Articles | Volume 17, issue 20
https://doi.org/10.5194/gmd-17-7347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Oct 2024
Model description paper |
| 16 Oct 2024
| 16 Oct 2024
PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
Gloria Pietropolli
CORRESPONDING AUTHOR
National Institute of Oceanography and Applied Geophysics – OGS, Trieste, Italy
Dipartimento di Matematica, Informatica e Geoscienze, University of Trieste, Trieste, Italy
Luca Manzoni
National Institute of Oceanography and Applied Geophysics – OGS, Trieste, Italy
Dipartimento di Matematica, Informatica e Geoscienze, University of Trieste, Trieste, Italy
Gianpiero Cossarini
National Institute of Oceanography and Applied Geophysics – OGS, Trieste, Italy
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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
Baldi, P. and Sadowski, P. J.: Understanding dropout, Adv. Neur. Inf., 26, 2814–2822, 2013. a
Barth, A., Alvera-Azcárate, A., Licer, M., and Beckers, J.-M.: DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations, Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, 2020. a
Bittig, H., Wong, A., and Plant, J.: BGC-Argo synthetic profile file processing and format on Coriolis GDAC, Version 1.1, https://doi.org/10.13155/55637, 2019. a
Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzède, R., Körtzinger, A., and Gattuso, J.-P.: An alternative to static climatologies: Robust estimation of open ocean CO2 variables and nutrient concentrations from T, S, and O2 data using Bayesian neural networks, Front. Mar. Sci., 5, https://doi.org/10.3389/fmars.2018.00328, 2018. a, b
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399, 2019. a
Bolzon, G., Teruzzi, A., Salon, S., Biagio, V. D., Feudale, L., Amadio, C., Coidessa, G., and Cossarini, G.: bit.sea, Zenodo [code], https://doi.org/10.5281/zenodo.8283692, 2023. a
Bottou, L.: Large-scale machine learning with stochastic gradient descent, in: Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, 22–27 August 2010 Keynote, Invited and Contributed Papers, Springer, 177–186, https://doi.org/10.1007/978-3-7908-2604-3_16, 2010. a
Campbell, L. M., Gray, N. J., Fairbanks, L., Silver, J. J., Gruby, R. L., Dubik, B. A., and Basurto, X.: Global oceans governance: New and emerging issues, Annu. Rev. Environ. Resour., 41, 517–543, 2016. a
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa, P.: Natural language processing (almost) from scratch, J. Mach. Learn. Res., 12, 2493–2537, 2011. a
Coppini, G., Clementi, E., Cossarini, G., Salon, S., Korres, G., Ravdas, M., Lecci, R., Pistoia, J., Goglio, A. C., Drudi, M., Grandi, A., Aydogdu, A., Escudier, R., Cipollone, A., Lyubartsev, V., Mariani, A., Cretì, S., Palermo, F., Scuro, M., Masina, S., Pinardi, N., Navarra, A., Delrosso, D., Teruzzi, A., Di Biagio, V., Bolzon, G., Feudale, L., Coidessa, G., Amadio, C., Brosich, A., Miró, A., Alvarez, E., Lazzari, P., Solidoro, C., Oikonomou, C., and Zacharioudaki, A.: The Mediterranean Forecasting System – Part 1: Evolution and performance, Ocean Sci., 19, 1483–1516, https://doi.org/10.5194/os-19-1483-2023, 2023. a
Dall'Olmo, G., Bhaskar, T. V. S. U., Bittig, H., Boss, E., Brewster, J., Claustre, H., Donnelly, M., Maurer, T., Nicholson, D., Paba, V., Plant, J., Poteau, A., Sauzède, R., Schallenberg, C., Schmechtig, C., Schmid, C., and Xing, X.: Real-time quality control of optical backscattering data from Biogeochemical-Argo floats, Open Res. Eur., 2, 118, https://doi.org/10.12688/openreseurope.15047.2, 2022. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The operational sea surface temperature and sea ice analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, 2012. a
D’Ortenzio, F., Lavigne, H., Besson, F., Claustre, H., Coppola, L., Garcia, N., Laës-Huon, A., Le Reste, S., Malardé, D., Migon, C., Morin, P., Mortier, L., Poteau, A., Prieur, L., Raimbault, P., and Testor, P.: Observing mixed layer depth, nitrate and chlorophyll concentrations in the northwestern Mediterranean: A combined satellite and NO3 profiling floats experiment, Geophys. Res. Lett., 41, 6443–6451, 2014. a
D'ortenzio, F., Taillandier, V., Claustre, H., Prieur, L. M., Leymarie, E., Mignot, A., Poteau, A., Penkerc'h, C., and Schmechtig, C. M.: Biogeochemical Argo: The test case of the NAOS Mediterranean array, Front. Mar. Sci., 7, https://doi.org/10.3389/fmars.2020.00120, 2020. a, b
Ford, D.: Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design, Biogeosciences, 18, 509–534, https://doi.org/10.5194/bg-18-509-2021, 2021. a
Fourrier, M., Coppola, L., Claustre, H., D’Ortenzio, F., Sauzède, R., and Gattuso, J.-P.: A regional neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED, Front. Mar. Sci., 7, https://doi.org/10.3389/fmars.2020.00620, 2020. a, b, c, d, e
Goh, G. B., Hodas, N. O., and Vishnu, A.: Deep learning for computational chemistry, J. Comput. Chem., 38, 1291–1307, 2017. a
Goldstein, E. B., Coco, G., and Plant, N. G.: A review of machine learning applications to coastal sediment transport and morphodynamics, Earth-Sci. Rev., 194, 97–108, 2019. a
Goodwin, J. D., North, E. W., and Thompson, C. M.: Evaluating and improving a semi-automated image analysis technique for identifying bivalve larvae, Limnol. Oceanogr.-Meth., 12, 548–562, 2014. a
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., and Chen, T.: Recent advances in convolutional neural networks, Pattern Recogn., 77, 354–377, 2018. a
Johnson, K. and Claustre, H.: Bringing biogeochemistry into the Argo age, Eos T. Am. Geophys. Un., https://doi.org/10.1029/2016EO062427, 2016. a, b
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., and Inman, D. J.: 1D convolutional neural networks and applications: A survey, Mech. Syst. signal Pr., 151, 107398, https://doi.org/10.1016/j.ymssp.2020.107398, 2021. a, b
Krogh, A.: What are artificial neural networks?, Nat. Biotechnol., 26, 195–197, 2008. a
Le Traon, P., Abadie, V., Ali, A., Behrens, A., Staneva, J., Hieronymi, M., and Krasemann, H.: The Copernicus Marine Service from 2015 to 2021: six years of achievements, https://doi.org/10.48670/moi-cafr-n813, 2021. a
Mignot, A., Claustre, H., Uitz, J., Poteau, A., d'Ortenzio, F., and Xing, X.: Understanding the seasonal dynamics of phytoplankton biomass and the deep chlorophyll maximum in oligotrophic environments: A Bio-Argo float investigation, Global Biogeochem. Cy., 28, 856–876, 2014. a
Miloslavich, P., Seeyave, S., Muller-Karger, F., Bax, N., Ali, E., Delgado, C., Evers-King, H., Loveday, B., Lutz, V., Newton, J., Nolan, G., Peralta Brichtova, A.C., Traeger-Chatterjee, C., and Urban, E.: Challenges for global ocean observation: the need for increased human capacity, J. Oper. Oceanogr., 12, S137–S156, 2019. a
Mori, U., Mendiburu, A., Keogh, E., and Lozano, J. A.: Reliable early classification of time series based on discriminating the classes over time, Data Min. Knowl. Discovery, 31, 233–263, 2017. a
Munk, W.: Oceanography before, and after, the advent of satellites, in: Elsevier Oceanography Series, Elsevier, vol. 63, 1–4, https://doi.org/10.1016/S0422-9894(00)80002-1, 2000. a
O'Shea, K. and Nash, R.: An introduction to convolutional neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1511.08458, 2015. a
Pietropolli, G., Cossarini, G., and Manzoni, L.: GANs for Integration of Deterministic Model and Observations in Marine Ecosystem, in: Progress in Artificial Intelligence: 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, 31 August 31–2 September 2022, Proceedings, Springer, 452–463, https://doi.org/10.1007/978-3-031-16474-3_37, 2022. a
Pietropolli, G., Manzoni, L., and Gianpiero, C.: PPCon 1.0: Biogeochemical Argo Profile Prediction with 1D Convolutional Networks, Zenodo, https://doi.org/10.5281/zenodo.8369573, 2023b. a
Pietropolli, G., Manzoni, L., and Gianpiero, C.: PPCon 1.0: Biogeochemical Argo Profile Prediction with 1D Convolutional Networks, Zenodo, https://doi.org/10.5281/zenodo.13126961, 2024. a
Rasamoelina, A. D., Adjailia, F., and Sinčák, P.: A review of activation function for artificial neural network, in: 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), IEEE, 281–286, https://doi.org/10.1109/SAMI48414.2020.9108717, 2020. a
Sammartino, M., Buongiorno Nardelli, B., Marullo, S., and Santoleri, R.: An artificial neural network to infer the Mediterranean 3D chlorophyll-a and temperature fields from remote sensing observations, Remote Sens., 12, 4123, https://doi.org/10.3390/rs12244123, 2020. a, b
Santurkar, S., Tsipras, D., Ilyas, A., and Madry, A.: How does batch normalization help optimization?, arXiv [preprint], https://doi.org/10.48550/arXiv.1805.11604, 2018. a
Sauzède, R., Claustre, H., Uitz, J., Jamet, C., Dall'Olmo, G., d'Ortenzio, F., Gentili, B., Poteau, A., and Schmechtig, C.: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res.-Oceans, 121, 2552–2571, 2016. a
Sauzède, R., Bittig, H. C., Claustre, H., Pasqueron de Fommervault, O., Gattuso, J.-P., Legendre, L., and Johnson, K. S.: Estimates of water-column nutrient concentrations and carbonate system parameters in the global ocean: A novel approach based on neural networks, Front. Mar. Sci., 4, https://doi.org/10.3389/fmars.2017.00128, 2017. a, b, c
Shan, C., Zhang, J., Wang, Y., and Xie, L.: Attention-based end-to-end speech recognition on voice search, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 4764–4768, https://doi.org/10.1109/ICASSP.2018.8462492, 2018. a
Taud, H. and Mas, J.: Multilayer perceptron (MLP), Geomatic approaches for modeling land change scenarios, 451–455, https://doi.org/10.1007/978-3-319-60801-3_1, 2018. a
Teruzzi, A., Bolzon, G., Feudale, L., and Cossarini, G.: Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment, Biogeosciences, 18, 6147–6166, https://doi.org/10.5194/bg-18-6147-2021, 2021. a, b, c
Volpe, G., Nardelli, B. B., Colella, S., Pisano, A., and Santoleri, R.: Operational Interpolated Ocean Colour Product in the Mediterranean Sea, New Frontiers in Operational Oceanography, 227–244, https://doi.org/10.17125/gov2018.ch09, 2018. a
Yu, L., Fennel, K., Bertino, L., El Gharamti, M., and Thompson, K. R.: Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling, Ocean Model., 126, 13–28, 2018. a
Zeiler, M. D.: Adadelta: an adaptive learning rate method, arXiv [preprint], https://doi.org/10.48550/arXiv.1212.5701, 2012. a
Zou, H. and Hastie, T.: Regularization and variable selection via the elastic net, J. Roy. Stat. Soc. Ser. B, 67, 301–320, 2005. a
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.
Monitoring the ocean is essential for studying marine life and human impact. Our new software,...
