The Statistical Forecasting Models of a Hail for the Western Part of the North Caucasus and the Black Sea Coast Constructed on Output Production of Global System of Forecasts (GFS NCEP)

Abstract:

Statistical models of the hail forecast are proposed for the two regions of the North Caucasus, developed from the output of the global atmosphere model GFS NCEP. Statistical schemes are obtained as a result of discriminant analysis conducted using statistical software package SPSS. Independent variables in these schemes are the most informative predictors of strong convective cloud development, calculated on the basis of the global GFS model data related to local atmospheric instability and large-scale synoptic processes. Based on the results of the operational audit, the estimates of the success of the hail forecasts according to existing criteria are given, the high values of which assume a reduction in damage from hailstorms, when using them.

• Periodical: Materials Science Forum (Volume 931)
• Pages: 1037-1041
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.931.1037
• Online since: September 2018
• Authors: Arthur Kh. Kagermazov*
• Keywords: Criteria, Damage, Dangerous Phenomena, Forecast Success Rate, Global Model of the Atmosphere, Statistical Forecasting Model

References

[1] K.N. Golovlev, A.A. Lukin, E.V. Perekhodtseva, About the hydrodynamic-statistical forecast of the storm wind in the Northern, Norwegian and Barents Seas, J.Trudy of the Russian Hydrometeorological Centre. 354 (2015) 63-78.

[2] E.V. Perekhodtseva, On the hydrodynamic and statistical prediction of up to two days of strong winds and heavy precipitation events for the North Caucasus, J.Trudy the Russian Hydrometeorological Centre 347 (2011) 113-125.

[3] A.A Alekseyeva, V.M. Losev, Forecast of strong summer precipitation based on the output of the regional model of the Russian Hydrometeorological Centre, J.Trudy. 351 (2014) 43-63.

[4] E. Kalnay, M. Kanamitsu, and W.E Baker, Global numerical weather prediction at the National Meteorological Center, Bull. Amer. Meteor. Soc. 71 (1990) 1410-1428.

DOI: 10.1175/1520-0477(1990)071<1410:gnwpat>2.0.co;2

[5] M. Kanamitsu, Description of the NMC global data assimilation and forecast system, J. Weather and Forecasting. 4 (1989) 335-342.

DOI: 10.1175/1520-0434(1989)004<0335:dotngd>2.0.co;2

[6] M. Kanamitsu, J.C. Alpert, K.A Campana, P.M Caplan, D.G Deaven, M Iredell, B Katz, H.-L Pan and G.H White, Recent changes implemented into the global forecast system at NMC. J. Weather and Forecasting. 6 (1991) 425-435.

DOI: 10.1175/1520-0434(1991)006<0425:rciitg>2.0.co;2

[7] Information on http://www.emc.ncep.noaa.gov/GFS/doc.php.

[8] A. Kh. Kagermazov, Forecast of hail on the output data of the global atmosphere model (T254 NCEP). J. Meteorology and hydrology. 3 (2012) 28-34.

DOI: 10.3103/s106837391203003x

[9] A. Kh. Kagermazov, L.A. Sirotenko, Results of approbation of software modules for calculating meteorological parameters and computer forecast of hail according to aerological sounding data in the North Caucasus, Proceedings of Higher Educational Institutions of the North Caucasus region. Atmospheric Physics. Natural Science. (2010).

[10] L.M. Fedchenko, A. Kh. Kagermazov, The use of statistical methods for the prediction of hail processes and their characteristics, Meteorology and hydrology. 4 (1988) 41-50.

[11] G. Moosmyuller, N.N. Rebik, Marketing research With SPSS, INFRA-M, Moscow, (2009).

[12] Information on http://r-analytics.blogspot.de/2012/07/blog-post.html.

[13] Information on http://math.semestr.ru/corel/cheddok.php.