Stochastic modeling of meteorological elements for solving environmental monitoring problems

Abstract

The purpose of the research presented in  the  publication is  developing the  stochastic model  for simulating the series of meteorological elements used in predicting for transferring of biogenic elements into water bodies from the territory of agricultural land. The object of study is artificial series of meteorological elements with given statistical characteristics. As initial data, the meteorological data of the State Institution “Republican Hydrometeorological Center” for the Minsk object obtained for the period 2000–2020  were used. For development of the stochastic model, the following indicators were used: daily amount of total solar radiation, maximum and minimum air temperature, relative air humidity, amount of atmospheric precipitation, number of days with precipitation, maximum half-hour share of precipitation, wind speed at a height of 10 m above the earth’s surface. Artificial meteorological series are obtained on the basis of a sample set of randomly generated daily values with using the Monte Carlo method. The stochastic component of precipitation generation is a Markov chain gamma model; total precipitation – inverse method of two-parameter gamma distribution; relative air humidity – Simpson distribution; wind speed – inverse Weibull distribution method; the minimum and maximum air temperatures and solar radiation are determined using the Matalas first-order recurrent filter. For each of the elements, a differentiated correspondence of the statistical parameters determined from        the measured and calculated series of meteorological elements were proved. The results of investigation can be used for modeling and forecasting of weather in areas with limited meteorological observation data for solving applied problems in biological and ecological systems.