Efficient ANN Training for the Reconstruction of Isotopic Time Series
Atmospheric circulation plays a major role in the stable isotopic composition of precipitation. In this study, the relationship between the synoptic patterns and the stable isotopic composition (δ18O & δ2H) of precipitation is investigated using event-based rainfall data. The aim of this paper is the generation of isotopic time series using a combined synoptic classification technique. Using the classification software developed within the COST733 action, we generated synoptic catalogues utilizing various classification methods for two meteorological parameters: the geopotential height at the isobaric level of 500 hPa and the thickness (500–1,000 hPa) and we propose an efficient technique to generate a representative classification catalogue based on the above parameters. An ANN is trained using this catalogue in order to classify each one of the meteorological parameters. The output scheme is compared with the initial catalogues of the COST733 action using statistical indices both in terms of explaining the variance of the classified meteorological fields and in terms of providing classes with statistically distinct isotopic signatures. Finally, using the proposed classification, the isotopic composition of the synoptic classes is determined and used to reconstruct isotopic time series.
KeywordsIsotopic Composition Majority Classification Stable Isotopic Composition Combine Classification Synoptic Type
This study is funded by the Research Committee of the University of Patras under the ‘K. Karatheodori’ grand (Project No. C.907).
- IAEA (2010) Isotope Hydrology Information System: The ISOHIS database. Available from: http://isohis.iaea.org