MEDIUM-RANGE HAIL FORECAST BASED ON GLOBAL ATMOSPHERIC MODEL OUTPUT DATA

Introduction. The forecast of dangerous weather phenomena, including hail, is becoming more and more popular not only for services to combat hail, but also for other sectors of the national economy. This is facilitated by the intensification of weather processes due to climate warming and the operational availability of the results of modeling the Earth's atmosphere, in particular, the values of stratification according to the global model (GFS NCEP). This paper discusses the possibility of predicting hail with a lead time of 132 hours using discriminant analysis. The success of the hail forecast is assessed by the criteria of the forecast quality. Materials and methods of research. The research materials were the output data of the global atmospheric model GFS NCEP with a lead time of 132 hours. Discriminant functions were used to predict the phenomenon of «hail» and «non-hail». The results of the forecast were compared with the data of observations on the fall of hail provided by the paramilitary services for active influence on meteorological and other geophysical processes, located within the radius of representativeness of the actual data of the aerological sounding at the Mineralnye Vody station. To assess the success of the hail forecast, the conjugacy table is compiled and the forecast quality criteria are calculated. Research results and their discussion. The results of the calculations showed that the hail forecast with a lead time of 132 hours meets all the criteria for the quality of forecasts. The forecast success rates were good. Thus, the accuracy of the hail forecast was ~ 70 %. Conclusion. Studies have shown that the proposed approach to forecasting hail from the data of the global atmospheric model does not lead to a noticeable decrease in the quality of forecasts when the lead time is increased to five days.

global model of the atmosphere, aerological sounding, lead time, meteorological parameters, discriminant analysis, forecast of hazardous phenomena

1. Кагермазов А. Х. цифровая атмосфера. современные методы и методология исследования опасных метеорологических процессов и явлений. Нальчик: Печатный двор. 2015. 215 с.

2. Kagermazov A. Kh., Sozaeva L. T. Validation of the output of the global atmospheric model on days with the development of dangerous convective phenomena according to aerological sounding with a two-day lead time // VIII All-Russian Conference on Atmospheric Electricity: Journal of Physics: Conference Series. 2020. №1604. doi:10.1088/1742-6596/1604/1/012011.

3. Кагермазов А. Х., Созаева Л. Т. Балидация выходных данных глобальной модели атмосферы, полученных с заблаговременностью до трех суток, по данным аэрологического зондирования // Материалы VI Всероссийской научной конференции «Проблемы военно-прикладной геофизики и контроля состояния природной среды» (г. Санкт-Петербург, 16-18 сентября 2020 г.). СПб.: БКА им. А.Ф. Можайского. 2020. 131-136 с.

4. Кагермазов А. Х., Созаева Л. Т. Балидация выходных данных глобальной модели атмосферы по данным аэрологического зондирования с нарастающей заблаговременностью // Наука. Инновации. Технологии. 2020. № 4. 137-148 с.

5. Kagermazov A. Kh. The forecast of hail based on the atmospheric global model (T254 NCEP) output data // Russian Meteorology and Hydrology. 2012. № 37. C. 165-169.

6. Кагермазов А.Х., Созаева Л.Т. Прогноз града с заблаговременностью до трех суток по выходным данным глобальной модели атмосферы. Труды ГГО. 2020. Вып. 598. 204-214 с.

7. Kalnay E., Kanamitsu M., and Baker W.E. Global numerical weather prediction at the National Meteorological Center // Bull. Amer. Meteor. Soc. 1990. Vol.71. 1410-1428 p.

8. Kanamitsu M. Description of the NMC global data assimilation and forecast system // Weather and Forecasting. 1989. Vol. 4. 335-342 p.

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