Тemperature risk and estimated probability of its occurrence in the Altai regionand the Republic of Altai

At present the hydrometeorological risk is an urgent problem of research.Hydrometeorological risk is defined as the statistical probability of abnormal or violent weather occurrence. The calculatedprobability of hazardous events determines economic and social decisions. In this article the way to assess the temperature risk is offered,based on a Markov chain by example of Altai stations. Temperature risks, caused by the following violent weather, were considered:periods of low/high temperature, abnormal cold/hot weather, heavy frost/strong heat. In order to optimise the presentation of thematerial and find homogeneous climatic changes in the Altai, the temperature fields were classified. It helped to allocate four classes,with expressed geographical location, reflecting the common factors that form them. Temperature risk was estimated for the followingstations: Barnaul, Aleyskaya, Chemal, Ust-Koks and Kosh-Agach characterizing the selected classes. The following characteristics wereestimated: periodicity and duration of violent weather, probability of its emergence in certain months. Application of a Markov chainallowed estimating continuous duration of violent weather, possible once in a particular period of time. The research reveals that thefrequency of abnormal cold weather in the Altai accounts for 4-7%, abnormal heat weather - 0.6-1.9%, periods of low temperature -from 0.4 to 11.4%, periods of "heavy frost" - 4%, periods of "strong heat" - about 6-7%. Uses of a Markov chain algorithm allow estimatingprognostic continuous duration of periods with hazard gradation. For example, the Aleyskaya station witnesses abnormally hotweather in the course of seven days every 50 years (in July), the period of low temperature can last for 13 days running, heavy frost -for seven days, strong heat - for six days. The Kosh-Agach station witnesses a 19-day period of low temperature every 30 years, heavyfrost lasts for 12 days, strong heat - one day, respectively. The temperature risk forecast, based on a Markov chain, rates to the probabilityclimatological forecasts, which can improve the quality of meteorological information coverage for different branches of economy.

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