Articles | Volume 2, issue 2
https://doi.org/10.5194/ascmo-2-171-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/ascmo-2-171-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Weak constraint four-dimensional variational data assimilation in a model of the California Current System
Department of Ocean Sciences, University of California, Santa Cruz, CA 95062, USA
Polly J. Smith
Department of Mathematics and Statistics, University of Reading, Reading RG6 6AX, UK
Ralph F. Milliff
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80304, USA
Jerome Fiechter
Department of Ocean Sciences, University of California, Santa Cruz, CA 95062, USA
Christopher K. Wikle
Department of Statistics, University of Missouri, Columbia, MO 65211, USA
Christopher A. Edwards
Department of Ocean Sciences, University of California, Santa Cruz, CA 95062, USA
Andrew M. Moore
Department of Ocean Sciences, University of California, Santa Cruz, CA 95062, USA
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Pierre-Yves Le Traon, Antonio Novellino, and Andrew M. Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-36, https://doi.org/10.5194/sp-2024-36, 2024
Revised manuscript under review for SP
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Ocean prediction relies on the integration between models, satellite and in-situ observations through data assimilation techniques. The authors discuss the role of observations in operational ocean forecasting systems, describing the state-of-the-art of satellite and in-situ observing networks and defining the paths for addressing multi-scale monitoring and forecasting.
Antonio Novellino, Pierre-Yves Le Traon, and Andy Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-23, https://doi.org/10.5194/sp-2024-23, 2024
Preprint under review for SP
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This paper discusses the vital role of observations in ocean predictions and forecasting, highlighting the need for effective access, management, and integration of data to improve models and decision-making. The paper also explores opportunities for standardizing protocols and the potential of citizen-based, cost-effective data collection methods.
Gianpiero Cossarini, Andy Moore, Stefano Ciavatta, and Katja Fennel
State Planet Discuss., https://doi.org/10.5194/sp-2024-8, https://doi.org/10.5194/sp-2024-8, 2024
Revised manuscript under review for SP
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Marine biogeochemistry refers to the cycling of chemical elements resulting from physical transport, chemical reaction, uptake, and processing by living organisms. Biogeochemical models can have a wide range of complexity, from single parameterizations of processes to fully explicit representations of several nutrients, trophic levels, and functional groups. Uncertainty sources are the lack of knowledge about the parameterizations, initial and boundary conditions and the lack of observations
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-20, https://doi.org/10.5194/sp-2024-20, 2024
Preprint under review for SP
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Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity and currents will evolve over the coming days and weeks, as well as to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Oriol Tintó Prims, Mario C. Acosta, Andrew M. Moore, Miguel Castrillo, Kim Serradell, Ana Cortés, and Francisco J. Doblas-Reyes
Geosci. Model Dev., 12, 3135–3148, https://doi.org/10.5194/gmd-12-3135-2019, https://doi.org/10.5194/gmd-12-3135-2019, 2019
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Mixed-precision approaches can provide substantial speed-ups for both computing- and memory-bound codes, requiring little effort. A novel method to enable modern and legacy codes to benefit from a reduction of precision without sacrificing accuracy is presented. Using a precision emulator and a divide-and-conquer algorithm it identifies the parts that cannot handle reduced precision and the ones that can. The method has been proved using two ocean models, NEMO and ROMS, with promising results.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
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Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Carlos Rocha, Christopher A. Edwards, Moninya Roughan, Paulina Cetina-Heredia, and Colette Kerry
Geosci. Model Dev., 12, 441–456, https://doi.org/10.5194/gmd-12-441-2019, https://doi.org/10.5194/gmd-12-441-2019, 2019
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Off southeast Australia, the East Australian Current (EAC) moves warm nutrient-poor waters towards the pole. In this region, the EAC and a large number of vortices pinching off it strongly affect phytoplankton’s access to nutrients and light. To study these dynamics, we created a numerical model that is able to solve the ocean conditions and how they modulate the foundation of the region’s ecosystem. We validated model results against available data and this showed that the model performs well.
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Marceau Michel, Said Obakrim, Nicolas Raillard, Pierre Ailliot, and Valérie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 83–95, https://doi.org/10.5194/ascmo-8-83-2022, https://doi.org/10.5194/ascmo-8-83-2022, 2022
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In this study, we introduce a deep learning algorithm to establish the relationship between wind and waves in order to predict the latter. The performance of the proposed method has been evaluated both on the output of numerical wave models and on in situ data and compared to other statistical methods developed by our research team. The results obtained confirm the interest of deep learning methods for forecasting ocean data.
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Short summary
We present a method for estimating intrinsic model error in a model of the California Current System. The estimated model error covariance matrix is used in the weak constraint formulation of the Regional Ocean Modeling System, four-dimensional variational data assimilation system, and comparison of the circulation estimates computed in this way show demonstrable improvement to those computed in the strong constraint formulation, where intrinsic model error is not taken into account.
We present a method for estimating intrinsic model error in a model of the California Current...