A Model for Predicting the Amount of Photosynthetically Available Radiation from BGC-ARGO Float Observations in the Water Column

Frederic Theodor Stahl, Lars Nolle, Ahlem Jemai, Oliver Zielinski

In: Ibrahim Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat (Hrsg.). Communications of the ECMS. European Conference on Modelling and Simulation (ECMS-2022) May 30-June 3 Alesund Norway Seiten 174-180 Communications on Modelling and Simulation 36 1 ISBN 978-3-937436-77-7 ECMS 6/2022.


Modern oceanography uses, amongst other platforms, automated diving devices, which are drifting with the ocean current whilst continuously collecting vertical profiles of environmental parameters. One of the important parameters is photosynthetically available radiation (PAR). It was studied in this work whether the PAR values can be reconstructed by combinations of measurements from the remaining onboard sensors with specific wavelength. If a reconstruction of PAR is possible, this would allow allocating the sensor with a further specific wavelength instead of PAR. Having available more spectral information could for example enable natural scientists to better distinguish phytoplankton or UV radiation. Therefore, data from three different expeditions from different regions of the world were used to model PAR using multiple linear regression and regression trees (RT). Multiple linear regression achieved an R2 value of 0.970 for the combined dataset and RT achieved an R2 value of 0.960. Hence, the models are accurate enough to predict the PAR parameter without the need for a dedicated PAR sensor. Thus the PAR sensor reading could be replaced with measurements of an additional wave length.

2022_ECMS_PAR_cameraReady.pdf (pdf, 605 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence