V. Krasnopolsky

发表

Introduction to Mapping and Neural Networks

V. Krasnopolsky, 2013 .

R. DeVore, P. Binev, P. Lamby, 2013 .

Neural network emulations for complex multidimensional geophysical mappings: Applications of neural network techniques to atmospheric and oceanic satellite retrievals and numerical modeling

Vladimir M. Krasnopolsky, V. Krasnopolsky, 2007 .

Using Machine Learning for Model Physics: an Overview

V. Krasnopolsky, 2020, 2002.00416.

A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager

V. Krasnopolsky, L. Breaker, W. Gemmill, 1995 .

The Feasibility of Estimating Ocean Surface Currents on an Operational Basis Using Satellite Feature Tracking Methods

V. Krasnopolsky, L. Breaker, X-H. Yan, 1994 .

New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model

D. Chalikov, V. Krasnopolsky, M. Fox-Rabinovitz, 2005 .

Neural Network Applications to Solve Forward and Inverse Problems in Atmospheric and Oceanic Satellite Remote Sensing

Vladimir M. Krasnopolsky, V. Krasnopolsky, 2009 .

Neural network approximations for nonlinear interactions in wind wave spectra: direct mapping for wind seas in deep water

Vladimir M. Krasnopolsky, Hendrik L. Tolman, Dmitry Chalikov, 2005 .

A Neural Network Multiparameter Algorithm for SSM/I Ocean Retrievals

Vladimir M. Krasnopolsky, Laurence C. Breaker, William H. Gemmill, 2000 .

A multi-parameter empirical ocean algorithm for SSM/I retrievals

V. M. Krasnopolsky, L. C. Breaker, V. Krasnopolsky, 1999 .

The Use of SSM/I Data in Operational Marine Analysis*

Vladimir M. Krasnopolsky, William H. Gemmill, V. Krasnopolsky, 1999 .

A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US

Vladimir M. Krasnopolsky, Ying Lin, V. Krasnopolsky, 2012 .

A neural network technique to improve computational efficiency of numerical oceanic models

Vladimir M. Krasnopolsky, Hendrik L. Tolman, Dmitry Chalikov, 2002 .

Improving NCEP’s global-scale wave ensemble averages using neural networks

S. Penny, V. Krasnopolsky, R. Campos, 2020 .

USE OF NEURAL NETWORKS TO IMPROVE COMPUTATIONAL EFFICIENCY OF ENVIRONMENTAL NUMERICAL MODELS

H. Tolman, D. Chalikov, V. Krasnopolsky, 2001 .

NN Ensembles and Their Applications

V. Krasnopolsky, 2013 .

Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather

V. Krasnopolsky, 2013 .

Robustness of the NN Approach to emulating atmospheric long wave radiation in complex climate models

V. Krasnopolsky, M. Fox-Rabinovitz, M. Chou, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Using Neural Network Technique to Enhance Assimilating Surface Satellite Observation into Numerical Models

Hae-Cheol Kim, C. Lozano, V. Krasnopolsky, 2015 .

Self-consistent calculation of the interactions of lightest nuclei with 6Li

V. Krasnopolsky, V. Kukulin, Y. Nakao, 1992 .

Complex hybrid models combining deterministic and machine learning components as a new synergetic paradigm in numerical climate modeling and weather prediction

V. Krasnopolsky, M. Fox-Rabinovitz, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Using Machine Learning for Model Physics: an Overview

V. Krasnopolsky, Aleksei A. Belochitski, 2020, 2002.00416.

Assessments of Surface Winds and Waves from the NCEP Ensemble Forecast System

S. Penny, V. Krasnopolsky, R. Campos, 2018, Weather and Forecasting.

2006 Special issue: Earth Sciences and Environmental Applications of Computational IntelligenceIntroduction

Julio J. Valdés, Dimitri P. Solomatine, Vladimir Cherkassky, 2006, Neural Networks.

Computational intelligence in earth and environmental sciences

Julio J. Valdés, Dimitri P. Solomatine, Vladimir Cherkassky, 2007, Neural Networks.

Accurate and Fast Neural Network Emulations of Long and Short Wave Radiation for the NCEP Global Forecast System Model 1

S. Lord, V. Krasnopolsky, A. Belochitski, 2012 .

Padé-approximant techniques for processing scattering data II. Energy-dependent phase-shift analysis of low-energy4He +2H scattering

J. Horáček, V. Krasnopolsky, V. Kukulin, 1990 .

Computational intelligence in earth sciences and environmental applications: Issues and challenges

Julio J. Valdés, Dimitri P. Solomatine, Vladimir Cherkassky, 2006, Neural Networks.

The problem of AVHRR image navigation revisited

V. M. Krasnopolsky, L. C. Breaker, V. Krasnopolsky, 1994 .

Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data

Xiaofeng Li, John Sapper, Pablo Clemente-Colón, 2001 .

Construction ofS-matrix from scattering data

J. Horáček, V. Krasnopolsky, V. Kukulin, 1985 .

Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements

Helmut Schiller, Vladimir M. Krasnopolsky, V. Krasnopolsky, 2003, Neural Networks.

Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges

Vladimir M. Krasnopolsky, Sid-Ahmed Boukabara, Eric Maddy, 2019, Bulletin of the American Meteorological Society.

The Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings

Vladimir M. Krasnopolsky, V. Krasnopolsky, 2013 .

Using Artificial Neural Networks to Improve CFS Week 3-4 Precipitation and 2-Meter Air Temperature Forecasts

Chung-Yu Wu, H. V. D. Dool, J. Gottschalck, 2021, Weather and Forecasting.

Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences

S. E. Haupt, K. Casey, A. McGovern, 2020, Bulletin of the American Meteorological Society.

Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental numerical models

Vladimir M. Krasnopolsky, Frédéric Chevallier, F. Chevallier, 2003, Neural Networks.

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

Vladimir M. Krasnopolsky, Sudhir Nadiga, Avichal Mehra, 2015, Comput. Intell. Neurosci..

Nonlinear Wave Ensemble Averaging in the Gulf of Mexico Using Neural Networks

Stephen G. Penny, Jose-Henrique G. M. Alves, Ricardo Martins Campos, 2019, Journal of Atmospheric and Oceanic Technology.

Stable Emulation of an Entire Suite of Model Physics in a State-of-the-Art GCM using a Neural Network

V. Krasnopolsky, A. Belochitski, 2021, 2103.07028.

Accurate and Fast Neural Network Emulations of Model Radiation for the NCEP Coupled Climate Forecast System: Climate Simulations and Seasonal Predictions*

Stephen J. Lord, Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, 2010 .

Tree approximation of the long wave radiation parameterization in the NCAR CAM global climate model

Ronald A. DeVore, Vladimir M. Krasnopolsky, Peter Binev, 2011, J. Comput. Appl. Math..

Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, Alexei A. Belochitski, 2013, Adv. Artif. Neural Syst..

Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction

Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, V. Krasnopolsky, 2006, Neural Networks.

Adjusting Neural Network to a Particular Problem: Neural Network-Based Empirical Biological Model for Chlorophyll Concentration in the Upper Ocean

Vladimir M. Krasnopolsky, Sudhir Nadiga, Avichal Mehra, 2018, Appl. Comput. Intell. Soft Comput..

Using neural network emulations of model physics in numerical model ensembles

Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, Alexei A. Belochitski, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Decadal Climate Simulations Using Accurate and Fast Neural Network Emulation of Full, Longwave and Shortwave, Radiation*

Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, Alexei A. Belochitski, 2008 .

Introduction to Nonlinear Statistics and Neural Networks

Vladimir Krasnopolsky, V. Krasnopolsky, 2010 .

Robustness of Neural Network Emulations of Radiative Transfer Parameterizations in a State-of-the-Art General Circulation Model

V. Krasnopolsky, A. Belochitski, 2021, Geoscientific Model Development.

A Machine Learning Outlook: Post-processing of Global Medium-range Forecasts

C. Bromberg, Jason Hickey, E. Maddy, 2023, arXiv.org.

Development of neural network convection parameterizations for numerical climate and weather prediction models using cloud resolving model simulations

Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, Alexei A. Belochitski, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Atmospheric and Oceanic Remote Sensing Applications

Vladimir M. Krasnopolsky, V. Krasnopolsky, 2013 .

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

Vladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, V. Krasnopolsky, 2006 .

Using adaptive learning techniques for fast and accurate approximation of physics in numerical models

V.M. Krasnopolsky, M. Fox-Rabinovitz, D. Chalikov, 2003, The 3rd International Workshop on Scientific Use of Submarine Cables and Related Technologies, 2003..

Using Neural Network parameterizations of the Nonlinear Energy Transfer for Application in Wave Models

Hendrik L. Tolman, Vladimir M. Krasnopolsky, Dmitry Chalikov, 2001 .

Neural Network Applications to Developing Hybrid Atmospheric and Oceanic Numerical Models

Vladimir M. Krasnopolsky, V. Krasnopolsky, 2009 .

How to deal with inhomogeneous outputs and high dimensionality of neural network emulations of model physics in numerical climate and weather prediction models

Stephen J. Lord, Vladimir M. Krasnopolsky, Todd Spindler, 2009, 2009 International Joint Conference on Neural Networks.

Decadal Climate Simulations Using Accurate and Fast Neural Network Emulation of Full, Longwave and Shortwave, Radiation*

V. Krasnopolsky, M. Fox-Rabinovitz, A. Belochitski, 2008 .