Article Type:
Reply

Reply

Vladimir M. Krasnopolsky NDAA/NCEP/SAIC, Camp Springs, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Michael S. Fox-Rabinovitz Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Dmitry V. Chalikov Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Print Publication:
01 Dec 2005
DOI:
https://doi.org/10.1175/MWR3079.1
Page(s):
3724–3729
Restricted access

Abstract

This reply is aimed at clarifying and further discussing the methodological aspects of this neural network application for a better understanding of the technique by the journal readership. The similarities and differences of two approaches and their areas of application are discussed. These two approaches outline a new interdisciplinary field based on application of neural networks (and probably other modern machine or statistical learning techniques) to significantly speed up calculations of time-consuming components of atmospheric and oceanic numerical models.

Corresponding author address: Vladimir Krasnopolsky, NOAA/ NCEP/SAIC, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: Vladimir.Krasnopolsky@noaa.gov

Abstract

This reply is aimed at clarifying and further discussing the methodological aspects of this neural network application for a better understanding of the technique by the journal readership. The similarities and differences of two approaches and their areas of application are discussed. These two approaches outline a new interdisciplinary field based on application of neural networks (and probably other modern machine or statistical learning techniques) to significantly speed up calculations of time-consuming components of atmospheric and oceanic numerical models.

Corresponding author address: Vladimir Krasnopolsky, NOAA/ NCEP/SAIC, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: Vladimir.Krasnopolsky@noaa.gov

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