| contributor author | Krasnopolsky, Vladimir M. | |
| contributor author | Fox-Rabinovitz, Michael S. | |
| contributor author | Chalikov, Dmitry V. | |
| date accessioned | 2017-06-09T17:27:34Z | |
| date available | 2017-06-09T17:27:34Z | |
| date copyright | 2005/12/01 | |
| date issued | 2005 | |
| identifier issn | 0027-0644 | |
| identifier other | ams-85626.pdf | |
| identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4229094 | |
| description 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. | |
| publisher | American Meteorological Society | |
| title | Reply | |
| type | Journal Paper | |
| journal volume | 133 | |
| journal issue | 12 | |
| journal title | Monthly Weather Review | |
| identifier doi | 10.1175/MWR3079.1 | |
| journal fristpage | 3724 | |
| journal lastpage | 3729 | |
| tree | Monthly Weather Review:;2005:;volume( 133 ):;issue: 012 | |
| contenttype | Fulltext | |
