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Micrometeorology, city comfort, land use management and air qualitymonitoring increasingly become important environmental issues. To serve theneeds, meteorology needs to achieve a serious advance in representation andforecast on micro-scales (meters to 100 km) called meteorological terraincognita. There is a suitable numerical tool, namely, the large-eddysimulation modelling (LES) to support the development. However, at present, theLES is of limited utility for applications. The study addresses two problems.First, the data assimilation problem on micro-scales is investigated as apossibility to recover the turbulent fields consistent with the meanmeteorological profiles. Second, the methods to incorporate of the unresolvedsurface structures are investigated in a priopi numerical experiments. Thenumerical experiments demonstrated that the simplest nudging or Newtonianrelaxation technique for the data assimilation is applicable on the turbulencescales. It is also shown that the filtering property of the three layersartificial neural network (ANN) can be used for formulation of the surfacestress from the unresolved surface features.

Numerical experiments with assimilation of the mean and unresolved meteorological conditions into large-eddy simulation model