European Union: Commission Implementing Regulation (EU) 2017/373 of 1 March 2017 laying down common requirements for providers of air traffic management/air navigation services and other air traffic management network functions and their oversight, Off. J. European Union, 60, L 62,
https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0373&from=EN (last access: 3 January 2024), 2017. a
Fabry, F.: Radar meteorology: principles and practice, Cambridge University Press, ISBN 9781108460392, 2018.
a,
b
Feldmann, M., Germann, U., Gabella, M., and Berne, A.: A characterisation of Alpine mesocyclone occurrence, Weather Clim. Dynam., 2, 1225–1244,
https://doi.org/10.5194/wcd-2-1225-2021, 2021.
a,
b
Figueras i Ventura, J., Pineda, N., Besic, N., Grazioli, J., Hering, A., van der Velde, O. A., Romero, D., Sunjerga, A., Mostajabi, A., Azadifar, M., Rubinstein, M., Montanyà, J., Germann, U., and Rachidi, F.: Polarimetric radar characteristics of lightning initiation and propagating channels, Atmos. Meas. Tech., 12, 2881–2911,
https://doi.org/10.5194/amt-12-2881-2019, 2019.
a
Foote, G. B., Krauss, T. W., and Makitov, V.: Hail Metrics Using Conventional Radar, in: 85th AMS Annual Meeting, American Meteorological Society, San Diego, CA, USA, 2005,
https://ams.confex.com/ams/Annual2005/webprogram/Paper86773.html (last access: 3 January 2024), 2005. a
Foresti, L., Reyniers, M., Seed, A., and Delobbe, L.: Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium, Hydrol. Earth Syst. Sci., 20, 505–527,
https://doi.org/10.5194/hess-20-505-2016, 2016.
a
Germann, U., Galli, G., Boscacci, M., and Bolliger, M.: Radar precipitation measurement in a mountainous region, Q. J. Roy. Meteor. Soc., 132, 1669–1692, 2006. a
Germann, U., Boscacci, M., Clementi, L., Gabella, M., Hering, A., Sartori, M., Sideris, I. V., and Calpini, B.: Weather radar in complex orography, Remote Sensing, 14, 503,
https://doi.org/10.3390/rs14030503, 2022.
a,
b
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, Cambridge, Massachusetts, USA,
http://www.deeplearningbook.org (last access: 3 January 2024), 2016. a
Guastavino, S., Piana, M., Tizzi, M., Cassola, F., Iengo, A., Sacchetti, D., Solazzo, E., and Benvenuto, F.: Prediction of severe thunderstorm events with ensemble deep learning and radar data, Sci. Rep., 12, 1–14, 2022. a
Han, L., Zhao, Y., Chen, H., and Chandrasekar, V.: Advancing radar nowcasting through deep transfer learning, IEEE T. Geosci. Remote, 60, 1–9, 2021. 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, vol. 1,
https://www.copernicus.org/erad/2004/online/ERAD04_P_206.pdf (last access: 3 January 2024), 2004. a
Hoeppe, P.: Trends in weather related disasters – Consequences for insurers and society, Weather and climate extremes, 11, 70–79, 2016. a
Holle, R. L.: Annual Rates of Lightning Fatalities by Country. In Proceedings of the 20th International Lightning Detection Conference, Tucson, AZ, USA, 21–23 April 2008; Volume 2425,
https://www.researchgate.net/profile/Ronald-Holle/publication/267855823_Annual_rates_of_lightning_fatalities_by_country/links/54af154a0cf29661a3d49861/Annual-rates-of-lightning-fatalities-by-country.pdf (last access: 3 January 2024), 2008. a
Imhoff, R., Brauer, C., Overeem, A., Weerts, A., and Uijlenhoet, R.: Spatial and temporal evaluation of radar rainfall nowcasting techniques on 1,533 events, Water Resour. Res., 56, e2019WR026723,
https://doi.org/10.1029/2019WR026723, 2020.
a
International Civil Aviation Organization: Annex 3 to the Convention on International Civil Aviation: Meteorological Service for International Air Navigation, International Civil Aviation Organization, Montreal, Canada, 20 edn., ISBN 978-92-9258-482-5, 2018. a
Kumjian, M. R.: Principles and Applications of Dual-Polarization Weather Radar. Part I: Description of the Polarimetric Radar Variables, Journal of Operational Meteorology, 1, 226–242,
https://doi.org/10.15191/nwajom.2013.0119, 2013b.
a,
b
Leinonen, J., Hamann, U., and Germann, U.: Data archive for “Seamless lightning nowcasting with recurrent-convolutional deep learning”, Zenodo [data set],
https://doi.org/10.5281/zenodo.6802292, 2022a.
a
Leinonen, J., Hamann, U., and Germann, U.: Seamless lightning nowcasting with recurrent-convolutional deep learning, Artificial Intelligence for the Earth Systems, 1, e220043,
https://doi.org/10.1175/AIES-D-22-0043.1, 2022b.
a,
b,
c,
d,
e,
f,
g,
h
Leinonen, J., Hamann, U., Sideris, I. V., and Germann, U.: Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model, Geophys. Res. Lett., 50, e2022GL101626,
https://doi.org/10.1029/2022GL101626, 2023.
a,
b,
c,
d,
e,
f,
g,
h,
i
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P.: Focal Loss for Dense Object Detection, in: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22 to 29 October 2017, 2999–3007,
https://doi.org/10.1109/ICCV.2017.324, 2017.
a
Lund, N. R., MacGorman, D. R., Schuur, T. J., Biggerstaff, M. I., and Rust, W. D.: Relationships between lightning location and polarimetric radar signatures in a small mesoscale convective system, Mon. Weather Rev., 137, 4151–4170, 2009. a
Molnar, C.: Interpretable Machine Learning: A Guide For Making Black Box Models Explainable, Independently published, 2 edn.,
https://christophm.github.io/interpretable-ml-book/ (last access: 3 January 2024), 2022. a
Nisi, L., Martius, O., Hering, A., Kunz, M., and Germann, U.: Spatial and temporal distribution of hailstorms in the Alpine region: a long-term, high resolution, radar-based analysis, Q. J. Roy. Meteor. Soc., 142, 1590–1604, 2016. a
Pan, X., Lu, Y., Zhao, K., Huang, H., Wang, M., and Chen, H.: Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables Into a Deep-Learning Model, Geophys. Res. Lett., 48, e2021GL095302,
https://doi.org/10.1029/2021GL095302, 2021.
a
Pierce, C., Seed, A., Ballard, S., Simonin, D., and Li, Z.: Nowcasting, in: Doppler Radar Observations, edited by: Bech, J. and Chau, J. L., chap. 4, IntechOpen, Rijeka,
https://doi.org/10.5772/39054, 2012.
a
Poelman, D. R., Schulz, W., Diendorfer, G., and Bernardi, M.: The European lightning location system EUCLID – Part 2: Observations, Nat. Hazards Earth Syst. Sci., 16, 607–616,
https://doi.org/10.5194/nhess-16-607-2016, 2016.
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
Rädler, A. T., Groenemeijer, P. H., Faust, E., Sausen, R., and Púčik, T.: Frequency of severe thunderstorms across Europe expected to increase in the 21st century due to rising instability, npj Climate and Atmospheric Science, 2, 1–5, 2019. a
Raupach, T. H., Martius, O., Allen, J. T., Kunz, M., Lasher-Trapp, S., Mohr, S., Rasmussen, K. L., Trapp, R. J., and Zhang, Q.: The effects of climate change on hailstorms, Nat. Rev. Earth Environ., 2, 213–226, 2021. a
Rinehart, R. E.: Radar for Meteorologists, Or, You Too Can be a Radar Meteorologist, Part III, Rinehart Publications Nevada, MO, USA, ISBN 0965800237, 2010. a
Ritvanen, J., Harnist, B., Aldana, M., Mäkinen, T., and Pulkkinen, S.: Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting, IEEE J. Sel. Top. Appl., 16, 1654–1667,
https://doi.org/10.1109/JSTARS.2023.3238016, 2023.
a
Roberts, N. M. and Lean, H. W.: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events, Mon. Weather Rev., 136, 78–97, 2008. a
Rombeek, N., Leinonen, J., and Hamann, U.: Data archive for “Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning”, Zenodo [data set],
https://doi.org/10.5281/zenodo.7760740, 2023.
a
Schulz, W., Diendorfer, G., Pedeboy, S., and Poelman, D. R.: The European lightning location system EUCLID – Part 1: Performance analysis and validation, Nat. Hazards Earth Syst. Sci., 16, 595–605,
https://doi.org/10.5194/nhess-16-595-2016, 2016.
a
Seliga, T. A. and Bringi, V.: Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation, J. Appl. Meteorol. Clim., 15, 69–76, 1976. a
Shapley, L. S.: Notes on the n-Person Game – II: The Value of an n-Person Game, Tech. Rep. RM-670, The RAND Corporation,
https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM670.pdf (last access: 3 January 2024), 1951. a
Sideris, I., Gabella, M., Erdin, R., and Germann, U.: Real-time radar–rain-gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland, Q. J. Roy. Meteorol. Soc., 140, 1097–1111, 2014a. a
Sideris, I., Gabella, M., Sassi, M., and Germann, U.: The CombiPrecip experience: development and operation of a real-time radar-raingauge combination scheme in Switzerland, in: 2014 International Weather Radar and Hydrology Symposium, Washington DC, USA, 2014, 1–10,
https://www.researchgate.net/profile/Ioannis-Sideris (last access: 3 January 2024), 2014b.
a
Sideris, I. V., Foresti, L., Nerini, D., and Germann, U.: NowPrecip: Localized precipitation nowcasting in the complex terrain of Switzerland, Q. J. Roy. Meteorol. Soc., 146, 1768–1800, 2020. a
Simonin, D., Pierce, C., Roberts, N., Ballard, S. P., and Li, Z.: Performance of Met Office hourly cycling NWP-based nowcasting for precipitation forecasts, Q. J. Roy. Meteorol. Soc., 143, 2862–2873, 2017. a
Snyder, J. C., Ryzhkov, A. V., Kumjian, M. R., Khain, A. P., and Picca, J.: A Z DR column detection algorithm to examine convective storm updrafts, Weather Forecast., 30, 1819–1844, 2015. a
Taszarek, M., Allen, J. T., Brooks, H. E., Pilguj, N., and Czernecki, B.: Differing trends in United States and European severe thunderstorm environments in a warming climate, B. Am. Meteorol. Soc., 102, E296–E322, 2021. a
Vivekanandan, J., Zrnic, D., Ellis, S., Oye, R., Ryzhkov, A., and Straka, J.: Cloud microphysics retrieval using S-band dual-polarization radar measurements, B. Am. Meteorol. Soc., 80, 381–388, 1999. a
Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J., and Dixon, M.: Nowcasting thunderstorms: A status report, B. Am. Meteorol. Soc., 79, 2079–2100, 1998. a
Wolfensberger, D., Gabella, M., Boscacci, M., Germann, U., and Berne, A.: RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland, Atmos. Meas. Tech., 14, 3169–3193,
https://doi.org/10.5194/amt-14-3169-2021, 2021.
a,
b
Yin, J., Gao, Z., and Han, W.: Application of a Radar Echo Extrapolation-Based Deep Learning Method in Strong Convection Nowcasting, Earth Space Sci., 8, e2020EA001621,
https://doi.org/10.1029/2020EA001621, 2021.
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, 2020. a
