K. L. Cummins and M. J. Murphy, “An overview of lightning locating systems: history, techniques, and data uses, with an in-depth look at the US NLDN,” IEEE Trans. Electromag. Comp. 51 (3), 499–518 (2009).

Article  Google Scholar 

A. V. Panyukov, D. V. Buduev, and D. N. Malov, “Systems for passive monitoring of thunderstorm activity,” Vest. Yuzhno-Ural’skogo Gos. Univ. Ser.: Matematika. Mekhanika. Fizika, No. 4, 11–20 (2003).

Google Scholar 

E. H. Lay, PhD Thesis (University of Washington, Seattle, 2008).

A. Kh. Adzhiev, V. N. Stasenko, and V. O. Tapaskhanov, “Lightning detection system in the North Caucasus,” Russ. Meteorol. Hydrol. 38 (1), 1–5 (2013).

Article  Google Scholar 

A. V. Snegurov and V. S. Snegurov, “Experimental lightning location system,” Trudy GGO, No. 567, 188–200 (2012).

Google Scholar 

G. D. Alexander, J. A. Weinman, V. M. Karyampudi, W. S. Olson, and A. C. L. Lee, “The effect of assimilating rain rates derived from satellites and lightning on forecasts of the 1993 superstorm,” Mon. Weather. Rev. 127 (7), 1433–1457 (1999).

Article  ADS  Google Scholar 

D. E. Chang, J. A. Weinman, C. A. Morales, and W. S. Olson, “The effect of space borne microwave and ground-based continuous lighting measurements on forecasts of the 1998 Groundhog 2 day storm,” Mon. Weather. Rev. 129, 1809–1833 (2001).

Article  ADS  Google Scholar 

A. T. Pessi and S. Businger, “The impact of lightning data assimilation on a winter storm simulation over the North Pacific Ocean,” Mon. Weather. Rev. 137 (10), 3177–3195 (2009).

Article  ADS  Google Scholar 

A. O. Fierro, E. R. Mansell, C. L. Ziegler, and D. R. MacGorman, “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 (8), 2609–2627 (2012).

Article  ADS  Google Scholar 

A. O. Fierro, A. J. Clark, E. R. Mansell, D. R. MacGorman, S. R. Dembek, and C. L. Ziegler, “Impact of storm-scale lightning data assimilation on WRF-ARW precipitation forecasts during the 2013 warm season over the contiguous United States,” Mon. Weather. Rev. 143 (3), 757–777 (2015).

Article  ADS  Google Scholar 

A. O. Fierro, J. Gao, C. L. Ziegler, K. M. Calhoun, E. R. Mansell, and D. R. MacGorman, “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 (11), 4373–4393 (2016).

Article  ADS  Google Scholar 

Y. Wang, Y. Yang, and C. Wang, “Improving forecasting of strong convection by assimilating cloud-to-ground lightning data using the physical initialization method,” Atmos. Res. 150, 31–41 (2014).

Article  Google Scholar 

A. Papadopoulos, T. G. Chronis, and E. N. Anagnostou, “Improving convective precipitation forecasting through assimilation of regional lightning measurements in a mesoscale model,” Mon. Weather. Rev. 133 (7), 1961–1977 (2005).

Article  ADS  Google Scholar 

T. M. Giannaros, V. Kotroni, and K. Lagouvardos, “WRFLTNGDA: A lightning data assimilation technique implemented in the WRF Model for improving precipitation forecasts,” Environ. Model. Softw. 76, 54–68 (2016).

Article  Google Scholar 

N. K. Heath, J. E. Pleim, R. C. Gilliam, and D. Kang, “A simple lightning assimilation technique for improving retrospective WRF simulations,” J. Adv. Model. Earth Syst.8 (4), 1806–1824 (2016).

Article  ADS  Google Scholar 

K. Dixon, C. F. Mass, G. J. Hakim, and R. H. Holzworth, “The impact of lightning data assimilation on deterministic and ensemble forecasts of convective events,” J. Atmos. Ocean. Technol. 33 (9), 1801–1823 (2016).

Article  ADS  Google Scholar 

https://arxiv.org/abs/1306.1884. Cited February 14, 2024.

Z. Chen, X. Qie, D. Liu, and Y. Xiong, “Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model,” Atmos. Res. 221, 72–87 (2019).

Article  ADS  Google Scholar 

K. G. Rubinstein, I. M. Gubenko, R. Yu. Ignatov, N. D. Tikhonenko, and Yu. I. Yusupov, “Experiments on lightning detection network data assimilation,” Atmos. Ocean. Opt. 33 (2), 219–228 (2020).

Article  Google Scholar 

I. M. Gubenko and K. G. Rubinstein, “An example of assimilation of data from several lightning detection networks in a numerical weather forecast,” Atmos. Ocean. Opt. 35 (1), 65–71 (2022).

Article  Google Scholar 

G. Feng, X. Qie, T. Yuan, and S. Niu, “Analysis on lightning activity and precipitation structure of hailstorms,” Sci. China. Ser. D. Earth Sci. 50 (4), 629–663 (2007).

Article  ADS  Google Scholar 

Z. I. Janjic, “The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes,” Mon. Weather. Rev. 122, 927–945 (1994).

Article  ADS  Google Scholar 

J. S. Kain, “The Kain–Fritsch convective parameterization: An update,” J. Appl. Meteorol. 43 (1), 170–181 (2004).

Article  ADS  Google Scholar 

W. C. Skamarock and J. B. Klemp, “A time-split nonhydrostatic atmospheric model for research and NWP applications,” J. Comp. Phys. 227 (7), 3465–3485 (2008).

Article  ADS  Google Scholar 

E. R. Mansell, C. L. Ziegler, and E. C. Bruning, “Simulated electrification of a small thunderstorm with two-moment bulk microphysics,” J. Atmos. Sci. 67, 171–194 (2010).

Article  ADS  Google Scholar 

M. J. Iacono, J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, “Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models,” J. Geophys. Res. 113, D13103 (2008).

Article  ADS  Google Scholar 

Guo-Yue. Niu, Z.-L. Yang, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, A. Kumar, K. Manning, D. Niyogi, E. Rosero, M. Tewari, and Y. Xia, “The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements,” J. Geophys. Res. 116, D12109 (2011).

Article  ADS  Google Scholar 

M. Nakanishi and H. Niino, “Development of an improved turbulence closure model for the atmospheric boundary layer,” J. Meteor. Soc. Japan 87, 895–912 (2009).

Article  ADS  Google Scholar 

https://climatedataguide.ucar.edu/climate-data/gpcp-daily-global-precipitation-climatology-project. Cited August 13, 2023.