Cecil, D. J., Buechler, D. E., and Blakeslee, R. J.: Gridded Lightning Climatology from TRMM-LIS and OTD: Dataset Description, Atmos. Res., 135, 404–414,
https://doi.org/10.1016/j.atmosres.2012.06.028, 2014.
a
Chakraborty, R., Menghal, P., Harshitha, M., and Sodunke, M.: Climatology of Lightning Activities Across the Equatorial African Region, in: IEEE 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 30 May–4 June 2022, Gran Canaria, Spain, 1–4,
https://doi.org/10.23919/AT-AP-RASC54737.2022.9814276, 2022.
a
Charn, A. B. and Parishani, H.: Predictive Proxies of Present and Future Lightning in a Superparameterized Model, J. Geophys. Res.-Atmos., 126, e2021JD035461,
https://doi.org/10.1029/2021JD035461, 2021.
a
Chen, H., Janizek, J. D., Lundberg, S., and Lee, S.-I.: True to the model or true to the data?, arXiv [preprint],
https://doi.org/10.48550/arXiv.2006.16234, 2020.
a,
b
Chen, H., Covert, I. C., Lundberg, S. M., and Lee, S.-I.: Algorithms to estimate Shapley value feature attributions, Nature Machine Intelligence, 5, 590–601, 2023. a
Cummins, K., Krider, E., and Malone, M.: The US National Lightning Detection Network and Applications of Cloud-to-Ground Lightning Data by Electric Power Utilities, IEEE T. Electromagn. C., 40, 465–480,
https://doi.org/10.1109/15.736207, 1998.
a
DeCaria, A. J., Pickering, K. E., Stenchikov, G. L., and Ott, L. E.: Lightning-Generated
NOx and its Impact on Tropospheric Ozone Production: A Three-Dimensional Modeling Study of a Stratosphere-Troposphere Experiment: Radiation, Aerosols and Ozone (STERAO-A) Thunderstorm, J. Geophys. Res.-Atmos., 110, D14303,
https://doi.org/10.1029/2004JD005556, 2005.
a
Dutta, D. and Pal, S. K.: Interpretation of Black Box for Short-Term Predictions of Pre-Monsoon Cumulonimbus Cloud Events over Kolkata, Journal of Data, Information and Management, 4, 167–183,
https://doi.org/10.1007/s42488-022-00071-9, 2022.
a
Ehrensperger, G., Hell, T., Mayr, G. J., and Simon, T.: Evaluating the generalization ability of a deep learning model trained to detect cloud-to-ground lightning on raw ERA5 data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15817,
https://doi.org/10.5194/egusphere-egu23-15817, 2023.
a
Ehrensperger, G., Hell, T., Mayr, G., and Simon, T.: xai_lightningprocesses, Zenodo [code],
https://doi.org/10.5281/zenodo.13907708, 2024.
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
Finney, D. L., Doherty, R. M., Wild, O., Huntrieser, H., Pumphrey, H. C., and Blyth, A. M.: Using cloud ice flux to parametrise large-scale lightning, Atmos. Chem. Phys., 14, 12665–12682,
https://doi.org/10.5194/acp-14-12665-2014, 2014.
a,
b,
c
Finney, D. L., Doherty, R. M., Wild, O., Stevenson, D. S., MacKenzie, I. A., and Blyth, A. M.: A Projected Decrease in Lightning under Climate Change, Nat. Clim. Change, 8, 210–213,
https://doi.org/10.1038/s41558-018-0072-6, 2018.
a,
b
Groenemeijer, P., Púcik, T., Tsonevsky, I., and Bechtold, P.: An Overview of Convective Available Potential Energy and Convective Inhibition provided by NWP models for operational forecasting, ECMWF,
https://doi.org/10.21957/q392hofrl, 2019.
a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, CDS [data set],
https://doi.org/10.24381/cds.adbb2d47, 2018.
a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
a,
b
Hilburn, K. A., Ebert-Uphoff, I., and Miller, S. D.: Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations, J. Appl. Meteorol. Clim., 60, 3–21,
https://doi.org/10.1175/JAMC-D-20-0084.1, 2021.
a
Holmlund, K., Grandell, J., Schmetz, J., Stuhlmann, R., Bojkov, B., Munro, R., Lekouara, M., Coppens, D., Viticchie, B., August, T., Theodore, B., Watts, P., Dobber, M., Fowler, G., Bojinski, S., Schmid, A., Salonen, K., Tjemkes, S., Aminou, D., and Blythe, P.: Meteosat Third Generation (MTG): Continuation and Innovation of Observations from Geostationary Orbit, B. Am. Meteorol. Soc., 102, 990–1015,
https://doi.org/10.1175/BAMS-D-19-0304.1, 2021.
a
Janzing, D., Minorics, L., and Blöbaum, P.: Feature relevance quantification in explainable AI: A causal problem, in: International Conference on artificial intelligence and statistics, PMLR, 108, 2907–2916, 2020. a
Lou, Y., Caruana, R., and Gehrke, J.: Intelligible Models for Classification and Regression, in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, Association for Computing Machinery, New York, NY, USA, 150–158,
https://doi.org/10.1145/2339530.2339556, 2012.
a
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Advances in Neural Information Processing Systems, vol. 30, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., Curran Associates, Inc.,
https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf (last access: 20 February 2025), 2017.
a,
b
Markowski, P. and Richardson, Y.: Mesoscale Meteorology in Midlatitudes, Wiley-Blackwell,
https://doi.org/10.1002/9780470682104, 2010.
a,
b
Mayer, K. J. and Barnes, E. A.: Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network, Geophys. Res. Lett., 48, e2020GL092092,
https://doi.org/10.1029/2020GL092092, 2021.
a
McCaul, E. W., Goodman, S. J., LaCasse, K. M., and Cecil, D. J.: Forecasting Lightning Threat Using Cloud-Resolving Model Simulations, Weather Forecast., 24, 709–729,
https://doi.org/10.1175/2008WAF2222152.1, 2009.
a
Morgenstern, D., Stucke, I., Simon, T., Mayr, G. J., and Zeileis, A.: Differentiating lightning in winter and summer with characteristics of the wind field and mass field, Weather Clim. Dynam., 3, 361–375,
https://doi.org/10.5194/wcd-3-361-2022, 2022.
a
Morgenstern, D., Stucke, I., Mayr, G. J., Zeileis, A., and Simon, T.: Thunderstorm environments in Europe, Weather Clim. Dynam., 4, 489–509,
https://doi.org/10.5194/wcd-4-489-2023, 2023.
a,
b,
c
Price, C. and Rind, D.: A Simple Lightning Parameterization for Calculating Global Lightning Distributions, J. Geophys. Res.-Atmos., 97, 9919–9933,
https://doi.org/10.1029/92JD00719, 1992.
a,
b
Reineking, B., Weibel, P., Conedera, M., and Bugmann, H.: Environmental Determinants of Lightning- v. Human-Induced Forest Fire Ignitions Differ in a Temperate Mountain Region of Switzerland, Int. J. Wildland Fire, 19, 541–557,
https://doi.org/10.1071/WF08206, 2010.
a
Reynolds, S., Brook, M., and Gourley, M. F.: Thunderstorm Charge Separation, J. Atmos. Sci., 14, 426–436,
https://doi.org/10.1175/1520-0469(1957)014<0426:TCS>2.0.CO;2, 1957.
a
Ritenour, A. E., Morton, M. J., McManus, J. G., Barillo, D. J., and Cancio, L. C.: Lightning Injury: A Review, Burns, 34, 585–594,
https://doi.org/10.1016/j.burns.2007.11.006, 2008.
a
Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., and Moreira, A.: Generation and performance assessment of the global TanDEM-X digital elevation model, ISPRS J. Photogramm., 132, 119–139,
https://doi.org/10.1016/j.isprsjprs.2017.08.008, 2017.
a
Romps, D. M., Charn, A. B., Holzworth, R. H., Lawrence, W. E., Molinari, J., and Vollaro, D.: CAPE Times P Explains Lightning Over Land But Not the Land-Ocean Contrast, Geophys. Res. Lett., 45, 12623–12630,
https://doi.org/10.1029/2018GL080267, 2018.
a
Saunders, C. P. R., Bax-norman, H., Emersic, C., Avila, E. E., and Castellano, N. E.: Laboratory Studies of the Effect of Cloud Conditions on Graupel/Crystal Charge Transfer in Thunderstorm Electrification, Q. J. Roy. Meteor. Soc., 132, 2653–2673,
https://doi.org/10.1256/qj.05.218, 2006.
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,
b
Shan, S., Allen, D., Li, Z., Pickering, K., and Lapierre, J.: Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains, Atmos. Chem. Phys., 23, 14547–14560,
https://doi.org/10.5194/acp-23-14547-2023, 2023.
a,
b
Shi, M., Zhang, W., Fan, P., Chen, Q., Liu, Z., Li, Q., and Liu, X.: Modelling Deep Convective Activity Using Lightning Clusters and Machine Learning, Int. J. Climatol., 42, 952–973,
https://doi.org/10.1002/joc.7282, 2022.
a,
b
Silva, S. J., Keller, C. A., and Hardin, J.: Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence, J. Adv. Model. Earth Sy., 14, e2021MS002881,
https://doi.org/10.1029/2021MS002881, 2022.
a,
b
Simon, T., Mayr, G., Morgenstern, D., Umlauf, N., and Zeileis, A.: Amplification of annual and diurnal cycles of alpine lightning, Clim. Dynam., 61, 1–13,
https://doi.org/10.1007/s00382-023-06786-8, 2023.
a,
b,
c,
d
Simon, T., Schulz, W., Ehrensperger, G., and Mayr, G.: ALDIS cloud to ground lightning strike occurrence aggregated to spatiotemporal ERA5 cells (summer months 2010 to 2019), Zenodo [data set],
https://doi.org/10.5281/zenodo.13164463, 2024.
a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014. a
Stirnberg, R., Cermak, J., Kotthaus, S., Haeffelin, M., Andersen, H., Fuchs, J., Kim, M., Petit, J.-E., and Favez, O.: Meteorology-driven variability of air pollution (PM
1) revealed with explainable machine learning, Atmos. Chem. Phys., 21, 3919–3948,
https://doi.org/10.5194/acp-21-3919-2021, 2021.
a
Sundararajan, M. and Najmi, A.: The many Shapley values for model explanation, in: Proceedings of the 37th International Conference on Machine Learning, PMLR, 119, 9269–9278, 2020. a
Takahashi, T., Sugimoto, S., Kawano, T., and Suzuki, K.: Microphysical Structure and Lightning Initiation in Hokuriku Winter Clouds, J. Geophys. Res.-Atmos., 124, 13156–13181,
https://doi.org/10.1029/2018JD030227, 2019.
a
Takeuti, T., Nakano, M., Brook, M., Raymond, D. J., and Krehbiel, P.: The anomalous winter thunderstorms of the Hokuriku Coast, J. Geophys. Res.-Oceans, 83, 2385–2394,
https://doi.org/10.1029/JC083iC05p02385, 1978.
a
Tippett, M. K., Lepore, C., Koshak, W. J., Chronis, T., and Vant-Hull, B.: Performance of a Simple Reanalysis Proxy for U. S. Cloud-to-Ground Lightning, Int. J. Climatol., 39, 3932–3946,
https://doi.org/10.1002/joc.6049, 2019.
a
Toms, B. A., Barnes, E. A., and Hurrell, J. W.: Assessing Decadal Predictability in an Earth-System Model Using Explainable Neural Networks, Geophys. Res. Lett., 48, e2021GL093842,
https://doi.org/10.1029/2021GL093842, 2021.
a
Tost, H., Jöckel, P., and Lelieveld, J.: Lightning and convection parameterisations – uncertainties in global modelling, Atmos. Chem. Phys., 7, 4553–4568,
https://doi.org/10.5194/acp-7-4553-2007, 2007.
a
Ukkonen, P. and Mäkelä, A.: Evaluation of Machine Learning Classifiers for Predicting Deep Convection, J. Adv. Model. Earth Sy., 11, 1784–1802,
https://doi.org/10.1029/2018MS001561, 2019.
a,
b
Ukkonen, P., Manzato, A., and Mäkelä, A.: Evaluation of Thunderstorm Predictors for Finland Using Reanalyses and Neural Networks, J. Appl. Meteorol. Clim., 56, 2335–2352,
https://doi.org/10.1175/JAMC-D-16-0361.1, 2017.
a,
b
Wang, D., Zheng, D., Wu, T., and Takagi, N.: Winter Positive Cloud-to-Ground Lightning Flashes Observed by LMA in Japan, IEEJ T. Electr. Electr., 16, 402–411,
https://doi.org/10.1002/tee.23310, 2021.
a
Wang, P., Yuval, J., and O'Gorman, P. A.: Non-local parameterization of atmospheric subgrid processes with neural networks, J. Adv. Model. Earth Sy., 14, e2022MS002984,
https://doi.org/10.1029/2022MS002984, 2022.
a
Wood, S. N., Li, Z., Shaddick, G., and Augustin, N. H.: Generalized Additive Models for Gigadata: Modeling the U. K. Black Smoke Network Daily Data, J. Am. Stat. Assoc., 112, 1199–1210,
https://doi.org/10.1080/01621459.2016.1195744, 2017.
a
Zhang, Y., Tiňo, P., Leonardis, A., and Tang, K.: A Survey on Neural Network Interpretability, IEEE Transactions on Emerging Topics in Computational Intelligence, 5, 726–742,
https://doi.org/10.1109/TETCI.2021.3100641, 2021.
a
