Articles | Volume 17, issue 20
https://doi.org/10.5194/gmd-17-7347-2024
https://doi.org/10.5194/gmd-17-7347-2024
Model description paper
 | 
16 Oct 2024
Model description paper |  | 16 Oct 2024

PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks

Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini

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Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model
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Cited articles

Ahmad, H.: Machine learning applications in oceanography, Aquat. Res., 2, 161–169, 2019. a
Alvera-Azcárate, A., Barth, A., Rixen, M., and Beckers, J.-M.: Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature, Ocean Modell., 9, 325–346, 2005. a
Amadio, C., Teruzzi, A., Feudale, L., Bolzon, G., Di Biagio, V., Lazzari, P., Álvarez, E., Coidessa, G., Salon, S., and Cossarini, G.: Mediterranean Quality checked BGC-Argo 2013-2022 dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.10391759, 2023. a, b
Amadio, C., Teruzzi, A., Pietropolli, G., Manzoni, L., Coidessa, G., and Cossarini, G.: Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model, Ocean Sci., 20, 689–710, https://doi.org/10.5194/os-20-689-2024, 2024. a, b
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), https://doi.org/10.17882/42182, 2000. a, b
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Short summary
Monitoring the ocean is essential for studying marine life and human impact. Our new software, PPCon, uses ocean data to predict key factors like nitrate and chlorophyll levels, which are hard to measure directly. By leveraging machine learning, PPCon offers more accurate and efficient predictions.
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