Analysis of the relationship between banded orographic convection and atmospheric properties using factorial discriminant analysis and neural networks

摘要

The relationship between banded orographic convection and atmospheric properties is investigated for a region in the south of France where the associated rainfall events are thought to represent a significant portion of the hydrologic input. The purpose is to develop a method capable of producing an extensive data base of banded orographic convection rainfall events from atmospheric sounding data for this region where insufficient raingage data and little or no suitable radar or satellite data is available. Two statistical methods (Discriminant Factorial Analysis (DFA) and Neural Networks (NN)) are used to determine 16 so-called elaborated nonlinear variables that best identify rainfall events related to banded orographic convection from atmospheric soundings. The approach takes rainfall information into account indirectly because it learns from the results of a previous study that explored meteorological and available rainfall data bases, even if incomplete. The new variables include wind shear, low level moisture fluxes and gradients of the potential temperature in the lower layers of the atmosphere, and were used to create an extensive data base of banded orographic convection events from the archive of atmospheric soundings. Results of numerical simulations using the MesoNH meteorological model validate this approach and offer interesting perspectives for the understanding of the physical processes associated with banded orographic convection. DFA proves to be useful to determine the most discriminant factors with a physical meaning. NN provides better results, but does not allow physical interpretation. The best solution is therefore to use the two methods together.