Vladimir M. Krasnopolsky

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A new approach based on a synergetic combination of statistical/machine learning and deterministic modeling within atmospheric models is presented. The approach uses neural networks as a statistical or machine learning technique for an accurate and fast emulation or statistical approximation of model physics parameterizations. It is applied to development(More)
This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty,(More)
[1] A group of geophysical applications, which from the mathematical point of view, can be formulated as complex, multidimensional, nonlinear mappings and which in terms of the neural network (NN) technique, utilize a particular type of NN, the multilayer perceptron (MLP), is reviewed in this paper. This type of NN application covers the majority of NN(More)
A new generic neural network (NN) application-improving computational efficiency of certain processes in numerical environmental models-is considered. This approach can be used to accelerate the calculations and improve the accuracy of the parameterizations of several types of physical processes which generally require computations involving complex(More)
A new application of the NN ensemble approach is presented. It is applied to NN emulations of model physics in complex numerical climate models, and aimed at improving the accuracy of climate simulations. In particular, this approach is applied to NN emulations of the long wave radiation of the widely used National Center for Atmospheric Research Community(More)
A generic approach that allows extracting functional nonlinear dependencies and mappings between atmospheric or ocean state variables in a relatively simple form is presented. These dependencies and mappings between the 2-and 3D fields of the prognostic and diagnostic variables are implicitly contained in the highly nonlinear coupled partial differential(More)
In this paper the use of the neural network emulation technique, developed earlier by the authors, is investigated in application to ensembles of general circulation models used for the weather prediction and climate simulation. It is shown that the neural network emulation technique allows us: (1) to introduce fast versions of model physics (or components(More)
The developing neural retina expresses a set of extracellular proteases including plasminogen activator and gelatinases. Since neurites of retina cells cultured on fluorescent gelatin digest the substrate in their paths, we have suggested that the proteases are used by the tips of growing fibers to allow them to migrate within the mass of the tissue in(More)
A broad class of neural network (NN) applications dealing with the remote measurements of geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and land surface is presented. In order to infer these parameters from remote sensing (RS) measurements, standard retrieval and variational techniques are applied. Both techniques(More)
A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing(More)