Vol. 35, issue 05, article # 10

Ignatov R. Yu., Rubinshtein K. G., Yusupov Yu. I. Forecast of the maximum thickness of ice deposits. // Optika Atmosfery i Okeana. 2022. V. 35. No. 05. P. 408–413. DOI: 10.15372/AOO20220510 [in Russian].
Copy the reference to clipboard
Abstract:

The methods and results of numerical prediction of the maximum thickness of ice deposits are described. The success of ice deposition forecasts is estimated from calculations based on the WRF-ARW model output for different Russian regions.

Keywords:

glazed frost, estimates of maximum thickness of ice deposits, numerical weather forecast model WRF-ARW

Figures:
References:

1. Ignatov R.Yu., Rubinshtein K.G., Yusupov Yu.I. Chislennye eksperimenty po prognozu gololednyh yavlenij // Optika atmosf. i okeana. 2020. V. 33, N 9. P. 735–741; Ignatov R.Yu., Rubinshtein K.G., Yusupov Yu.I. Numerical experiments on forecasting glaze phenomena // Atmos. Ocean. Opt. 2020. V. 33, N 6. P. 682–689.
2. Rubinshtejn K.G., Ignatov R.Yu., Yusupov Yu.I., Titov D.E. Ispol'zovanie teplo-balansnogo metoda dlya prognozirovaniya gololedno-izmorozevyh otlozhenij na provodah vozdushnyh linij elektroperedachi // Energiya edinoj Seti. 2018. V. 37, N 2. P. 43–50.
3. Titov D.E., Ugarov G.G., Soshinov A.G. Monitoring the intensity of ice formation on overhead electric power lines and contact networks // Power Technol. Eng. 2015. V. 49, N 1. P. 78–82.
4. Titov D.E., Ugarov G.G., Ustinov A.A. Аnalysis of application of models to assess parameters of ice formation on overhead electric power lines // Power Technol. Eng. 2017. V. 51, N 2. P. 240–246.
5. DeGaetano A.T., Belcher B.N., Spier P.L. Short-term ice accretion forecasts for electric utilities using the weather research and forecasting model and a modified precipitation-type algorithm // Weather Forecas. 2008. V. 23. P. 878–853.
6. DeGaetano A.T., Belcher B.N., Spier P.L., Zarnani A., Musilek P., Shi X., Ke X., He H., Greiner R.M. Learning to predict ice accretion on electric power lines // J. Eng. Appl. Art. Intel. Arc. 2012. V. 25, N 3. P. 609–617.
7. Nygaard В.E.K. Evaluation of icing simulations for the “COST727 icing test sites” in Europe // Proc. IWAIS XIII, 2009. P. 11–16.
8. Thompson G. The weather research and forecasting (WRF) model to predict ground structural icing // proc. IWAIS XIII, 2009. P. 2–10.
9. Shao J., Laux S.J., Trainor B.J., Pettifer R.E.W. Nowcasts of temperature and ice on overhead railway transmission wires // Meteorol. Appl. 2003. V. 10, N 2. P. 123–133.
10. Degaetano A.T., Belcher B.N., Spier P.L. Short-term ice accretion forecasts for electric utilities using the weather research and forecasting model and a modified precipitation-type algorithm // Weather Forecast. 2008. V. 23. P. 838–853.
11. Skamarock W.C., Klemp J.B., Dudhia J., Gill D.O., Barker D., Duda M.G., Huang X.-Y., Wang W.A. Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR. USA: University Corporation for Atmospheric Research, 2008. 520 р.
12. Ramer J. An empirical technique for diagnosing precipitation type from model output // Fifth Intern. Conf. on Aviation Weather Syst. Vienna, 1993. P. 227–230.
13. Skamarock W.C., Klemp J.B., Dudhia J., Gill D.O., Liu Z., Berner J., Huang X-Yu. A Description of the Advanced Research WRF Model Version 4.3. 2021. DOI: 10.5065/1dfh-6p97.
14. Jones K.F. Ice Accretion in freezing rain // Environ. Sci., Phys. Eng. 1996. DOI: 10.21236/ada310659.
15. NCEP Products Inventory [Electronic resource]. URL: https://www.nco.ncep.noaa.gov/pmb/ products/ (last access: 10.10.2021).
16. Hong S.-Y., Noh Y., Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes // Mon. Weather Rev. 2006. V. 134. P. 2318–2341.
17. Nakanishi M., Niino H. An improved Mellor–Yamada level 3 model: Its numerical stability and application to a regional prediction of advecting fog // Bound.-Lay. Meteorol. 2006. V. 119. P. 397–407.
18. Nakanishi M., Niino H. Development of an improved turbulence closure model for the atmospheric boundary layer // J. Meteorol. Soc. Jap. 2009. V. 87. P. 895–912.
19. Olson J.B., Kenyon J.S., Angevine W.M., Brown J.M., Pagowski M., Sušelj K. A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF-ARW. 2019. DOI: 10.25923/n9wm-be49.
20. Pleim J.E. A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing // J. Appl. Meteorol. Climatol. 2007. V. 46. P. 1383–1395.
21. Bougeault P., Lacarrere P. Parameterization of orography-induced turbulence in a mesobeta-scale model // Mon. Weather Rev. 1989. V. 117. P. 1872–1890.
22. Bretherton C.S., Park S. A new moist turbulence parameterization in the Community Atmosphere Model // J. Climate. 2009. V. 22. P. 3422–3448.
23. Angevine W.M., Jiang H., Mauritsen T. Performance of an eddy diffusivity-mass flux scheme for shallow cumulus boundary layers // Mon. Weather Rev. 2010. V. 138. P. 2895–2912.
24. Shin H.H., Hong S.-Y. Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions // Mon. Weather Rev. 2015. V. 143. P. 250–271.
25. Herve G., Bretherton C.S. A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers // Mon. Weather Rev. 2001. V. 129. P. 357–377.