Articles | Volume 22, issue 2
https://doi.org/10.5194/nhess-22-577-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-577-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Feb 2022
Research article |
| 25 Feb 2022
| 25 Feb 2022
Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance
Jussi Leinonen
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Ulrich Hamann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Urs Germann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
John R. Mecikalski
Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama, USA
Related authors
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
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
Short summary
Short summary
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.
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964, https://doi.org/10.5194/amt-13-2949-2020, https://doi.org/10.5194/amt-13-2949-2020, 2020
Short summary
Short summary
The appearance of snowflakes provides a signature of the atmospheric processes that created them. To get this information from large numbers of snowflake images, automated analysis using computer image recognition is needed. In this work, we use a neural network that learns the structure of the snowflake images to divide a snowflake dataset into classes corresponding to different sizes and structures. Unlike with most comparable methods, only minimal input from a human expert is needed.
Mark Richardson, Jussi Leinonen, Heather Q. Cronk, James McDuffie, Matthew D. Lebsock, and Graeme L. Stephens
Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019, https://doi.org/10.5194/amt-12-1717-2019, 2019
Short summary
Short summary
We retrieve cloud properties, including geometric thickness, by combining hyperspectral Orbiting Carbon Observatory-2 (OCO-2) A-band measurements with CALIPSO lidar. This uses cloudy scene data that are not used in OCO-2's main mission, which is aimed at clear-sky atmospheric CO2 abundance. This is the first retrieval using such hyperspectral information and promises to provide a unique constraint on the properties of low liquid clouds over the ocean.
Jussi Leinonen, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev
Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018, https://doi.org/10.5194/amt-11-5471-2018, 2018
Short summary
Short summary
We developed a technique for inferring the physical properties (amount, size and density) of falling snow from radar observations made using multiple different frequencies. We tested this method using measurements from airborne radar and compared the results to direct measurements from another aircraft, as well as ground-based radar. The results demonstrate that multifrequency radars have significant advantages over those with a single frequency in determining the snow size and density.
J. Leinonen, M. D. Lebsock, S. Tanelli, K. Suzuki, H. Yashiro, and Y. Miyamoto
Atmos. Meas. Tech., 8, 3493–3517, https://doi.org/10.5194/amt-8-3493-2015, https://doi.org/10.5194/amt-8-3493-2015, 2015
Short summary
Short summary
Using multiple frequencies in cloud and precipitation radars enables them to be both sensitive enough to detect thin clouds and to penetrate heavy precipitation, profiling the entire vertical structure of the atmospheric component of the water cycle. Here, we evaluate the performance of a potential future three-frequency space-based radar system by simulating its observations using data from a high-resolution global atmospheric model.
Frédéric G. Jordan, Clément Cosson, Marco Gabella, Ioannis V. Sideris, Adrien Liernur, Alexis Berne, and Urs Germann
Abstr. Int. Cartogr. Assoc., 9, 19, https://doi.org/10.5194/ica-abs-9-19-2025, https://doi.org/10.5194/ica-abs-9-19-2025, 2025
Alfonso Ferrone, Jérôme Kopp, Martin Lainer, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 17, 7143–7168, https://doi.org/10.5194/amt-17-7143-2024, https://doi.org/10.5194/amt-17-7143-2024, 2024
Short summary
Short summary
Estimates of hail size have been collected by a network of hail sensors, installed in three regions of Switzerland, since September 2018. In this study, we use a technique called “double-moment normalization” to model the distribution of diameter sizes. The parameters of the method have been defined over 70 % of the dataset and tested over the remaining 30 %. An independent distribution of hail sizes, collected by a drone, has also been used to evaluate the method.
Corey G. Amiot, Timothy J. Lang, Susan C. van den Heever, Richard A. Ferrare, Ousmane O. Sy, Lawrence D. Carey, Sundar A. Christopher, John R. Mecikalski, Sean W. Freeman, George Alexander Sokolowsky, Chris A. Hostetler, and Simone Tanelli
EGUsphere, https://doi.org/10.5194/egusphere-2024-2384, https://doi.org/10.5194/egusphere-2024-2384, 2024
Short summary
Short summary
Decoupling aerosol and environmental impacts on convection is challenging. Using airborne data, we correlated microwave-frequency convective metrics with aerosol concentrations in several different environments. Medium-to-high aerosol concentrations were often strongly and positively correlated with convective intensity and frequency, especially in favorable environments based on temperature lapse rates and K-Index. Storm environment is important to consider when evaluating aerosol effects.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Daniel Wolfensberger, Marco Gabella, Marco Boscacci, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 14, 3169–3193, https://doi.org/10.5194/amt-14-3169-2021, https://doi.org/10.5194/amt-14-3169-2021, 2021
Short summary
Short summary
In this work, we present a novel quantitative precipitation estimation method for Switzerland that uses random forests, an ensemble-based machine learning technique. The estimator has been trained with a database of 4 years of ground and radar observations. The results of an in-depth evaluation indicate that, compared with the more classical method in use at MeteoSwiss, this novel estimator is able to reduce both the average error and bias of the predictions.
Maxi Boettcher, Andreas Schäfler, Michael Sprenger, Harald Sodemann, Stefan Kaufmann, Christiane Voigt, Hans Schlager, Donato Summa, Paolo Di Girolamo, Daniele Nerini, Urs Germann, and Heini Wernli
Atmos. Chem. Phys., 21, 5477–5498, https://doi.org/10.5194/acp-21-5477-2021, https://doi.org/10.5194/acp-21-5477-2021, 2021
Short summary
Short summary
Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation. We present a case study that involves aircraft, lidar and radar observations of water and clouds in a WCB ascending from western Europe across the Alps towards the Baltic Sea during the field campaigns HyMeX and T-NAWDEX-Falcon in October 2012. A probabilistic trajectory measure and an airborne tracer experiment were used to confirm the long pathway of the WCB.
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964, https://doi.org/10.5194/amt-13-2949-2020, https://doi.org/10.5194/amt-13-2949-2020, 2020
Short summary
Short summary
The appearance of snowflakes provides a signature of the atmospheric processes that created them. To get this information from large numbers of snowflake images, automated analysis using computer image recognition is needed. In this work, we use a neural network that learns the structure of the snowflake images to divide a snowflake dataset into classes corresponding to different sizes and structures. Unlike with most comparable methods, only minimal input from a human expert is needed.
Floor van den Heuvel, Loris Foresti, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 13, 2481–2500, https://doi.org/10.5194/amt-13-2481-2020, https://doi.org/10.5194/amt-13-2481-2020, 2020
Short summary
Short summary
In areas with reduced visibility at the ground level, radar precipitation measurements higher up in the atmosphere need to be extrapolated to the ground and be corrected for the vertical change (i.e. growth and transformation) of precipitation. This study proposes a method based on hydrometeor proportions and machine learning (ML) to apply these corrections at smaller spatiotemporal scales. In comparison with existing techniques, the ML methods can make predictions from higher altitudes.
Jordi Figueras i Ventura, Nicolau Pineda, Nikola Besic, Jacopo Grazioli, Alessandro Hering, Oscar A. van der Velde, David Romero, Antonio Sunjerga, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Joan Montanyà, Urs Germann, and Farhad Rachidi
Atmos. Meas. Tech., 12, 5573–5591, https://doi.org/10.5194/amt-12-5573-2019, https://doi.org/10.5194/amt-12-5573-2019, 2019
Short summary
Short summary
This paper presents an analysis of the lightning production of convective cells. Polarimetric weather radar data were used to identify and characterize the convective cells while lightning was detected using the EUCLID network and a lightning mapping array deployed during the summer of 2017 in the northeastern part of Switzerland. In it we show that there is a good correlation between the height of the rimed-particle column and the intensity of the lightning activity in the convective cell.
Seppo Pulkkinen, Daniele Nerini, Andrés A. Pérez Hortal, Carlos Velasco-Forero, Alan Seed, Urs Germann, and Loris Foresti
Geosci. Model Dev., 12, 4185–4219, https://doi.org/10.5194/gmd-12-4185-2019, https://doi.org/10.5194/gmd-12-4185-2019, 2019
Short summary
Short summary
Reliable precipitation forecasts are vital for the society, as water-related hazards can cause economic losses and loss of lives. Pysteps is an open-source Python library for radar-based precipitation forecasting. It aims to be a well-documented platform for development of new methods as well as an easy-to-use tool for practitioners. The potential of the library is demonstrated by case studies and scientific experiments using radar data from Finland, Switzerland, the United States and Australia.
Jordi Figueras i Ventura, Nicolau Pineda, Nikola Besic, Jacopo Grazioli, Alessandro Hering, Oscar A. van der Velde, David Romero, Antonio Sunjerga, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Joan Montanyà, Urs Germann, and Farhad Rachidi
Atmos. Meas. Tech., 12, 2881–2911, https://doi.org/10.5194/amt-12-2881-2019, https://doi.org/10.5194/amt-12-2881-2019, 2019
Short summary
Short summary
This paper presents an analysis of a large dataset of lightning and polarimetric weather radar data collected over the course of a lightning measurement campaign that took place in the summer of 2017 in the area surrounding Säntis in northeastern Switzerland. We show that polarimetric weather radar data can be helpful in determining regions where lightning is more likely to occur, which is a first step towards a lightning nowcasting system.
Mark Richardson, Jussi Leinonen, Heather Q. Cronk, James McDuffie, Matthew D. Lebsock, and Graeme L. Stephens
Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019, https://doi.org/10.5194/amt-12-1717-2019, 2019
Short summary
Short summary
We retrieve cloud properties, including geometric thickness, by combining hyperspectral Orbiting Carbon Observatory-2 (OCO-2) A-band measurements with CALIPSO lidar. This uses cloudy scene data that are not used in OCO-2's main mission, which is aimed at clear-sky atmospheric CO2 abundance. This is the first retrieval using such hyperspectral information and promises to provide a unique constraint on the properties of low liquid clouds over the ocean.
Jussi Leinonen, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev
Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018, https://doi.org/10.5194/amt-11-5471-2018, 2018
Short summary
Short summary
We developed a technique for inferring the physical properties (amount, size and density) of falling snow from radar observations made using multiple different frequencies. We tested this method using measurements from airborne radar and compared the results to direct measurements from another aircraft, as well as ground-based radar. The results demonstrate that multifrequency radars have significant advantages over those with a single frequency in determining the snow size and density.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
Short summary
Short summary
A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Vikalp Mishra, James F. Cruise, Christopher R. Hain, John R. Mecikalski, and Martha C. Anderson
Hydrol. Earth Syst. Sci., 22, 4935–4957, https://doi.org/10.5194/hess-22-4935-2018, https://doi.org/10.5194/hess-22-4935-2018, 2018
Short summary
Short summary
Multiple satellite observations can be used for surface and subsurface soil moisture estimations. In this study, satellite observations along with a mathematical model were used to distribute and develop multiyear soil moisture profiles over the southeastern US. Such remotely sensed profiles become particularly useful at large spatiotemporal scales, can be a significant tool in data-scarce regions of the world, can complement various land and crop models, and can act as drought indicators etc.
Floor van den Heuvel, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 11, 5181–5198, https://doi.org/10.5194/amt-11-5181-2018, https://doi.org/10.5194/amt-11-5181-2018, 2018
Short summary
Short summary
The paper aims at characterising and quantifying the spatio-temporal variability of the melting layer (ML; transition zone from solid to liquid precipitation). A method based on the Fourier transform is found to accurately describe different ML signatures. Hence, it is applied to characterise the ML variability in a relatively flat area and in an inner Alpine valley in Switzerland, where the variability at smaller spatial scales is found to be relatively more important.
Nikola Besic, Josué Gehring, Christophe Praz, Jordi Figueras i Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018, https://doi.org/10.5194/amt-11-4847-2018, 2018
Short summary
Short summary
In this paper we propose an innovative approach for hydrometeor de-mixing, i.e., to identify and quantify the presence of mixtures of different hydrometeor types in a radar sampling volume. It is a bin-based approach, inspired by conventional decomposition methods and evaluated using C- and X-band radar measurements compared with synchronous ground observations. The paper also investigates the potential influence of incoherency in the backscattering from hydrometeor mixtures in a radar volume.
Daniele Nerini, Nikola Besic, Ioannis Sideris, Urs Germann, and Loris Foresti
Hydrol. Earth Syst. Sci., 21, 2777–2797, https://doi.org/10.5194/hess-21-2777-2017, https://doi.org/10.5194/hess-21-2777-2017, 2017
Short summary
Short summary
Stochastic generators are effective tools for the quantification of uncertainty in a number of applications with weather radar data, including quantitative precipitation estimation and very short-term forecasting. However, most of the current stochastic rainfall field generators cannot handle spatial non-stationarity. We propose an approach based on the short-space Fourier transform, which aims to reproduce the local spatial structure of the observed rainfall fields.
Nikola Besic, Jordi Figueras i Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, https://doi.org/10.5194/amt-9-4425-2016, 2016
Short summary
Short summary
In this paper we propose a novel semi-supervised method for hydrometeor classification, which takes into account both the specificities of acquired polarimetric radar measurements and the presumed electromagnetic behavior of different hydrometeor types. The method has been applied on three datasets, each acquired by different C-band radar from the Swiss network, and on two X-band research radar datasets. The obtained classification is found to be of high quality.
Luca Panziera, Marco Gabella, Stefano Zanini, Alessandro Hering, Urs Germann, and Alexis Berne
Hydrol. Earth Syst. Sci., 20, 2317–2332, https://doi.org/10.5194/hess-20-2317-2016, https://doi.org/10.5194/hess-20-2317-2016, 2016
Short summary
Short summary
This paper presents a novel system to issue heavy rainfall alerts for predefined geographical regions by evaluating the sum of precipitation fallen in the immediate past and expected in the near future. In order to objectively define the thresholds for the alerts, an extreme rainfall analysis for the 159 regions used for official warnings in Switzerland was developed. It is shown that the system has additional lead time with respect to thunderstorm tracking tools targeted for convective storms.
Claudia Emde, Robert Buras-Schnell, Arve Kylling, Bernhard Mayer, Josef Gasteiger, Ulrich Hamann, Jonas Kylling, Bettina Richter, Christian Pause, Timothy Dowling, and Luca Bugliaro
Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, https://doi.org/10.5194/gmd-9-1647-2016, 2016
Short summary
Short summary
libradtran is a widely used software package for radiative transfer calculations. It allows one to compute (polarized) radiances, irradiance, and actinic fluxes in the solar and thermal spectral regions. This paper gives an overview of libradtran version 2.0 with focus on new features (e.g. polarization, Raman scattering, absorption parameterization, cloud and aerosol optical properties). libRadtran is freely available at http://www.libradtran.org.
J. Leinonen, M. D. Lebsock, S. Tanelli, K. Suzuki, H. Yashiro, and Y. Miyamoto
Atmos. Meas. Tech., 8, 3493–3517, https://doi.org/10.5194/amt-8-3493-2015, https://doi.org/10.5194/amt-8-3493-2015, 2015
Short summary
Short summary
Using multiple frequencies in cloud and precipitation radars enables them to be both sensitive enough to detect thin clouds and to penetrate heavy precipitation, profiling the entire vertical structure of the atmospheric component of the water cycle. Here, we evaluate the performance of a potential future three-frequency space-based radar system by simulating its observations using data from a high-resolution global atmospheric model.
U. Hamann, A. Walther, B. Baum, R. Bennartz, L. Bugliaro, M. Derrien, P. N. Francis, A. Heidinger, S. Joro, A. Kniffka, H. Le Gléau, M. Lockhoff, H.-J. Lutz, J. F. Meirink, P. Minnis, R. Palikonda, R. Roebeling, A. Thoss, S. Platnick, P. Watts, and G. Wind
Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, https://doi.org/10.5194/amt-7-2839-2014, 2014
K. Liechti, L. Panziera, U. Germann, and M. Zappa
Hydrol. Earth Syst. Sci., 17, 3853–3869, https://doi.org/10.5194/hess-17-3853-2013, https://doi.org/10.5194/hess-17-3853-2013, 2013
Related subject area
Atmospheric, Meteorological and Climatological Hazards
Compound winter low-wind and cold events impacting the French electricity system: observed evolution and role of large-scale circulation
Probabilistic hazard analysis of the gas emission of Mefite d'Ansanto, southern Italy
Are heavy-rainfall events a major trigger of associated natural hazards along the German rail network?
Brief communication: Forecasting extreme precipitation from atmospheric rivers in New Zealand
The record-breaking precipitation event of December 2022 in Portugal
Compound events in Germany in 2018: drivers and case studies
Assimilation of temperature and relative humidity observations from personal weather stations in AROME-France
The anomalously thundery month of June 1925 in southwest Spain: description and synoptic analysis
Spatial identification of regions exposed to multi-hazards at the pan-European level
Classification of North Atlantic and European extratropical cyclones using multiple measures of intensity
Subseasonal forecasts of heat waves in West African cities
Impacts on and damage to European forests from the 2018–2022 heat and drought events
Brief communication: Training of AI-based nowcasting models for rainfall early warning should take into account user requirements
Examining the Eastern European extreme summer temperatures of 2023 from a long-term perspective: the role of natural variability vs. anthropogenic factors
How well are hazards associated with derechos reproduced in regional climate simulations?
Reconstructing hail days in Switzerland with statistical models (1959–2022)
Classifying extratropical cyclones and their impact on Finland’s electricity grid: Insights from 92 damaging windstorms
GTDI: a game-theory-based integrated drought index implying hazard-causing and hazard-bearing impact change
Insurance loss model vs. meteorological loss index – how comparable are their loss estimates for European windstorms?
Intense rains in Israel associated with the train effect
Convection-permitting climate model representation of severe convective wind gusts and future changes in southeastern Australia
Impact-based temporal clustering of multiple meteorological hazard types in southwestern Germany
On the potential of using smartphone sensors for wildfire hazard estimation through citizen science
Global estimates of 100-year return values of daily precipitation from ensemble weather prediction data
Soil conditioner mixtures as an agricultural management alternative to mitigate drought impacts
Exploring the sensitivity of extreme event attribution of two recent extreme weather events in Sweden using long-running meteorological observations
Probabilistic short-range forecasts of high-precipitation events: optimal decision thresholds and predictability limits
The miscellaneous synoptic forcings in the four-day widespread extreme rainfall event over North China in July 2023
Surprise floods: the role of our imagination in preparing for disasters
Modelling crop hail damage footprints with single-polarization radar: the roles of spatial resolution, hail intensity, and cropland density
Insights into ground strike point properties in Europe through the EUCLID lightning location system
The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy
Precipitation extremes in Ukraine from 1979 to 2019: climatology, large-scale flow conditions, and moisture sources
Characterizing hail-prone environments using convection-permitting reanalysis and overshooting top detections over south-central Europe
Aircraft engine dust ingestion at global airports
Catchment-scale assessment of drought impact on environmental flow in the Indus Basin, Pakistan
The risk of synoptic-scale Arctic cyclones to shipping
Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data
Climatic characteristics of the Jianghuai cyclone and its linkage with precipitation during the Meiyu period from 1961 to 2020
An appraisal of the value of simulated weather data for quantifying coastal flood hazard in the Netherlands
The usefulness of Extended-Range Probabilistic Forecasts for Heat wave forecasts in Europe
Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters
Projections and uncertainties of winter windstorm damage in Europe in a changing climate
Improving seasonal predictions of German Bight storm activity
A satellite view of the exceptionally warm summer of 2022 over Europe
Application of machine learning to forecast agricultural drought impacts for large scale sub-seasonal drought monitoring in Brazil
Demographic yearbooks as a source of weather-related fatalities: the Czech Republic, 1919–2022
FOREWARNS: development and multifaceted verification of enhanced regional-scale surface water flood forecasts
Assessment of wind–damage relations for Norway using 36 years of daily insurance data
Interannual variations in the seasonal cycle of extreme precipitation in Germany and the response to climate change
François Collet, Margot Bador, Julien Boé, Laurent Dubus, and Bénédicte Jourdier
Nat. Hazards Earth Syst. Sci., 25, 843–856, https://doi.org/10.5194/nhess-25-843-2025, https://doi.org/10.5194/nhess-25-843-2025, 2025
Short summary
Short summary
Our aim is to characterize the observed evolution of compound winter low-wind and cold events impacting the French electricity system. The frequency of compound events exhibits a decrease over the 1950–2022 period, which is likely due to a decrease in cold days. Large-scale atmospheric circulation is an important driver of compound event occurrence and has likely contributed to the decrease in cold days, while we cannot draw conclusions on its influence on the decrease in compound events.
Fabio Dioguardi, Giovanni Chiodini, and Antonio Costa
Nat. Hazards Earth Syst. Sci., 25, 657–674, https://doi.org/10.5194/nhess-25-657-2025, https://doi.org/10.5194/nhess-25-657-2025, 2025
Short summary
Short summary
We present results of non-volcanic-gas (CO2) hazard assessment at the Mefite d’Ansanto area (Italy) where a cold-gas stream, which has already been lethal to humans and animals, forms in the valleys surrounding the emission zone. We took the uncertainty related to the gas emission and meteorological conditions into account. Results include maps of CO2 concentrations at defined probability levels and the probability of overcoming specified CO2 concentrations over specified time intervals.
Sonja Szymczak, Frederick Bott, Vigile Marie Fabella, and Katharina Fricke
Nat. Hazards Earth Syst. Sci., 25, 683–707, https://doi.org/10.5194/nhess-25-683-2025, https://doi.org/10.5194/nhess-25-683-2025, 2025
Short summary
Short summary
We investigate the correlation between heavy-rainfall events and three associated natural hazards along the German rail network using GIS analyses and random-effects logistic models. The results show that 23 % of floods, 14 % of gravitational mass movements, and 2 % of tree fall events between 2017 and 2020 occurred after a heavy-rainfall event, and the probability of occurrence of flood and tree fall events significantly increased. This study contributes to more resilient rail transport.
Daniel G. Kingston, Liam Cooper, David A. Lavers, and David M. Hannah
Nat. Hazards Earth Syst. Sci., 25, 675–682, https://doi.org/10.5194/nhess-25-675-2025, https://doi.org/10.5194/nhess-25-675-2025, 2025
Short summary
Short summary
Extreme rainfall comprises a major hydrohazard for New Zealand and is commonly associated with atmospheric rivers – narrow plumes of very high atmospheric moisture transport. Here, we focus on improved forecasting of these events by testing a forecasting tool previously applied to similar situations in western Europe. However, our results for New Zealand suggest the performance of this forecasting tool may vary depending on geographical setting.
Tiago M. Ferreira, Ricardo M. Trigo, Tomás H. Gaspar, Joaquim G. Pinto, and Alexandre M. Ramos
Nat. Hazards Earth Syst. Sci., 25, 609–623, https://doi.org/10.5194/nhess-25-609-2025, https://doi.org/10.5194/nhess-25-609-2025, 2025
Short summary
Short summary
We investigate the synoptic evolution associated with the occurrence of an atmospheric river that led to a 24 h record-breaking extreme precipitation event (120.3 mm) in Lisbon, Portugal, on 13 December 2022. The synoptic background allowed the formation, on 10 December, of an atmospheric river associated with a deep extratropical cyclone and with a high moisture content and an inflow of moisture, due to the warm conveyor belt, throughout its life cycle. The system made landfall on 12 December.
Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim G. Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Svenja Szemkus, Sara M. Vallejo-Bernal, Odysseas Vlachopoulos, and Frederik Wolf
Nat. Hazards Earth Syst. Sci., 25, 541–564, https://doi.org/10.5194/nhess-25-541-2025, https://doi.org/10.5194/nhess-25-541-2025, 2025
Short summary
Short summary
Europe frequently experiences compound events, with major impacts. We investigate these events’ interactions, characteristics, and changes over time, focusing on socio-economic impacts in Germany and central Europe. Highlighting 2018’s extreme events, this study reveals impacts on water, agriculture, and forests and stresses the need for impact-focused definitions and better future risk quantification to support adaptation planning.
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
Short summary
Short summary
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.
Francisco Javier Acero, Manuel Antón, Alejandro Jesús Pérez Aparicio, Nieves Bravo-Paredes, Víctor Manuel Sánchez Carrasco, María Cruz Gallego, José Agustín García, Marcelino Núñez, Irene Tovar, Javier Vaquero-Martínez, and José Manuel Vaquero
Nat. Hazards Earth Syst. Sci., 25, 305–320, https://doi.org/10.5194/nhess-25-305-2025, https://doi.org/10.5194/nhess-25-305-2025, 2025
Short summary
Short summary
The month of June 1925 was found to be exceptional in the southwest interior of the Iberian Peninsula due to the large number of thunderstorms and their significant impacts, with serious losses of human lives and material resources. We analyzed this event from different, complementary perspectives: reconstruction of the history of the events from newspapers, study of monthly meteorological variables of the longest series available, and the analysis of the meteorological synoptic situation.
Tiberiu-Eugen Antofie, Stefano Luoni, Aloïs Tilloy, Andrea Sibilia, Sandro Salari, Gustav Eklund, Davide Rodomonti, Christos Bountzouklis, and Christina Corbane
Nat. Hazards Earth Syst. Sci., 25, 287–304, https://doi.org/10.5194/nhess-25-287-2025, https://doi.org/10.5194/nhess-25-287-2025, 2025
Short summary
Short summary
This is the first study that uses spatial patterns (clusters/hotspots) and meta-analysis in order to identify the regions at a European level at risk of multi-hazards. The findings point out the socioeconomic dimension as a determining factor in the potential risk of multi-hazards. The outcome provides valuable input for the disaster risk management policy support and will assist national authorities on the implementation of a multi-hazard approach in national risk assessment preparation.
Joona Cornér, Clément Bouvier, Benjamin Doiteau, Florian Pantillon, and Victoria A. Sinclair
Nat. Hazards Earth Syst. Sci., 25, 207–229, https://doi.org/10.5194/nhess-25-207-2025, https://doi.org/10.5194/nhess-25-207-2025, 2025
Short summary
Short summary
Classification reduces the considerable variability between extratropical cyclones (ETCs) and thus simplifies studying their representation in climate models and changes in the future climate. In this paper we present an objective classification of ETCs using measures of ETC intensity. This is motivated by the aim of finding a set of ETC intensity measures which together comprehensively describe both the dynamical and impact-relevant nature of ETC intensity.
Cedric G. Ngoungue Langue, Christophe Lavaysse, and Cyrille Flamant
Nat. Hazards Earth Syst. Sci., 25, 147–168, https://doi.org/10.5194/nhess-25-147-2025, https://doi.org/10.5194/nhess-25-147-2025, 2025
Short summary
Short summary
The present study addresses the predictability of heat waves at subseasonal timescales in West African cities over the period 2001–2020. Two models, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office models, were evaluated using two reanalyses: ERA5 and MERRA. The results suggest that at subseasonal timescales, the forecast models provide a better forecast than climatology, but the hit rate and false alarm rate are sub-optimal.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Mortimer M. Müller, Joni-Pekka Pietikäinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Sarah Veit, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, https://doi.org/10.5194/nhess-25-77-2025, 2025
Short summary
Short summary
Our research, involving 22 European scientists, investigated drought and heat impacts on forests in 2018–2022. Findings reveal that climate extremes are intensifying, with central Europe being most severely impacted. The southern region showed resilience due to historical drought exposure, while northern and Alpine areas experienced emerging or minimal impacts. The study highlights the need for region-specific strategies, improved data collection, and sustainable practices to safeguard forests.
Georgy Ayzel and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 25, 41–47, https://doi.org/10.5194/nhess-25-41-2025, https://doi.org/10.5194/nhess-25-41-2025, 2025
Short summary
Short summary
Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep learning (DL) has emerged as a powerful alternative to conventional nowcasting technologies, but it still struggles to adequately predict impact-relevant heavy rainfall. We think that DL could do much better if the training tasks were defined more specifically and that such specification presents an opportunity to better align the output of nowcasting models with actual user requirements.
Monica Ionita, Petru Vaideanu, Bogdan Antonescu, Catalin Roibu, Qiyun Ma, and Viorica Nagavciuc
Nat. Hazards Earth Syst. Sci., 24, 4683–4706, https://doi.org/10.5194/nhess-24-4683-2024, https://doi.org/10.5194/nhess-24-4683-2024, 2024
Short summary
Short summary
Eastern Europe's heat wave history is explored from 1885 to 2023, with a focus on pre-1960 events. The study reveals two periods with more frequent and intense heat waves (HWs): 1920s–1960s and 1980s–present. The research highlights the importance of a long-term perspective, revealing that extreme heat events have occurred throughout the entire study period, and it emphasizes the combined influence of climate change and natural variations on increasing HW severity.
Tristan Shepherd, Frederick Letson, Rebecca J. Barthelmie, and Sara C. Pryor
Nat. Hazards Earth Syst. Sci., 24, 4473–4505, https://doi.org/10.5194/nhess-24-4473-2024, https://doi.org/10.5194/nhess-24-4473-2024, 2024
Short summary
Short summary
A historic derecho in the USA is presented. The 29 June 2012 derecho caused more than 20 deaths and millions of US dollars of damage. We use a regional climate model to understand how model fidelity changes under different initial conditions. We find changes drive different convective conditions, resulting in large variation in the simulated hazards. The variation using different reanalysis data shows that framing these results in the context of contemporary and future climate is a challenge.
Lena Wilhelm, Cornelia Schwierz, Katharina Schröer, Mateusz Taszarek, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 3869–3894, https://doi.org/10.5194/nhess-24-3869-2024, https://doi.org/10.5194/nhess-24-3869-2024, 2024
Short summary
Short summary
In our study we used statistical models to reconstruct past hail days in Switzerland from 1959–2022. This new time series reveals a significant increase in hail day occurrences over the last 7 decades. We link this trend to increases in moisture and instability variables in the models. This time series can now be used to unravel the complexities of Swiss hail occurrence and to understand what drives its year-to-year variability.
Ilona Láng-Ritter, Terhi Kristiina Laurila, Antti Mäkelä, Hilppa Gregow, and VIctoria Anne SInclair
EGUsphere, https://doi.org/10.5194/egusphere-2024-3019, https://doi.org/10.5194/egusphere-2024-3019, 2024
Short summary
Short summary
We present a classification method for extratropical cyclones and windstorms and show their impacts on Finland's electricity grid by analysing 92 most damaging windstorms (2005–2018). The southwest- and northwest-originating windstorms cause the most damage to the power grid. The most relevant parameters for damage are the wind gust speed and extent of wind gusts. Windstorms are more frequent and damaging in autumn and winter, but weaker wind speeds in summer also cause significant damage.
Xiaowei Zhao, Tianzeng Yang, Hongbo Zhang, Tian Lan, Chaowei Xue, Tongfang Li, Zhaoxia Ye, Zhifang Yang, and Yurou Zhang
Nat. Hazards Earth Syst. Sci., 24, 3479–3495, https://doi.org/10.5194/nhess-24-3479-2024, https://doi.org/10.5194/nhess-24-3479-2024, 2024
Short summary
Short summary
To effectively track and identify droughts, we developed a novel integrated drought index that combines the effects of precipitation, temperature, and soil moisture on drought. After comparison and verification, the integrated drought index shows superior performance compared to a single meteorological drought index or agricultural drought index in terms of drought identification.
Julia Moemken, Inovasita Alifdini, Alexandre M. Ramos, Alexandros Georgiadis, Aidan Brocklehurst, Lukas Braun, and Joaquim G. Pinto
Nat. Hazards Earth Syst. Sci., 24, 3445–3460, https://doi.org/10.5194/nhess-24-3445-2024, https://doi.org/10.5194/nhess-24-3445-2024, 2024
Short summary
Short summary
European windstorms regularly cause damage to natural and human-made environments, leading to high socio-economic losses. For the first time, we compare estimates of these losses using a meteorological loss index (LI) and the insurance loss (catastrophe) model of Aon Impact Forecasting. We find that LI underestimates high-impact windstorms compared to the insurance model. Nonetheless, due to its simplicity, LI is an effective index, suitable for estimating impacts and ranking storm events.
Baruch Ziv, Uri Dayan, Lidiya Shendrik, and Elyakom Vadislavsky
Nat. Hazards Earth Syst. Sci., 24, 3267–3277, https://doi.org/10.5194/nhess-24-3267-2024, https://doi.org/10.5194/nhess-24-3267-2024, 2024
Short summary
Short summary
The train effect is related to convective cells that pass over the same place. Trains produce heavy rainfall and sometimes floods and are reported in North America during spring and summer. In Israel, 17 trains associated with Cyprus lows were identified by radar images and were found within the cold sector south of the low center and in the left flank of a maximum wind belt; they cross the Israeli coast, with a mean length of 45 km; last 1–3 h; and yield 35 mm of rainfall up to 60 mm.
Andrew Brown, Andrew Dowdy, and Todd P. Lane
Nat. Hazards Earth Syst. Sci., 24, 3225–3243, https://doi.org/10.5194/nhess-24-3225-2024, https://doi.org/10.5194/nhess-24-3225-2024, 2024
Short summary
Short summary
A computer model that simulates the climate of southeastern Australia is shown here to represent extreme wind events associated with convective storms. This is useful as it allows us to investigate possible future changes in the occurrences of these events, and we find in the year 2050 that our model simulates a decrease in the number of occurrences. However, the model also simulates too many events in the historical climate compared with observations, so these future changes are uncertain.
Katharina Küpfer, Alexandre Tuel, and Michael Kunz
EGUsphere, https://doi.org/10.5194/egusphere-2024-2803, https://doi.org/10.5194/egusphere-2024-2803, 2024
Short summary
Short summary
Using loss data, we assess when and how single and multiple types of meteorological extremes (river floods and heavy rainfall events, windstorms and convective gusts, and hail). We find that the combination of several types of hazards clusters robustly on a seasonal scale, whereas only some single hazard types occur in clusters. This can be associated with higher losses compared to isolated events. We argue for the relevance of jointly considering multiple types of hazards.
Hofit Shachaf, Colin Price, Dorita Rostkier-Edelstein, and Cliff Mass
Nat. Hazards Earth Syst. Sci., 24, 3035–3047, https://doi.org/10.5194/nhess-24-3035-2024, https://doi.org/10.5194/nhess-24-3035-2024, 2024
Short summary
Short summary
We have used the temperature and relative humidity sensors in smartphones to estimate the vapor pressure deficit (VPD), an important atmospheric parameter closely linked to fuel moisture and wildfire risk. Our analysis for two severe wildfire case studies in Israel and Portugal shows the potential for using smartphone data to compliment the regular weather station network while also providing high spatial resolution of the VPD index.
Florian Ruff and Stephan Pfahl
Nat. Hazards Earth Syst. Sci., 24, 2939–2952, https://doi.org/10.5194/nhess-24-2939-2024, https://doi.org/10.5194/nhess-24-2939-2024, 2024
Short summary
Short summary
High-impact river floods are often caused by extreme precipitation. Flood protection relies on reliable estimates of the return values. Observational time series are too short for a precise calculation. Here, 100-year return values of daily precipitation are estimated on a global grid based on a large set of model-generated precipitation events from ensemble weather prediction. The statistical uncertainties in the return values can be substantially reduced compared to observational estimates.
Juan F. Dueñas, Edda Kunze, Huiying Li, and Matthias C. Rillig
EGUsphere, https://doi.org/10.5194/egusphere-2024-2566, https://doi.org/10.5194/egusphere-2024-2566, 2024
Short summary
Short summary
We explored the possibility of adding mixtures composed of minimum dosages of several popular amendment types to soil. Adding mixtures of 3 to 5 amendment types substantially increased the capacity of soil to retain water, reduced soil erosion and increased microorganism abundance, while buffering soil from drastic changes in pH.
Erik Holmgren and Erik Kjellström
Nat. Hazards Earth Syst. Sci., 24, 2875–2893, https://doi.org/10.5194/nhess-24-2875-2024, https://doi.org/10.5194/nhess-24-2875-2024, 2024
Short summary
Short summary
Associating extreme weather events with changes in the climate remains difficult. We have explored two ways these relationships can be investigated: one using a more common method and one relying solely on long-running records of meteorological observations.
Our results show that while both methods lead to similar conclusions for two recent weather events in Sweden, the commonly used method risks underestimating the strength of the connection between the event and changes to the climate.
François Bouttier and Hugo Marchal
Nat. Hazards Earth Syst. Sci., 24, 2793–2816, https://doi.org/10.5194/nhess-24-2793-2024, https://doi.org/10.5194/nhess-24-2793-2024, 2024
Short summary
Short summary
Weather prediction uncertainties can be described as sets of possible scenarios – a technique called ensemble prediction. Our machine learning technique translates them into more easily interpretable scenarios for various users, balancing the detection of high precipitation with false alarms. Key parameters are precipitation intensity and space and time scales of interest. We show that the approach can be used to facilitate warnings of extreme precipitation.
Jinfang Yin, Feng Li, Mingxin Li, Rudi Xia, Xinghua Bao, Jisong Sun, and Xudong Liang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-145, https://doi.org/10.5194/nhess-2024-145, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
A persistent severe rainfall event occurred over North China in July 2023, which was regarded as one of the precipitation extremes of 2023 globally. The extreme rainfall was significant underestimated by forecasters at that time. Flooding from this event affected 1.3 million people, causing severe human casualties and significant economic losses. In this study, we examined the convective initiation and subsequent persistent heavy rainfall over North China based on simulations with the WRF model.
Joy Ommer, Jessica Neumann, Milan Kalas, Sophie Blackburn, and Hannah L. Cloke
Nat. Hazards Earth Syst. Sci., 24, 2633–2646, https://doi.org/10.5194/nhess-24-2633-2024, https://doi.org/10.5194/nhess-24-2633-2024, 2024
Short summary
Short summary
What’s the worst that could happen? Recent floods are often claimed to be beyond our imagination. Imagination is the picturing of a situation in our mind and the emotions that we connect with this situation. But why is this important for disasters? This survey found that when we cannot imagine a devastating flood, we are not preparing in advance. Severe-weather forecasts and warnings need to advance in order to trigger our imagination of what might happen and enable us to start preparing.
Raphael Portmann, Timo Schmid, Leonie Villiger, David N. Bresch, and Pierluigi Calanca
Nat. Hazards Earth Syst. Sci., 24, 2541–2558, https://doi.org/10.5194/nhess-24-2541-2024, https://doi.org/10.5194/nhess-24-2541-2024, 2024
Short summary
Short summary
The study presents an open-source model to determine the occurrence of hail damage to field crops and grapevines after hailstorms in Switzerland based on radar, agricultural land use data, and insurance damage reports. The model performs best at 8 km resolution for field crops and 1 km for grapevine and in the main production areas. Highlighting performance trade-offs and the relevance of user needs, the study is a first step towards the assessment of risk and damage for crops in Switzerland.
Dieter Roel Poelman, Hannes Kohlmann, and Wolfgang Schulz
Nat. Hazards Earth Syst. Sci., 24, 2511–2522, https://doi.org/10.5194/nhess-24-2511-2024, https://doi.org/10.5194/nhess-24-2511-2024, 2024
Short summary
Short summary
EUCLID's lightning data unveil distinctive ground strike point (GSP) patterns in Europe. Over seas, GSPs per flash surpass inland, reaching a minimum in the Alps. Mountainous areas like the Alps and Pyrenees have the closest GSP separation, highlighting terrain elevation's impact. The daily peak current correlates with average GSPs per flash. These findings could significantly influence lightning protection measures, urging a focus on GSP density rather than flash density for risk assessment.
Nicola Loglisci, Giorgio Boni, Arianna Cauteruccio, Francesco Faccini, Massimo Milelli, Guido Paliaga, and Antonio Parodi
Nat. Hazards Earth Syst. Sci., 24, 2495–2510, https://doi.org/10.5194/nhess-24-2495-2024, https://doi.org/10.5194/nhess-24-2495-2024, 2024
Short summary
Short summary
We analyse the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in Genoa. Rainfall observations were made using rain gauge networks based on either official networks or citizen science networks. The merged analysis stresses the spatial variability in the precipitation, which cannot be captured by the current spatial density of authoritative stations. Results show that at minimal distances the variations in cumulated rainfall over a sub-hourly duration are significant.
Ellina Agayar, Franziska Aemisegger, Moshe Armon, Alexander Scherrmann, and Heini Wernli
Nat. Hazards Earth Syst. Sci., 24, 2441–2459, https://doi.org/10.5194/nhess-24-2441-2024, https://doi.org/10.5194/nhess-24-2441-2024, 2024
Short summary
Short summary
This study presents the results of a climatological investigation of extreme precipitation events (EPEs) in Ukraine for the period 1979–2019. During all seasons EPEs are associated with pronounced upper-level potential vorticity (PV) anomalies. In addition, we find distinct seasonal and regional differences in moisture sources. Several extreme precipitation cases demonstrate the importance of these processes, complemented by a detailed synoptic analysis.
Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino
Nat. Hazards Earth Syst. Sci., 24, 2331–2357, https://doi.org/10.5194/nhess-24-2331-2024, https://doi.org/10.5194/nhess-24-2331-2024, 2024
Short summary
Short summary
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.
Claire L. Ryder, Clément Bézier, Helen F. Dacre, Rory Clarkson, Vassilis Amiridis, Eleni Marinou, Emmanouil Proestakis, Zak Kipling, Angela Benedetti, Mark Parrington, Samuel Rémy, and Mark Vaughan
Nat. Hazards Earth Syst. Sci., 24, 2263–2284, https://doi.org/10.5194/nhess-24-2263-2024, https://doi.org/10.5194/nhess-24-2263-2024, 2024
Short summary
Short summary
Desert dust poses a hazard to aircraft via degradation of engine components. This has financial implications for the aviation industry and results in increased fuel burn with climate impacts. Here we quantify dust ingestion by aircraft engines at airports worldwide. We find Dubai and Delhi in summer are among the dustiest airports, where substantial engine degradation would occur after 1000 flights. Dust ingestion can be reduced by changing take-off times and the altitude of holding patterns.
Khalil Ur Rahman, Songhao Shang, Khaled Saeed Balkhair, Hamza Farooq Gabriel, Khan Zaib Jadoon, and Kifayat Zaman
Nat. Hazards Earth Syst. Sci., 24, 2191–2214, https://doi.org/10.5194/nhess-24-2191-2024, https://doi.org/10.5194/nhess-24-2191-2024, 2024
Short summary
Short summary
This paper assesses the impact of drought (meteorological drought) on the hydrological alterations in major rivers of the Indus Basin. Threshold regression and range of variability analysis are used to determine the drought severity and times where drought has caused low flows and extreme low flows (identified using indicators of hydrological alterations). Moreover, this study also examines the degree of alterations in river flows due to drought using the hydrological alteration factor.
Alexander Frank Vessey, Kevin I. Hodges, Len C. Shaffrey, and Jonathan J. Day
Nat. Hazards Earth Syst. Sci., 24, 2115–2132, https://doi.org/10.5194/nhess-24-2115-2024, https://doi.org/10.5194/nhess-24-2115-2024, 2024
Short summary
Short summary
The risk posed to ships by Arctic cyclones has seldom been quantified due to the lack of publicly available historical Arctic ship track data. This study investigates historical Arctic ship tracks, cyclone tracks, and shipping incident reports to determine the number of shipping incidents caused by the passage of Arctic cyclones. Results suggest that Arctic cyclones have not been hazardous to ships and that ships are resilient to the rough sea conditions caused by Arctic cyclones.
Niklas Ebers, Kai Schröter, and Hannes Müller-Thomy
Nat. Hazards Earth Syst. Sci., 24, 2025–2043, https://doi.org/10.5194/nhess-24-2025-2024, https://doi.org/10.5194/nhess-24-2025-2024, 2024
Short summary
Short summary
Future changes in sub-daily rainfall extreme values are essential in various hydrological fields, but climate scenarios typically offer only daily resolution. One solution is rainfall generation. With a temperature-dependent rainfall generator climate scenario data were disaggregated to 5 min rainfall time series for 45 locations across Germany. The analysis of the future 5 min rainfall time series showed an increase in the rainfall extremes values for rainfall durations of 5 min and 1 h.
Ran Zhu and Lei Chen
Nat. Hazards Earth Syst. Sci., 24, 1937–1950, https://doi.org/10.5194/nhess-24-1937-2024, https://doi.org/10.5194/nhess-24-1937-2024, 2024
Short summary
Short summary
There is a positive correlation between the frequency of Jianghuai cyclone activity and precipitation during the Meiyu period. Its occurrence frequency has an obvious decadal variation, which corresponds well with the quasi-periodic and decadal variation in precipitation during the Meiyu period. This study provides a reference for the long-term and short-term forecasting of precipitation during the Meiyu period.
Cees de Valk and Henk van den Brink
EGUsphere, https://doi.org/10.5194/egusphere-2024-912, https://doi.org/10.5194/egusphere-2024-912, 2024
Short summary
Short summary
Recently updated flood safety norms in the Netherlands prescribe that sea dikes and other flood-protecting structures should withstand high sea levels reached very rarely, locally down to only once in 10 million years. We show that such levels can be estimated with reasonable accuracy by the cautious use of very large datasets of numerical simulations of weather and storm surge.
Natalia Korhonen, Otto Hyvärinen, Virpi Kollanus, Timo Lanki, Juha Jokisalo, Risto Kosonen, David S. Richardson, and Kirsti Jylhä
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-75, https://doi.org/10.5194/nhess-2024-75, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
The skill of hindcasts of the European Centre for Medium-Range Weather Forecasts in forecasting heat wave days (periods with the 5-day moving average temperature being above its local summer 90th percentile) over Europe 1 to 4 weeks ahead is examined. The heat wave days forecasts show potential in warning of heat risk in 1–2 weeks in advance, and enhanced accuracy in forecasting prolonged heat waves, in lead times of up to 3 weeks, when the heat wave had initiated prior to the forecast issuance.
Andi Xhelaj and Massimiliano Burlando
Nat. Hazards Earth Syst. Sci., 24, 1657–1679, https://doi.org/10.5194/nhess-24-1657-2024, https://doi.org/10.5194/nhess-24-1657-2024, 2024
Short summary
Short summary
The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania, utilizing an analytical model and optimization algorithm. The goal is to explore a multitude of generating solutions and to identify potential alternatives to the optimal solution. Advanced data analysis techniques help to discern three main distinct storm scenarios. For this particular event, the best overall solution from the optimization algorithm shows promise in reconstructing the downburst.
Luca G. Severino, Chahan M. Kropf, Hilla Afargan-Gerstman, Christopher Fairless, Andries Jan de Vries, Daniela I. V. Domeisen, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 1555–1578, https://doi.org/10.5194/nhess-24-1555-2024, https://doi.org/10.5194/nhess-24-1555-2024, 2024
Short summary
Short summary
We combine climate projections from 30 climate models with a climate risk model to project winter windstorm damages in Europe under climate change. We study the uncertainty and sensitivity factors related to the modelling of hazard, exposure and vulnerability. We emphasize high uncertainties in the damage projections, with climate models primarily driving the uncertainty. We find climate change reshapes future European windstorm risk by altering damage locations and intensity.
Daniel Krieger, Sebastian Brune, Johanna Baehr, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 24, 1539–1554, https://doi.org/10.5194/nhess-24-1539-2024, https://doi.org/10.5194/nhess-24-1539-2024, 2024
Short summary
Short summary
Previous studies found that climate models can predict storm activity in the German Bight well for averages of 5–10 years but struggle in predicting the next winter season. Here, we improve winter storm activity predictions by linking them to physical phenomena that occur before the winter. We guess the winter storm activity from these phenomena and discard model solutions that stray too far from the guess. The remaining solutions then show much higher prediction skill for storm activity.
João P. A. Martins, Sara Caetano, Carlos Pereira, Emanuel Dutra, and Rita M. Cardoso
Nat. Hazards Earth Syst. Sci., 24, 1501–1520, https://doi.org/10.5194/nhess-24-1501-2024, https://doi.org/10.5194/nhess-24-1501-2024, 2024
Short summary
Short summary
Over Europe, 2022 was truly exceptional in terms of extreme heat conditions, both in terms of temperature anomalies and their temporal and spatial extent. The satellite all-sky land surface temperature (LST) is used to provide a climatological context to extreme heat events. Where drought conditions prevail, LST anomalies are higher than 2 m air temperature anomalies. ERA5-Land does not represent this effect correctly due to a misrepresentation of vegetation anomalies.
Joseph William Gallear, Marcelo Valadares Galdos, Marcelo Zeri, and Andrew Hartley
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-60, https://doi.org/10.5194/nhess-2024-60, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
In Brazil, drought is of national concern and can have major consequences for agriculture. Here, we determine how to develop forecasts for drought impacts on vegetation health using machine learning. Results aim to inform future developments in operational drought monitoring at the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN). This information is essential for disaster preparedness and planning of future actions to support areas affected by drought.
Rudolf Brázdil, Kateřina Chromá, and Pavel Zahradníček
Nat. Hazards Earth Syst. Sci., 24, 1437–1457, https://doi.org/10.5194/nhess-24-1437-2024, https://doi.org/10.5194/nhess-24-1437-2024, 2024
Short summary
Short summary
The official mortality data in the Czech Republic in 1919–2022 are used to show long-term fluctuations in the number of fatalities caused by excessive natural cold and heat, lightning, natural disasters, and falls on ice/snow, as well as the sex and age of the deceased, based on certain meteorological, historical, and socioeconomic factors that strongly influence changes in the number and structure of such fatalities. Knowledge obtained is usable in risk management for the preservation of lives.
Ben Maybee, Cathryn E. Birch, Steven J. Böing, Thomas Willis, Linda Speight, Aurore N. Porson, Charlie Pilling, Kay L. Shelton, and Mark A. Trigg
Nat. Hazards Earth Syst. Sci., 24, 1415–1436, https://doi.org/10.5194/nhess-24-1415-2024, https://doi.org/10.5194/nhess-24-1415-2024, 2024
Short summary
Short summary
This paper presents the development and verification of FOREWARNS, a novel method for regional-scale forecasting of surface water flooding. We detail outcomes from a workshop held with UK forecast users, who indicated they valued the forecasts and would use them to complement national guidance. We use results of objective forecast tests against flood observations over northern England to show that this confidence is justified and that FOREWARNS meets the needs of UK flood responders.
Ashbin Jaison, Asgeir Sorteberg, Clio Michel, and Øyvind Breivik
Nat. Hazards Earth Syst. Sci., 24, 1341–1355, https://doi.org/10.5194/nhess-24-1341-2024, https://doi.org/10.5194/nhess-24-1341-2024, 2024
Short summary
Short summary
The present study uses daily insurance losses and wind speeds to fit storm damage functions at the municipality level of Norway. The results show that the damage functions accurately estimate losses associated with extreme damaging events and can reconstruct their spatial patterns. However, there is no single damage function that performs better than another. A newly devised damage–no-damage classifier shows some skill in predicting extreme damaging events.
Madlen Peter, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 24, 1261–1285, https://doi.org/10.5194/nhess-24-1261-2024, https://doi.org/10.5194/nhess-24-1261-2024, 2024
Short summary
Short summary
The paper introduces a statistical modeling approach describing daily extreme precipitation in Germany more accurately by including changes within the year and between the years simultaneously. The changing seasonality over years is regionally divergent and mainly weak. However, some regions stand out with a more pronounced linear rise of summer intensities, indicating a possible climate change signal. Improved modeling of extreme precipitation is beneficial for risk assessment and adaptation.
Cited articles
Abrams, M., Crippen, R., and Fujisada, H.: ASTER Global Digital Elevation
Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD), Remote Sens., 12,
1156, https://doi.org/10.3390/rs12071156, 2020. a
Autonès, F. and Claudon, M.: Algorithm Theoretical Basis Document for the
Convection Product Processors of the NWC/GEO, Tech. Rep.
SAF/NWC/CDOP/MFT/SCI/ATBD/11, Meteo-France, Toulouse,
https://www.nwcsaf.org/Downloads/GEO/2018.1/Documents/Scientific_Docs/NWC-CDOP2-GEO-MFT-SCI-ATBD-Convection_v2.2.pdf
(last access: 21 February 2022), 2012. a
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional
neural network for radar-based precipitation nowcasting, Geosci. Model Dev.,
13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a
Barras, H., Hering, A., Martynov, A., Noti, P.-A., Germann, U., and Martius,
O.: Experiences with >50,000
Bedka, K., Murillo, E. M., Homeyer, C. R., Scarino, B., and Mersiovsky, H.: The Above-Anvil Cirrus Plume: An Important Severe Weather Indicator in Visible and Infrared Satellite Imagery, Weather Forecast., 33, 1159–1181,
https://doi.org/10.1175/WAF-D-18-0040.1, 2018. a
Bedka, K. M. and Khlopenkov, K.: A Probabilistic Multispectral Pattern
Recognition Method for Detection of Overshooting Cloud Tops Using Passive
Satellite Imager Observations, J. Appl. Meteorol. Clim., 55, 1983–2005,
https://doi.org/10.1175/JAMC-D-15-0249.1, 2016. a
Chai, T. and Draxler, R. R.: Root mean square error (RMSE) or mean absolute
error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247–1250, https://doi.org/10.5194/gmd-7-1247-2014, 2014. a
Changnon, S. A.: Relationships between Thunderstorms and Cloud-to-Ground
Lightning in the United States, J. Appl. Meteorol., 32, 88–105,
https://doi.org/10.1175/1520-0450(1993)032<0088:RBTACT>2.0.CO;2, 1993. a
Czernecki, B., Taszarek, M., Marosz, M., Półrolniczak, M., Kolendowicz, L.,
Wyszogrodzki, A., and Szturc, J.: Application of machine learning to large
hail prediction – The importance of radar reflectivity, lightning occurrence
and convective parameters derived from ERA5, Atmos. Res., 227, 249–262,
https://doi.org/10.1016/j.atmosres.2019.05.010, 2019. a
Dixon, M. and Wiener, G.: TITAN: Thunderstorm Identification, Tracking,
Analysis, and Nowcasting – A Radar-based Methodology, J. Atmos. Ocean. Tech., 10, 785–797, https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2, 1993. a
Foresti, L., Sideris, I. V., Nerini, D., Beusch, L., and Germann, U.: Using a
10-Year Radar Archive for Nowcasting Precipitation Growth and Decay: A
Probabilistic Machine Learning Approach, Weather Forecast., 34, 1547–1569, https://doi.org/10.1175/WAF-D-18-0206.1, 2019. a
Franch, G., Nerini, D., Pendesini, M., Coviello, L., Jurman, G., and
Furlanello, C.: Precipitation Nowcasting with Orographic Enhanced Stacked
Generalization: Improving Deep Learning Predictions on Extreme Events,
Atmosphere, 11, 267, https://doi.org/10.3390/atmos11030267, 2020. a
GOES-R Algorithm Working Group and GOES-R Series Program Office: NOAA GOES-R
Series Advanced Baseline Imager (ABI) Level 2 Cloud Top Height (ACHA) [data set], https://doi.org/10.7289/V5HX19ZQ, 2018a. a
GOES-R Algorithm Working Group and GOES-R Series Program Office: NOAA GOES-R
Series Advanced Baseline Imager (ABI) Level 2 Cloud Optical Depth (COD) [data set], https://doi.org/10.7289/V58G8J02, 2018b. a
GOES-R Algorithm Working Group and GOES-R Series Program Office: NOAA GOES-R
Series Advanced Baseline Imager (ABI) Level 2 Cloud Top Pressure (CTP) [data set], https://doi.org/10.7289/V5D50K85, 2018c. a
GOES-R Algorithm Working Group and GOES-R Series Program Office: NOAA GOES-R
Series Advanced Baseline Imager (ABI) Level 2 Derived Stability Indices [data set], https://doi.org/10.7289/V50Z71KF, 2018d. a
GOES-R Calibration Working Group and GOES-R Series Program: NOAA GOES-R
Series Advanced Baseline Imager (ABI) Level 1b Radiances [data set], https://doi.org/10.7289/V5BV7DSR, 2017. a
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
Handwerker, J.: Cell tracking with TRACE3D – a new algorithm, Atmos. Res.,
61, 15–34, https://doi.org/10.1016/S0169-8095(01)00100-4, 2002. a
Heidinger, A. K., Pavolonis, M. J., Calvert, C., Hoffman, J., Nebuda, S.,
Straka, W., Walther, A., and Wanzong, S.: ABI Cloud Products from the GOES-R Series, in: The GOES-R Series: A New Generation of Geostationary Environmental Satellites, chap. 6, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 43–62,
https://doi.org/10.1016/B978-0-12-814327-8.00006-8, 2020. a
Heiss, W. H., McGrew, D. L., and Sirmans, D.: Nexrad: next generation weather
radar (WSR-88D), Microwave J., 33, 79+, 1990. a
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. Software, 4, e25, https://doi.org/10.5334/jors.119, 2016. a
Hering, A., Morel, C., Galli, G., Sénési, S., Ambrosetti, P., and Boscacci, M.: Nowcasting thunderstorms in the Alpine region using a radar based adaptive thresholding scheme, in: Proceedings of ERAD 2004, https://www.copernicus.org/erad/2004/online/ERAD04_P_206.pdf (last access: 21 February 2022), 2004. a, b, c
Hering, A., Sénési, S., Ambrosetti, P., and Bernard-Bouissières, I.: Nowcasting thunderstorms in complex cases using radar data, in: WMO Symposium on Nowcasting and Very Short Range Forecasting, https://www.researchgate.net/publication/228609271_Nowcasting_thunderstorms_in_complex_cases_using_radar_data
(last access: 21 February 2022), 2005. a, b
Hering, A., Germann, U., Boscacci, M., and Sénési, S.: Operational
thunderstorm nowcasting in the Alpine region using 3D-radar severe weather
parameters and lightning data, in: Proceedings of ERAD 2006, http://www.crahi.upc.edu/ERAD2006/proceedingsMask/00122.pdf (last
access: 21 February 2022), 2006. a, b
Hoffmann, J.: Entwicklung und Anwendung von statistischen Vorhersage – Interpretationsverfahren für Gewitternowcasting und Unwetterwarnungen unter Einbeziehung von Fernerkundungsdaten, PhD thesis, Freie Universität Berlin, Berlin, https://doi.org/10.17169/refubium-15903, 2008. a
Huang, W., Jiang, Y., Liu, X., Pan, Y., Li, X., Guo, R., Huang, Y., and Duan,
B.: Classified Early-warning and Nowcasting of Hail Weather Based on Radar
Products and Random Forest Algorithm, in: 2019 International Conference on
Meteorology Observations (ICMO), https://doi.org/10.1109/ICMO49322.2019.9026039, 2019. a
James, P. M., Reichert, B. K., and Heizenreder, D.: NowCastMIX: Automatic
Integrated Warnings for Severe Convection on Nowcasting Time Scales at the
German Weather Service, Weather Forecasti., 33, 1413–1433,
https://doi.org/10.1175/WAF-D-18-0038.1, 2018. a
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu,
T.-Y.: LightGBM: a highly efficient gradient boosting decision tree, in:
Proceedings of the 31st International Conference on Neural Information
Processing Systems, 3149–3157, https://dl.acm.org/doi/abs/10.5555/3294996.3295074 (last access: 21 February 2022), 2017. a
Kelly, D. L., Schaefer, J. T., and Doswell, C. A.: Climatology of Nontornadic
Severe Thunderstorm Events in the United States, Mon. Weather Rev., 113,
1997–2014, https://doi.org/10.1175/1520-0493(1985)113<1997:CONSTE>2.0.CO;2, 1985. a
Kober, K. and Tafferner, A.: Tracking and Nowcasting of Convective Cells Using Remote Sensing Data from Radar and Satellite, Meteorol. Z., 1, 75–84, https://doi.org/10.1127/0941-2948/2009/359, 2009. a, b
Kober, K., Craig, G. C., Keil, C., and Dörnbrack, A.: Blending a probabilistic nowcasting method with a high-resolution numerical weather prediction ensemble for convective precipitation forecasts, Q. J. Roy. Meteorol. Soc., 138, 755–768, https://doi.org/10.1002/qj.939, 2012. a
Kumar, A., Islam, T., Sekimoto, Y., Mattmann, C., and Wilson, B.: Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data, PLOS One, 15, 1–18,
https://doi.org/10.1371/journal.pone.0230114, 2020. a
Lagerquist, R., McGovern, A., and Smith, T.: Machine Learning for Real-Time
Prediction of Damaging Straight-Line Convective Wind, Weather Forecast., 32, 2175–2193, https://doi.org/10.1175/WAF-D-17-0038.1, 2017. a
Lagerquist, R., McGovern, A., Homeyer, C. R., Gagne II, D. J., and Smith, T.:
Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado
Prediction, Mon. Weather Rev., 148, 2837–2861, https://doi.org/10.1175/MWR-D-19-0372.1, 2020. a
Leinonen, J., Hamann, U., Germann, U., and Mecikalski, J. R.: Machine learning code and dataset for “Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance”, Zenodo [code and data set], https://doi.org/10.5281/zenodo.6206919, 2021. a
Li, J., Li, Z., and Schmit, T. J.: ABI Legacy Atmospheric Profiles and
Derived Products from the GOES-R Series, in: The GOES-R Series: A New
Generation of Geostationary Environmental Satellites, chap. 7, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 63–77, https://doi.org/10.1016/B978-0-12-814327-8.00007-X, 2020. a
Marshall, J. S. and Radhakant, S.: Radar Precipitation Maps as Lightning
Indicators, J. Appl. Meteorol. Clim., 17, 206–212,
https://doi.org/10.1175/1520-0450(1978)017<0206:RPMALI>2.0.CO;2, 1978. a
Martner, B. E., Yuter, S. E., White, A. B., Matrosov, S. Y., Kingsmill, D. E., and Ralph, F. M.: Raindrop Size Distributions and Rain Characteristics in
California Coastal Rainfall for Periods with and without a Radar Bright Band,
J. Hydrometeorol., 9, 408–425, https://doi.org/10.1175/2007JHM924.1, 2008. a
Mecikalski, J. R. and Bedka, K. M.: Forecasting Convective Initiation by
Monitoring the Evolution of Moving Cumulus in Daytime GOES Imager, Mon. Weather Rev., 134, 49–78, https://doi.org/10.1175/MWR3062.1, 2006. a
Mecikalski, J. R., MacKenzie, W. M., Koenig, M., and Muller, S.: Cloud-Top
Properties of Growing Cumulus prior to Convective Initiation as Measured by
Meteosat Second Generation. Part I: Infrared Fields, J. Appl. Meteorol. Clim., 4, 521–534, https://doi.org/10.1175/2009JAMC2344.1, 2010. a
Mecikalski, J. R., Williams, J. K., Jewett, C. P., Ahijevych, D., LeRoy, A.,
and Walker, J. R.: Probabilistic 0–1-h Convective Initiation Nowcasts that
Combine Geostationary Satellite Observations and Numerical Weather Prediction
Model Data, J. Appl. Meteorol. Clim., 54, 1039–1059,
https://doi.org/10.1175/JAMC-D-14-0129.1, 2015. a
Mecikalski, J. R., Sandmæl, T. N., Murillo, E. M., Homeyer, C. R., Bedka, K. M., Apke, J. M., and Jewett, C. P.: Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations, Mon. Weather Rev., 149, 1725–1746,
https://doi.org/10.1175/MWR-D-19-0274.1, 2021. a
Mostajabi, A., Finney, D. L., Rubinstein, M., and Rachidi, F.: Nowcasting
lightning occurrence from commonly available meteorological parameters using
machine learning techniques, Clim. Atmos. Sci., 2, 41,
https://doi.org/10.1038/s41612-019-0098-0, 2019. a
Mueller, C., Saxen, T., Roberts, R., Wilson, J., Betancourt, T., Dettling, S., Oien, N., and J., Y.: NCAR Auto-Nowcast System, Weather Forecast., 18, 545–561, https://doi.org/10.1175/1520-0434(2003)018<0545:NAS>2.0.CO;2, 2003. a
NASA/METI/AIST/Japan Spacesystems and US/Japan ASTER Science Team: ASTER
Global Digital Elevation Model V003 [data set], https://doi.org/10.5067/ASTER/ASTGTM.003, 2019. a
Natekin, A. and Knoll, A.: Gradient boosting machines, a tutorial, Front.
Neurorobot., 7, 21, https://doi.org/10.3389/fnbot.2013.00021, 2013. a
NOAA National Weather Service (NWS) Radar Operations Center: Next Generation Radar (NEXRAD) Level 2 Base Data [data set], https://doi.org/10.7289/V5W9574V, 1991. a
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A.,
Germann, U., and Foresti, L.: Pysteps: an open-source Python library for
probabilistic precipitation nowcasting (v1.0), Geosci. Model Dev., 12,
4185–4219, https://doi.org/10.5194/gmd-12-4185-2019, 2019. a
Raspaud, M., Hoese, D., Dybbroe, A., Lahtinen, P., Devasthale, A., Itkin, M.,
Hamann, U., Rasmussen, L. O., Nielsen, E. S., Leppelt, T., Maul, A., Kliche,
C., and Thorsteinsson, H.: PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data, B. Am. Meteorol. Soc., 99, 1329–1336, https://doi.org/10.1175/BAMS-D-17-0277.1, 2018. a
Roberts, R. D. and Rutledge, S.: Nowcasting Storm Initiation and Growth Using
GOES-8 and WSR-88D Data, Weather Forecast., 18, 562–584,
https://doi.org/10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2, 2003. a
Rudlosky, S. D., Goodman, S. J., and Virts, K. S.: Lightning Detection: GOES-R Series Geostationary Lightning Mapper, in: The GOES-R Series: A New
Generation of Geostationary Environmental Satellites, chap. 16, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 193–202, https://doi.org/10.1016/B978-0-12-814327-8.00016-0, 2020. a
Schmit, T. J. and Gunshor, M. M.: ABI Imagery from the GOES-R Series, in:
The GOES-R Series: A New Generation of Geostationary Environmental Satellites, chap. 4, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 23–34, https://doi.org/10.1016/B978-0-12-814327-8.00004-4, 2020. a
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-C.:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation
Nowcasting, in: Advances in Neural Information Processing Systems 28, edited
by Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R.,
Curran Associates, Inc., 802–810,
http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach
-for-precipitation-nowcasting.pdf (last access: 21 February 2022), 2015. a
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo,
W.-C.: Deep learning for precipitation nowcasting: a benchmark and a new
model, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, 5622–5632, https://dl.acm.org/doi/abs/10.5555/3295222.3295313 (last access: 21 February 2022), 2017. 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
Brodgen, 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
Snyder, J. P.: Map Projections – A Working Manual, United States Government
Printing Office, Washington, DC, USA, https://doi.org/10.3133/pp1395, 1987. a
Sprenger, M., Schemm, S., Oechslin, R., and Jenkner, J.: Nowcasting Foehn Wind Events Using the AdaBoost Machine Learning Algorithm, Weather Forecast., 32, 1079–1099, https://doi.org/10.1175/WAF-D-16-0208.1, 2017. a
Steinacker, R., Dorninger, M., Wölfelmaier, F., and Krennert, T.: Automatic Tracking of Convective Cells and Cell Complexes from Lightning and Radar Data, Meteorol. Atmos. Phys., 72, 101–110, https://doi.org/10.1007/s007030050009, 2000. a
Süli, E. and Mayers, D. F.: An Introduction to Numerical Analysis, Cambridge University Press, Cambridge, UK, https://doi.org/10.1017/CBO9780511801181, 2003. a
Sullivan, P. C.: GOES-R Series Spacecraft and Instruments, in: The GOES-R
Series: A New Generation of Geostationary Environmental Satellites, chap. 3, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J.,
Elsevier, 13–21, https://doi.org/10.1016/B978-0-12-814327-8.00003-2, 2020. a
Waldvogel, A., Federer, B., and Grimm, P.: Criteria for the Detection of Hail
Cells, J. Appl. Meteorol., 18, 1521–1525,
https://doi.org/10.1175/1520-0450(1979)018<1521:CFTDOH>2.0.CO;2, 1979. a
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE)
over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30, 79–82, https://doi.org/10.3354/cr030079, 2005. a
Wilson, J. W. and Mueller, C. K.: Nowcasts of Thunderstorm Initiation and
Evolution, Weather Forecast., 8, 113–131, https://doi.org/10.1175/1520-0434(1993)008<0113:NOTIAE>2.0.CO;2, 1993. a
Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J., and Dixon, M.: Nowcasting Thunderstorms: A Status Report, B. Amer. Meteorol. Soc., 79, 2079–2100, https://doi.org/10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2, 1998. a
Yeung, J. K., Smith, J. A., Baeck, M. L., and Villarini, G.: Lagrangian
Analyses of Rainfall Structure and Evolution for Organized Thunderstorm
Systems in the Urban Corridor of the Northeastern United States, J. Hydrometeorol., 16, 1575–1595, https://doi.org/10.1175/JHM-D-14-0095.1, 2015. a
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H.,
Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J.,
and Kitzmiller, D.: Multi-Radar Multi-Sensor (MRMS) Quantitative
Precipitation Estimation: Initial Operating Capabilities, B. Am. Meteorol. Soc., 97, 621–638, https://doi.org/10.1175/BAMS-D-14-00174.1, 2016. a
Zhou, K., Zheng, Y., Dong, W., and Wang, T.: A Deep Learning Network for
Cloud-to-Ground Lightning Nowcasting with Multisource Data, J. Atmos. Ocean.
Tech., 37, 927–942, https://doi.org/10.1175/JTECH-D-19-0146.1, 2020.
a
Zinner, T., Mannstein, H., and Tafferner, A.: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data, Meteorol. Atmos. Phys., 101, 191–210, https://doi.org/10.1007/s00703-008-0290-y, 2008. a
Short summary
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.
We evaluate the usefulness of different data sources and variables to the short-term prediction...
Altmetrics
Final-revised paper
Preprint
Similar articles
Compound winter low-wind and cold events...
Collet et al.
Brief communication: Forecasting extreme...
Kingston et al.
Are heavy-rainfall events a major trigger...
Szymczak et al.