Articles | Volume 14, issue 7
Geosci. Model Dev., 14, 4769–4780, 2021
https://doi.org/10.5194/gmd-14-4769-2021
Geosci. Model Dev., 14, 4769–4780, 2021
https://doi.org/10.5194/gmd-14-4769-2021

Model description paper 30 Jul 2021

Model description paper | 30 Jul 2021

Ocean Plastic Assimilator v0.2: assimilation of plastic concentration data into Lagrangian dispersion models

Axel Peytavin et al.

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Cited articles

Anderson, J.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007. a
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 37, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9, 2002. a, b
Campin, J.-M., Heimbach, P., Losch, M., Forget, G., edhill3, Adcroft, A., amolod, Menemenlis, D., dfer22, Hill, C., Jahn, O., Scott, K., stephdut, Mazloff, M., Fox-Kemper, B., antnguyen13, E., D., Fenty, I., Bates, M., AndrewEichmann-NOAA, Smith, T., Martin, T., Lauderdale, J., Abernathey, R., samarkhatiwala, hongandyan, Deremble, B., dngoldberg, Bourgault, P., and Dussin, R.: MITgcm/MITgcm: mid 2020 version (Version checkpoint67s), Zenodo, https://doi.org/10.5281/zenodo.3967889, 2020. a
Dagestad, K.-F., Röhrs, J., Breivik, Ø., and Ådlandsvik, B.: OpenDrift v1.0: a generic framework for trajectory modelling, Geosci. Model Dev., 11, 1405–1420, https://doi.org/10.5194/gmd-11-1405-2018, 2018. a, b
Delandmeter, P. and van Sebille, E.: The Parcels v2.0 Lagrangian framework: new field interpolation schemes, Geosci. Model Dev., 12, 3571–3584, https://doi.org/10.5194/gmd-12-3571-2019, 2019. a
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We present a new algorithm developed at The Ocean Cleanup to update ocean plastic models based on measurements from the field to improve future cleaning operations. Prepared in collaboration with MIT researchers, this initial study presents its use in several analytical and real test cases in which two observers in a flow field record regular observations to update a plastic forecast. We demonstrate this improves the prediction, even with inaccurate knowledge of the water flows driving plastic.