Assessment of high (diurnal) to low (seasonal) frequency variations of isoprene emission rates using a neural network approach C. Boissard, F. Chervier, and 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
Abstract. 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 T0 and photosynthetic photon flux density L0)
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 (T21) and to L0 and T0 variations. T21, T0 and L0 only accounts for 76% of the
Citation: Boissard, C., Chervier, F., and Dutot, A. L.: Assessment of high (diurnal) to low (seasonal) frequency variations of isoprene emission rates using a neural network approach, Atmos. Chem. Phys., 8, 2089-2101, doi:10.5194/acp-8-2089-2008, 2008.