi.org/10.1175/1520-0493(1993)121<2223:PMIASH>2.0.CO;2, 1993. a
Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
This study demonstrates the potential of enhancing severe-hail detection through the application...
