Carey, L. D., Murphy, M. J., McCormick, T. L., and Demetriades, N. W.:
Lightning location relative to storm structure in a leading-line, trailing-stratiform mesoscale convective system, J. Geophys. Res., 110, D03105,
https://doi.org/10.1029/2003JD004371, 2005.
a
Cohen, J., Patricia, C., West, S., and Aiken, L.: Applied multiple
regression, Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, Incorporated,
https://doi.org/10.4324/9781410606266, 1983.
a
Deierling, W. and Petersen, W. A.: Total lightning activity as an indicator of updraft characteristics, J. Geophys. Res.-Atmos., 113, D16210,
https://doi.org/10.1029/2007JD009598, 2008.
a,
b,
c,
d
Deierling, W., Latham, J., Petersen, W. A., Ellis, S. M., and Christian, H. J.: On the relationship of thunderstorm ice hydrometeor characteristics and
total lightning measurements, Atmos. Res., 76, 114–126,
https://doi.org/10.1016/j.atmosres.2004.11.023, 2005.
a,
b
Erdmann, F.: Preparation for the use of Meteosat Third Generation Lightning
Imager observations in short-term numerical weather prediction, PhD thesis, Université de Toulouse, Université Toulouse III – Paul Sabatier,
http://thesesups.ups-tlse.fr/4947/ (last access: 30 August 2022), 2020. a
Erdmann, F., Caumont, O., and Defer, E.: A Geostationary Lightning
Pseudo-Observation Generator Utilizing Low-Frequency Ground-Based Lightning
Observations, J. Atmos. Ocean. Tech., 39, 3–30,
https://doi.org/10.1175/JTECH-D-20-0160.1, 2022.
a,
b,
c,
d
Faggian, N., Roux, B., Steinle, P., and Ebert, B.: Fast calculation of the
fractions skill score, MAUSAM, 66, 457–466,
https://metnet.imd.gov.in/mausamdocs/166310_F.pdf (last access: 30 August 2022), 2015. a
Fierro, A. O., Mansell, E. R., Ziegler, C. L., and MacGorman, D. R.:
Application of a Lightning Data Assimilation Technique in the WRF-ARW Model
at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011, Mon.
Weather Rev., 140, 2609–2627,
https://doi.org/10.1175/MWR-D-11-00299.1, 2012.
a,
b
Fierro, A. O., Gao, J., Ziegler, C. L., Calhoun, K. M., Mansell, E. R., and
MacGorman, D. R.: Assimilation of Flash Extent Data in the Variational
Framework at Convection-Allowing Scales: Proof-of-Concept and Evaluation for
the Short-Term Forecast of the 24 May 2011 Tornado Outbreak, Mon. Weather Rev., 144, 4373–4393,
https://doi.org/10.1175/MWR-D-16-0053.1, 2016.
a,
b
Fierro, A. O., Wang, Y., Gao, J., and Mansell, E. R.: Variational Assimilation of Radar Data and GLM Lightning-Derived Water Vapor for the Short-Term Forecasts of High-Impact Convective Events, Mon.Weather Rev., 147,
4045–4069,
https://doi.org/10.1175/MWR-D-18-0421.1, 2019.
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.: Analysis of the lightning production of convective cells, Atmos. Meas. Tech., 12, 5573–5591,
https://doi.org/10.5194/amt-12-5573-2019, 2019.
a,
b,
c
Giannaros, T. M., Kotroni, V., and Lagouvardos, K.: Predicting lightning
activity in Greece with the Weather Research and Forecasting (WRF) model, Atmos. Res., 156, 1–13,
https://doi.org/10.1016/j.atmosres.2014.12.009, 2015.
a
Hu, J., Fierro, A. O., Wang, Y., Gao, J., and Mansell, E. R.: Exploring the
Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for
the Short-Term Forecasts of High-Impact Convective Events, Mon. Weather Rev., 148, 1005–1028,
https://doi.org/10.1175/MWR-D-19-0198.1, 2020.
a,
b
Karagiannidis, A., Lagouvardos, K., Lykoudis, S., Kotroni, V., Giannaros, T.,
and Betz, H. D.: Modeling lightning density using cloud top parameters, Atmos. Res., 222, 163–171,
https://doi.org/10.1016/j.atmosres.2019.02.013, 2019.
a
Kokou, P., Willemsen, P., Lekouara, M., Arioua, M., Mora, A., Van den
Braembussche, P., Neri, E., and Aminou, D. M. A.: Algorithmic Chain for
Lightning Detection and False Event Filtering Based on the MTG Lightning
Imager, IEEE T. Geosci. Remote, 56, 5115–5124,
https://doi.org/10.1109/TGRS.2018.2808965, 2018.
a
Lynn, B. H.: The Usefulness and Economic Value of Total Lightning Forecasts
Made with a Dynamic Lightning Scheme Coupled with Lightning Data Assimilation, Weather Forecast., 32, 645–663,
https://doi.org/10.1175/WAF-D-16-0031.1, 2017.
a
Mandement, M. and Caumont, O.: Contribution of personal weather stations to
the observation of deep-convection features near the ground, Nat. Hazards Earth Syst. Sci., 20, 299–322,
https://doi.org/10.5194/nhess-20-299-2020, 2020.
a
Mansell, E. R., MacGorman, D. R., Ziegler, C. L., and Straka, J. M.: Simulated three-dimensional branched lightning in a numerical thunderstorm model, J. Geophys. Res.-Atmos., 107, 2–1,
https://doi.org/10.1029/2000JD000244, 2002.
a
METEO FRANCE: Données publiques,
https://donneespubliques.meteofrance.fr/, last access: 31 August 2022. 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,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l
McCaul, E. W., Priftis, G., Case, J. L., Chronis, T., Gatlin, P. N., Goodman,
S. J., and Kong, F.: Sensitivities of the WRF Lightning Forecasting Algorithm to Parameterized Microphysics and Boundary Layer Schemes, Weather Forecast., 35, 1545–1560,
https://doi.org/10.1175/WAF-D-19-0101.1, 2020.
a
Mittermaier, M. and Roberts, N.: Intercomparison of Spatial Forecast
Verification Methods: Identifying Skillful Spatial Scales Using the Fractions
Skill Score, Weather Forecast., 25, 343–354,
https://doi.org/10.1175/2009WAF2222260.1, 2010.
a
Neter, J., Kutner, M., Nachtsheim, C., and Wasserman, W.: Applied Linear
Statistical Models, in: 4th Edn., McGraw-Hill, New York, ISBN 978-0-256-08601-0, 1996. a
Oshiro, T. M., Perez, P. S., and Baranauskas, J. A.: How Many Trees in a
Random Forest?, in: Machine Learning and Data Mining in Pattern Recognition, MLDM 2012, Lecture Notes in Computer Science, vol. 7376, edited by: Perner, P., Springer, Berlin, Heidelberg, 154–168,
https://doi.org/10.1007/978-3-642-31537-4_13, 2012.
a
Petersen, W. A., Christian, H. J., and Rutledge, S. A.: TRMM observations of
the global relationship between ice water content and lightning, Geophys. Res. Lett., 32, 1–4,
https://doi.org/10.1029/2005GL023236, 2005.
a,
b,
c,
d
Peterson, M., Deierling, W., Liu, C., Mach, D., and Kalb, C.: The properties
of optical lightning flashes and the clouds they illuminate, J. Geophys. Res.-Atmos., 122, 423–442,
https://doi.org/10.1002/2016JD025312, 2017.
a
Price, C. and Rind, D.: A simple lightning parameterization for calculating
global lightning distributions, J. Geophys. Res., 97, 9919–9933,
https://doi.org/10.1029/92JD00719, 1992.
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,
https://doi.org/10.1175/2007MWR2123.1, 2008.
a
Schultz, C. J., Petersen, W. A., and Carey, L. D.: Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather, J. Appl. Meteorol. Clim., 48, 2543–2563,
https://doi.org/10.1175/2009JAMC2237.1, 2009.
a
Schultz, C. J., Petersen, W. A., and Carey, L. D.: Lightning and Severe
Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends,
Weather Forecast., 26, 744–755,
https://doi.org/10.1175/WAF-D-10-05026.1, 2011.
a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier,
F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational
Model, Mon. Weather Rev., 139, 976–991,
https://doi.org/10.1175/2010MWR3425.1, 2011.
a
Skok, G. and Roberts, N.: Analysis of Fractions Skill Score properties for
random precipitation fields and ECMWF forecasts, Q. J. Roy. Meteorol. Soc., 142, 2599–2610,
https://doi.org/10.1002/qj.2849, 2016.
a
Weiss, S. A., MacGorman, D. R., and Calhoun, K. M.: Lightning in the Anvils of Supercell Thunderstorms, Mon. Weather Rev., 140, 2064–2079,
https://doi.org/10.1175/MWR-D-11-00312.1, 2012.
a
Wong, J., Barth, M. C., and Noone, D.: Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations, Geosci. Model Dev., 6, 429–443,
https://doi.org/10.5194/gmd-6-429-2013,
2013.
a
Xiao, X., Sun, J., Qie, X., Ying, Z., Ji, L., Chen, M., and Zhang, L.:
Lightning Data Assimilation Scheme in a 4DVAR System and Its Impact on Very
Short-Term Convective Forecasting, Mon. Weather Rev., 149, 353–373,
https://doi.org/10.1175/MWR-D-19-0396.1, 2021.
a
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., and Del Carmen Llasat, M.: Predicting the potential for lightning
activity in Mediterranean storms based on the Weather Research and
Forecasting (WRF) model dynamic and microphysical fields, J. Geophys. Res.-Atmos., 115, D04205,
https://doi.org/10.1029/2008JD010868, 2010.
a,
b,
c,
d
Yoshida, S., Morimoto, T., Ushio, T., and Kawasaki, Z.: A fifth-power
relationship for lightning activity from Tropical Rainfall Measuring Mission
satellite observations, J.Geophys. Res., 114, D09104,
https://doi.org/10.1029/2008JD010370, 2009.
a
Zhang, R., Zhang, Y., Xu, L., Zheng, D., and Yao, W.: Assimilation of total
lightning data using the three-dimensional variational method at
convection-allowing resolution, J. Meteorol. Res., 31, 731–746,
https://doi.org/10.1007/s13351-017-6133-3, 2017.
a
