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Details for article 18 of 18 found articles
| Title: |
Using neural networks to describe tracer correlations |
| Author: |
D. J. Lary M. D. Müller H. Y. Mussa |
| Appeared in: |
Atmospheric chemistry and physics |
| Paging: | Volume 4 (2004) nr. 1 pages 143-146 |
| Year: |
2004 |
| Contents: |
Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH<sub>4</sub>-N<sub>2</sub>O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH<sub>4</sub>-N<sub>2</sub>O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH<sub>4 </sub> (but not N<sub>2</sub>O) from 1991 till the present. The neural network Fortran code used is available for download. |
| Publisher: |
Copernicus GmbH (provided by DOAJ) |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 18 of 18 found articles
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