Direct and Inverse Problems in a Variational Concept of Environmental Modeling |
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Authors: | Penenko Vladimir Baklanov Alexander Tsvetova Elena Mahura Alexander |
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Institution: | (1) Institute of Computational Mathematics and Mathematical Geophysics (ICM&MG), Siberian Branch of the Russian Academy of Sciences, 6, Prospekt Lavrentieva, Novosibirsk, 630090, Russia;(2) Research Department, Danish Meteorological Institute (DMI), Lyngbyvej 100, 2100 Copenhagen, Denmark;; |
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Abstract: | A concept of environmental forecasting based on a variational approach is discussed. The basic idea is to augment the existing
technology of modeling by a combination of direct and inverse methods. By this means, the scope of environmental studies can
be substantially enlarged. In the concept, mathematical models of processes and observation data subject to some uncertainties
are considered. The modeling system is derived from a specially formulated weak-constraint variational principle. A set of
algorithms for implementing the concept is presented. These are: algorithms for the solution of direct, adjoint, and inverse
problems; adjoint sensitivity algorithms; data assimilation procedures; etc. Methods of quantitative estimations of uncertainty
are of particular interest since uncertainty functions play a fundamental role for data assimilation, assessment of model
quality, and inverse problem solving. A scenario approach is an essential part of the concept. Some methods of orthogonal
decomposition of multi-dimensional phase spaces are used to reconstruct the hydrodynamic background fields from available
data and to include climatic data into long-term prognostic scenarios. Subspaces with informative bases are constructed to
use in deterministic or stochastic-deterministic scenarios for forecasting air quality and risk assessment. The results of
implementing example scenarios for the Siberian regions are presented. |
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