Vol. 32, issue 11, article # 10

Rubinshtein K.G., Gubenko I.M., Ignatov R.Yu., Tikhonenko N.D., Yusupov Yu.I. Experiments on lightning data assimilation gathered from lightning detection network. // Optika Atmosfery i Okeana. 2019. V. 32. No. 11. P. 936–941 [in Russian].
Copy the reference to clipboard
Abstract:

The work is devoted to the analysis of our first results about the impact of lightning data assimilation on the numerical weather forecast. We present a brief overview of the methods for lightning data assimilation in weather prediction models, a description of the algorithm used, and the results of numerical experiments on convective storms over Krasnodar region, Russia, observed in 2017. It is found that the average absolute errors are reduced. It is shown that the configuration of prognostic precipitation fields and their intensity is much closer to the observations. This is especially clearly seen for light precipitation (0–7 mm).

Keywords:

thunderstorms, convective precipitation, data assimilation, WRF-ARW, lightning detection networks

Figures:
References:

  1. Belov D. Groza v Moskve 13 iyulya 2016: Pol'zovatelej seti pokorili vspyshki molnij v stolitse [Elektronnyj resurs]. URL: https://www.metronews.ru/novosti/moscow/reviews/groza-v-moskve-13-iyulya-2016-polzovateley-seti-pokorili-vspyshki-molnii-v-stolice-1194538 (data obrashcheniya: 25.07.2016).
  2. Regnum: Livni i grozy v Moskve i regionah: fotoreportazhi onlajn [Elektronnyj resurs]. URL: https://egnum.ru/news/2295016.html (data obrashcheniya: 17.07.2017).
  3. Dovgalyuk Yu.A., Veremej N.E., Sin'kevich A.A. Primenenie polutoramernoj modeli dlya resheniya fundamental'nyh i prikladnyh zadach fiziki oblakov. SPb.: Mobi Dik, 2013. Iss. 2. 220 p.
  4. Lay E.H. Investigating lightning-to-ionosphere energy coupling based on VLF lightning propagation haracterization: PhD Thesis. Seattle: University of Washington, 2008. 26 p.
  5. Adzhiev A.H., Stasenko V.N., Tapashanov V.O. Sistema grozopelengatsii na Severnom Kavkaze // Meteorol. i gidrol. 2013. N 1. P. 5–11.
  6. Snegurov A.V., Snegurov V.S. Eksperimental'naya grozopelengatsionnaya sistema // Tr. GGO. 2012. Iss. 567. P. 188–200.
  7. Skamaroch W.C., Klemp J.B., Dudhia J., Gill D.O., Barker D.M, Duda M.G., Huang X.Yu., Wang W., Powers J.G. A description of the Advanced Research WRF Version 3. National Center of Atmospheric Research: Boulder, Colorado, 2008. 113 p.
  8. Hakim G.J., Regulski P., Mass C, Torn R.D. Lightning data assimilation using an ensemble Kalman filter // Extended Abstracts of the 20th Int. Lightning Detection Conf. Norman, Oklahoma, USA. October, 2014. P. 3683–3695.
  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. Wang Y., Yang Y., Liu D., Zhang D., Yao W., Wand C. A case study of assimilating lightning-proxy relative humidity with WRF-3DVAR // Atmosphere. 2017. V. 8, N 55. P. 1–20.
  11. Dixon K., Mass C.F., Gregory J.H., Robert H. The impact of lightning data assimilation on deterministic and ensemble forecasts of convective events // J. Atmos. Ocean. Technol. 2016. V. 33. P. 1801–1823.
  12. Giannaros T.M., Kotroni V., Lagouvardos K. WRF-LTNGDA: A lightning data assimilation technique implemented in the WRF model for improving precipitation forecasts // Environ. Model. Softw. 2016. V. 76. P. 54–68.
  13. Kain J.S. The Kain–Fritsch convective parameterization: An update // J. Appl. Meteorol. 2004. V. 43, N 1. P. 170–181.
  14. Mansell E.R., Ziegler C.L., MacGorman D.R. A lightning data assimilation technique for mesoscale forecast models // Mon. Weather Rev. 2006. V. 135. P. 1732–1748.
  15. Lagouvardos K., Kotroni V., Defer E., Bousquet O. Study of a heavy precipitation event over southern France, in the frame of HYMEX project: Observational analysis and model results using assimilation of lightning // Atmos. Res. V. 134. P. 45–55.
  16. Bruning E.C., Weiss S.A., Calhoun K.M. Continuous variability in thunderstorm primary electrification and an evaluation of inverted-polarity terminology // Atmos. Res. 2014. V. 135. P. 274–284.
  17. 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, N 1. P. 171–194.
  18. Ek M.B., Mitchell K.E., Lin Y., Rogers E., Grunmann P., Koren V., Gayno G., Tarpley J.D. Implementation of NOAH land surface model advances in the NCEP operational mesoscale Eta model // J. Geophys. Res. 2003. V. 108, N 22. P. 8851.
  19. 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.
  20. RD 52.27.284-91. Rukovodyashchij dokument. Metodicheskie ukazaniya. Provedenie proizvodstvennyh (operativnyh) ispytanij novyh i usovershenstvovannyh metodov gidrometeorologicheskih i geliofizicheskih prognozov. M.: Komitet Gidrometeorologii pri Kabinete Ministrov SSSR, 1991. 149 p.
  21. Hromov S.P., Mamontova L.I. Meteorologicheskij slovar'. L.: Gidrometeoizdat, 1974. 568 p.
  22. Harold F. Pierce: Global Precipitation Analysis [Electronic resource]. URL: https://precip.gsfc.nasa.gov/ (last access: 11.11.2018).

Back