Atmospheric Chemistry and Physics
www.atmos-chem-phys.net
1680-7316
1680-7324
8
7
2008
10.5194/acp-8-2089-2008
http://www.atmos-chem-phys.net/8/2089/2008/
http://www.atmos-chem-phys.net/8/2089/2008/acp-8-2089-2008.html
http://www.atmos-chem-phys.net/8/2089/2008/acp-8-2089-2008.pdf
2089
2101
2008-04-11
Assessment of high (diurnal) to low (seasonal) frequency variations of isoprene emission rates using a neural network approach
C. Boissard
boissard@lisa.univ-paris12.fr
F. Chervier
A. L. Dutot
Laboratoire Interuniversitaire des Systèmes Atmosphériques, Universités Paris 12 et Paris 7, CNRS, 61 avenue du Général de Gaulle, 94010 Créteil Cedex, France
Using a statistical approach based on artificial neural networks, an
emission algorithm (ISO-LF) accounting for high to low frequency variations
was developed for isoprene emission rates. ISO-LF was optimised using a data
base (ISO-DB) specifically designed for this work, which consists of 1321
emission rates collected in the literature and 34 environmental variables,
measured or assessed using National Climatic Data Center or National Centers
for Environmental Predictions meteorological databases. ISO-DB covers a
large variety of emitters (25 species) and environmental conditions (10° S to 60° N).
When only instantaneous environmental regressors
(instantaneous air temperature <i>T0</i> and photosynthetic photon flux density <i>L0</i>)
were used, a maximum of 60% of the overall isoprene variability was
assessed with the highest emissions being strongly underestimated. ISO-LF
includes a total of 9 high (instantaneous) to low (up to 3 weeks) frequency
regressors and accounts for up to 91% of the isoprene emission
variability, whatever the emission range, species or climate investigated.
ISO-LF was found to be mainly sensitive to air temperature cumulated over 3
weeks (<i>T21</i>) and to <i>L0</i> and <i>T0</i> variations. <i>T21</i>, <i>T0</i> and <i>L0</i> only accounts for 76% of the
overall variability.
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