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Вероятностно-географический прогноз опасных явлений погоды

Вероятностно-географический прогноз опасных явлений погоды

Abstract

The scheme of the method of probabilistic-geographical prediction of dangerous weather phenomena. Discusses two basic stages of realization of the proposed method. It is emphasized that the method responds to topical issues of modern weather forecasting and its separate phenomena: the prediction is for a specific point in space and at a certain point in time. The basic condition for building scientific diagram method is evidence a statement of quasistationary frequency chronological series of severe weather events and the possibility of using the properties of the Markov chain for the implementation of forecast probabilities on the basis of neuromodulatory. Physical essence of the ideas presented in successive reflection of the influence on the formation of dangerous weather phenomena-scale synoptic and geophysical processes that have been predictor role. A predictive model based on the analysis of long chronological series of severe weather events and neuromodulatory made with due account of regularity of the network of meteorological stations, which is important in modern operational synoptic practice. Actually forecast probability of dangerous weather phenomena is carried out with the help of neural network modeling with the obligatory account of such predictors: indexes transfer, meteorological parameters, the Laplacian pressure, the activity rate of the magnetic field of the Earth.

Предложена схема метода вероятностно-географического прогноза опасных явлений погоды. Обсуждаются два основных этапа реализации предложенного метода. Подчеркивается, что разработанный метод отвечает актуальным вопросам современного прогнозирования погоды и отдельных ее явлений: предсказание осуществляется для конкретной точки в пространстве и в определенный момент времени. Базовым условием построения научной схемы метода является доказательное утверждение о квазиустойчивости частот хронологических рядов опасных явлений погоды и возможности использования свойств марковской цепи для осуществления прогнозирования их вероятностей на основе нейромоделирования. Физическая суть идеи представлена в последовательном отражении влияния на формирование опасных явлений погоды разномасштабных синоптических и геофизических процессов, которые переведены в предикторную роль. Прогностическая модель на основе анализа длительных хронологических рядов опасных явлений погоды и нейромоделирования выстроена с учетом нерегулярности сети метеорологических станций, что важно в современной оперативной синоптической практике. Собственно прогноз вероятностей опасных явлений погоды осуществляется с помощью нейросетевого моделирования при обязательном учете таких предикторов, как индексы переноса, метеорологические параметры, лапласиан давления, коэффициент активности магнитного поля Земли.

Keywords

СИНОПТИЧЕСКИЕ ПРОЦЕССЫ, ПОВТОРЯЕМОСТЬ, ПРЕДИКТОРЫ, НЕЙРОМОДЕЛИРОВАНИЕ, МАРКОВОСТЬ ЦЕПИ., MARKOVES CHAIN.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold