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On the Modelling of the Surface Meterological Variable Diurnal Cycles by Combined „Fuzzy Sets and Neural Networks“
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Datum
1998
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Metropolis
Zusammenfassung
The joint statistical distribution of principal meteorological variables (temperature of air and soil, humidity of air and soil, atmospheric precipitation, pressure, shortwave radiation, cloudiness) are investigated. Ten years data sets of one hour temporal resolution for several meteorological sites of Russian North-West region are used. The data set of simultaneous observations ( for all variables, seasons and sites ) allows to reveal main features of joint diurnal distributions by means of the fuzzy set approach. Known and novel interrelationships between various meteorological and connected soil variables are reviewed. The revealed relationships between solar downward radiative fluxes and soil temperature diurnal patterns allow to simulate all principal elements of surface energy exchange :longwave outgoing radiative fluxes in the atmosphere, soil heat fluxes, sensible, turbulent and latent heat fluxes. It was found out that the most complicated links take place for fuzzy sets, corresponding to fractional cloudness diurnal patterns. There is an assymmetrical relationship between solar daily sums of radiance for half cloudy day and mean soil temperature values. That is a main cause for simulation of meteorological and heat balance component diurnal cycles in most ecological models. Introduced stationary and transition modes for main meteorological variable diurnal patterns represent the background for simulation of all known weather phenomena. This modes are used also as a neural network’s nodes for hidden layers. Implementation of neural networks (back propagation algorithm) allows to perform several modelling experiments. Problem of optimum network configuration ( number of nodes in hidden layers ) is discussed. For example, it is possible to reconstruct the diurnal cycles of some meteorological variables with sufficient ( at the observational error levels ) precisions for each of them,by using 1, 2 or 3 instantaneous meteorological observations. Adapted nonlinear mapping of one group variables (and its diurnal distributions) on another one allow to investigate the possibility of application of this approach for diagnostical aims.
Potential applications of the developed approach are : downscaling in global models; spatial field reconstructions in case of missing observational data ( for some variable(s)); short-term forecasting; parametrization of energy exchange processes at the surface of the Earth.