Vol. 37, issue 05, article # 11

Abstract:

The paper considers the possibilities of taking into account data from lightning networks in the procedure for lightning data assimilation in numerical models of atmospheric dynamics. A universal procedure is proposed and the code is implemented within the framework of the WRF-ARW model. According to the data from lightning detection networks, the cells of the computational grid are determined, in which lightning was recorded. Then moisture is iteratively added in these cells until the occurrence of thermodynamic instability and, hence, convection. The effect of using this scheme on the forecast of precipitation, temperature, and humidity is studied, and a comparison is made with other lightning assimilation methods. The use of data from lightning detectors makes it possible to locally improve the forecast of heavy precipitation and temperature in areas where thunderstorms were observed. The Piercy–Obukhov coefficient for forecasting intense precipitation using the proposed procedure increases from 0.26 to 0.40.

Keywords:

data assimilation, thunderstorm, convective precipitation, WRF-ARW model, lightning detection network

Figures:

References:

1. Cummins K.L., Murphy M.J. An overview of lightning locating systems: History, techniques, and data uses, with an in-depth look at the US NLDN // IEEE Trans. Electromag. Comp. 2009. V. 51, N 3. P. 499–518.
2. Panyukov A.V., Buduev D.V., Malov D.N. Sistemy passivnogo monitoringa grozovoj deyatel'nosti // Vest. Yuzhno-Ural'skogo gos. un-ta. Ser.: Matematika. Mekhanika. Fizika. 2003. N 4. P. 11–20.
3. Lay E.H. Investigating lightning-to-ionosphere energy coupling based on VLF lightning propagation haracterization: PhD Thesis. Seattle: University of Washington, 2008. 26 p.
4. Аджиев А.Х., Стасенко В.Н., Тапасханов В.О. Система грозопеленгации на Северном Кавказе // Метеорол. и гидрол. 2013. N 1. P. 5–11.
5. Snegurov A.V., Snegurov V.S. Eksperimental'naya grozopelengacionnaya sistema // Tr. GGO. 2012. Iss. 567. P. 188–200.
6. Alexander G.D., Weinman J.A., Karyampudi V.M., Olson W.S., Lee A.C.L. The effect of assimilating rain rates derived from satellites and lightning on forecasts of the 1993 Superstorm // Mon. Weather Rev. 1999. V. 127, N 7. P. 1433–1457.
7. Chang D.E., Weinman J.A., Morales C.A., Olson W.S. The effect of space borne microwave and ground-based continuous lighting measurements on forecasts of the 1998 Groundhog 2 day storm // Mon. Weather Rev. 2001. V. 129. P. 1809–1833.
8. Pessi A.T., Businger S. The impact of lightning data assimilation on a winter storm simulation over the North Pacific Ocean // Mon. Weather Rev. 2009. V. 137, N 10. P. 3177–3195.
9. Fierro A.O., Mansell E.R., Ziegler C.L., 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. 2012. V. 140, N 8. P. 2609–2627.
10. Fierro A.O., Clark A.J., Mansell E.R., MacGorman D.R., Dembek S.R., Ziegler C.L. 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. 2015. V. 143, N 3. P. 757–777.
11. Fierro A.O., Gao J., Ziegler C.L., Calhoun K.M., Mansell E.R., 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. 2016. V. 144, N 11. P. 4373–4393.
12. Wang Y., Yang Y., Wang C. Improving forecasting of strong convection by assimilating cloud-to-ground lightning data using the physical initialization method // Atmos. Res. 2014. V. 150. P. 31–41.
13. Papadopoulos A., Chronis T.G., Anagnostou E.N. Improving convective precipitation forecasting through assimilation of regional lightning measurements in a mesoscale model // Mon. Weather Rev. 2005. V. 133, N 7. P. 1961–1977.
14. Giannaros T.M., Kotroni V., Lagouvardos K. WRFLTNGDA: A lightning data assimilation technique implemented in the WRF model for improving precipitation forecasts // Environ. Model. Softw. 2016. V. 76. P. 54–68.
15. Heath N.K., Pleim J.E., Gilliam R.C., Kang D. A simple lightning assimilation technique for improving retrospective WRF simulations // J. Adv. Model. Earth Syst. 2016. V. 8, N 4. P. 1806–1824.
16. Dixon K., Mass C.F., Hakim G.J., Holzworth R.H. The impact of lightning data assimilation on deterministic and ensemble forecasts of convective events // J. Atmos. Ocean. Technol. 2016. V. 33, N 9. P. 1801–1823.
17. URL: https://arxiv.org/abs/1306.1884 (last access: 14.02.2024).
18. Chen Z., Qie X., Liu D., Xiong Y. Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model // Atmos. Res. 2019. V. 221. P. 72–87.
19. Rubinshtejn K.G., Gubenko I.M., Ignatov R.Yu., Tihonenko N.D., Yusupov Yu.I. Eksperimenty po usvoeniyu dannyh seti grozopelengacii // Optika atmosf. i okeana. 2019. V. 32, N 11. P. 936–941; Rubinstein K.G., Gubenko I.M., Ignatov R.Yu., Tikhonenko N.D., Yusupov Yu.I. Experiments on lightning detection network data assimilation // Atmos. Ocean. Opt. 2020. V. 33, N 2. P. 219–228.
20. Gubenko I.M., Rubinstein K.G. Primer usvoeniya dannyh neskol'kih setej grozopelengacii v chislennom prognoze pogody // Optika atmosf. i okeana. 2021. V. 34, N 10. P. 803–807; Gubenko I.M., Rubinstein K.G. An example of assimilation of data from several lightning detection networks in a numerical weather forecast // Atmos. Ocean. Opt. 2022. V. 35, N 1. P. 65–71.
21. Feng G., Qie X., Yuan T., Niu S. Analysis on lightning activity and precipitation structure of hailstorms // Sci. China. Ser. D. Earth Sci. 2007. V. 50, N 4. P. 629–663.
22. Janjic Z.I. The step–mountain Eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes // Mon. Weather Rev. 1994. V. 122. P. 927–945.
23. Kain J.S. The Kain–Fritsch convective parameterization: An update // J. Appl. Meteorol. 2004. V. 43, N 1. P. 170–181.
24. Skamarock W.C., Klemp J.B. A time-split nonhydrostatic atmospheric model for research and NWP applications // J. Comp. Phys. 2008. V. 227, iss. 7. P. 3465–3485.
25. Mansell E.R., Ziegler C.L., Bruning E.C. Simulated electrification of a small thunderstorm with two-moment bulk microphysics // J. Atmos. Sci. 2010. V. 67. P. 171–194.
26. Iacono M.J., Delamere J.S., Mlawer E.J., Shephard M.W., Clough S.A., Collins W.D. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models // J. Geophys. Res. 2008. V. 113. P. D13103.
27. Niu Guo–Yue, Yang Z.–L., Mitchell K.E., Chen F., Ek M.B., Barlage M., Kumar A., Manning K., Niyogi D., Rosero E., Tewari M., Xia Y. The community Noah land surface model with multiparameterization options (Noah–MP): 1. Model description and evaluation with local-scale measurements // J. Geophys. Res. 2011. V. 116. P. D12109.
28. Nakanishi M., Niino H. Development of an improved turbulence closure model for the atmospheric boundary layer // J. Meteor. Soc. Japan. 2009. V. 87. P. 895–912.
29. Pendergrass A. & National Center for Atmospheric Research Staff. The Climate Data Guide: GPCP (Daily): Global Precipitation Climatology Project. URL: https: //climatedataguide.ucar.edu/climate-data/gpcp-daily-global-precipitation-climatology-project (last access: 13.08.2023).