Using neural networks to describe tracer correlations
Item
Title (Dublin Core)
Using neural networks to describe tracer correlations
Description (Dublin Core)
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 CH<sub>4</sub> 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.
Creator (Dublin Core)
Lary, D. J.
Müller, M. D.
Mussa, H. Y.
Date (Dublin Core)
2018-07-10
Type (Dublin Core)
Text
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
10.5194/acp-4-143-2004
https://acp.copernicus.org/articles/4/143/2004/
Source (Dublin Core)
eISSN: 1680-7324
Language (Dublin Core)
eng
