Numerical Experiments for Central Paris with Urbanized Large-Eddy Simulation Model PALM (MEGAPOLI Del 2.4)
|Title||Numerical Experiments for Central Paris with Urbanized Large-Eddy Simulation Model PALM (MEGAPOLI Del 2.4)|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Tertiary Title||NewsLetters of the FP7 EC MEGAPOLI Project|
Micrometeorology, city comfort, land use management and air quality monitoring increasingly become important environmental issues. To serve the needs, meteorology needs to achieve a serious advance in representation and forecast on micro-scales (meters to 100 km) called “meteorological terra incognita”. There is a suitable numerical tool, namely, the large-eddy simulation modelling (LES) to support the development. However, at present, the LES is of limited utility for applications as it cannot start from (i) scarcely measured atmospheric conditions and (ii) it cannot account for unresolved surface details. This study presents an analysis of several numerical experiments with the LESNIC LES code. The experiments were aimed to test the prospective ways to improve the LES utility for the applied problems. The study addresses two problems. First, the data assimilation problem on micro-scales is investigated as a possibility to recover the turbulent fields consistent with the mean meteorological profiles. Second, the methods to incorporate of the unresolved surface structures are investigated in a priopi numerical experiments. The numerical experiments demonstrated that the simplest nudging or Newtonian relaxation technique for the data assimilation is applicable on the turbulence scales. It is also shown that the filtering property of the three layers’ artificial neural network (ANN) can be used for formulation of the surface stress from the unresolved surface features. Introduction of independently trained ANN for each of dynamical sub-regions in the LES domain could greatly reduce computer time needed to estimate closure coefficients through omitting multi-layer explicit filtering in the dynamic closure. Moreover, the ANN is shown to be a robust predictor for scalar concentrations in the urban sub-layer with unresolved scalar sources.
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