Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8395-2022
© Author(s) 2022. 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-15-8395-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 Nov 2022
Development and technical paper |
| 18 Nov 2022
| 18 Nov 2022
An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0
RIKEN Center for Computational Science, Kobe, 6500047, Japan
RIKEN Cluster for Pioneering Research, Kobe, 6500047, Japan
Institute for Space-Earth Environmental Research, Nagoya University,
Nagoya, 4648601, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program
(iTHEMS), Kobe, 6500047, Japan
Tsutomu Hihara
Japan Fisheries Information Service Center, Tokyo, 1040055, Japan
Hidenori Aiki
Institute for Space-Earth Environmental Research, Nagoya University,
Nagoya, 4648601, Japan
Application Laboratory, Japan Agency for Marine-Earth Science and
Technology, Yokohama, 2360001, Japan
Joji Ishizaka
Institute for Space-Earth Environmental Research, Nagoya University,
Nagoya, 4648601, Japan
Yasumasa Miyazawa
Application Laboratory, Japan Agency for Marine-Earth Science and
Technology, Yokohama, 2360001, Japan
Misako Kachi
Earth Observation Research Center, Japan Aerospace Exploration Agency,
Tsukuba, 3058505, Japan
Takemasa Miyoshi
CORRESPONDING AUTHOR
RIKEN Center for Computational Science, Kobe, 6500047, Japan
RIKEN Cluster for Pioneering Research, Kobe, 6500047, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program
(iTHEMS), Kobe, 6500047, Japan
Related authors
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
Geosci. Model Dev., 15, 9057–9073, https://doi.org/10.5194/gmd-15-9057-2022, https://doi.org/10.5194/gmd-15-9057-2022, 2022
Short summary
Short summary
An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.
Arata Amemiya and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2543, https://doi.org/10.5194/egusphere-2025-2543, 2025
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
Short summary
Short summary
The accurate estimation of atmospheric state variables from radar observation in rapidly growing deep convection, which causes heavy thunderstorms, is a major challenge. This study examines the advantage of incorporating radar observation data with very high frequency such as 30 seconds compared with the conventional case of 5 minutes, from a theoretical perspective.
Juan Martin Guerrieri, Manuel Arturo Pulido, Takemasa Miyoshi, Arata Amemiya, and Juan José Ruiz
EGUsphere, https://doi.org/10.5194/egusphere-2025-2420, https://doi.org/10.5194/egusphere-2025-2420, 2025
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
Short summary
Short summary
This work extends the Mapping Particle Filter to account for local dependencies. Two localization methods are tested: a global particle flow with local kernels, and iterative local mappings based on correlation radius. Using a two-scale Lorenz-96 truth and a one-scale forecast model, experiments with full/partial and linear/nonlinear observations show Root Mean Square Error (RMSE) reductions using localized Gaussian mixture priors, achieving competitive performance against Gaussian filters.
Jennifer Veitch, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Mauro Cirano, Emanuela Clementi, Fraser Davidson, Ghada el Serafy, Guilherme Franz, Patrick Hogan, Sudheer Joseph, Svitlana Liubartseva, Yasumasa Miyazawa, Heather Regan, and Katerina Spanoudaki
State Planet, 5-opsr, 6, https://doi.org/10.5194/sp-5-opsr-6-2025, https://doi.org/10.5194/sp-5-opsr-6-2025, 2025
Short summary
Short summary
Ocean forecast systems provide information about a future state of the ocean. This information is provided in the form of decision support tools, or downstream applications, that can be accessed by various stakeholders to support livelihoods, coastal resilience and the good governance of the marine environment. This paper provides an overview of the various downstream applications of ocean forecast systems that are utilized around the world.
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego A. Narvaez, Heather Regan, Claudia G. Simionato, Gregory C. Smith, Joanna Staneva, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, Jennifer Veitch, and Jorge Zavala Hidalgo
State Planet, 5-opsr, 5, https://doi.org/10.5194/sp-5-opsr-5-2025, https://doi.org/10.5194/sp-5-opsr-5-2025, 2025
Short summary
Short summary
Operational ocean forecasting systems (OOFSs) are crucial for human activities, environmental monitoring, and policymaking. An assessment across eight key regions highlights strengths and gaps, particularly in coastal and biogeochemical forecasting. AI offers improvements, but collaboration, knowledge sharing, and initiatives like the OceanPrediction Decade Collaborative Centre (DCC) are key to enhancing accuracy, accessibility, and global forecasting capabilities.
Marina Tonani, Eric Chassignet, Mauro Cirano, Yasumasa Miyazawa, and Begoña Pérez Gómez
State Planet, 5-opsr, 3, https://doi.org/10.5194/sp-5-opsr-3-2025, https://doi.org/10.5194/sp-5-opsr-3-2025, 2025
Short summary
Short summary
This article provides an overview of the main characteristics of ocean forecast systems covering a limited region of the ocean. Their main components are described, as well as the spatial and temporal scales they resolve. The oceanic variables that these systems are able to predict are also explained. An overview of the main forecasting systems currently in operation is also provided.
Yann Drillet, Matthew Martin, Yosuke Fujii, Eric Chassignet, and Stefania Ciliberti
State Planet, 5-opsr, 2, https://doi.org/10.5194/sp-5-opsr-2-2025, https://doi.org/10.5194/sp-5-opsr-2-2025, 2025
Short summary
Short summary
This article describes the various stages of research and development that have been carried out over the last few decades to produce an operational reference service for global ocean monitoring and forecasting.
Michael Goodliff and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2025-933, https://doi.org/10.5194/egusphere-2025-933, 2025
Short summary
Short summary
Data-driven models (DDMs) learn from large datasets to make predictions, but data limitations affect reliability. Data assimilation (DA) improves accuracy by combining real-world observations with computational models. This research explores how DA enhances DDMs despite limited data. We propose an algorithm using DA to refine DDM training iteratively. This work has broad implications for fields like meteorology, engineering, and environmental science, where accurate prediction is critical.
Tim Trent, Marc Schröder, Shu-Peng Ho, Steffen Beirle, Ralf Bennartz, Eva Borbas, Christian Borger, Helene Brogniez, Xavier Calbet, Elisa Castelli, Gilbert P. Compo, Wesley Ebisuzaki, Ulrike Falk, Frank Fell, John Forsythe, Hans Hersbach, Misako Kachi, Shinya Kobayashi, Robert E. Kursinski, Diego Loyola, Zhengzao Luo, Johannes K. Nielsen, Enzo Papandrea, Laurence Picon, Rene Preusker, Anthony Reale, Lei Shi, Laura Slivinski, Joao Teixeira, Tom Vonder Haar, and Thomas Wagner
Atmos. Chem. Phys., 24, 9667–9695, https://doi.org/10.5194/acp-24-9667-2024, https://doi.org/10.5194/acp-24-9667-2024, 2024
Short summary
Short summary
In a warmer future, water vapour will spend more time in the atmosphere, changing global rainfall patterns. In this study, we analysed the performance of 28 water vapour records between 1988 and 2014. We find sensitivity to surface warming generally outside expected ranges, attributed to breakpoints in individual record trends and differing representations of climate variability. The implication is that longer records are required for high confidence in assessing climate trends.
Qingyang Song and Hidenori Aiki
Ocean Sci., 19, 1705–1717, https://doi.org/10.5194/os-19-1705-2023, https://doi.org/10.5194/os-19-1705-2023, 2023
Short summary
Short summary
There has been a long-standing need for a rapid-detection method for waves using simulation data for Atlantic Niño events. This study addresses this by utilizing an ocean reanalysis and an energy flux scheme during the 2019 Niño event. The results confirm the significant influence of subseasonal Kelvin waves on the event and also suggest that wave energy from off-equatorial regions likely preconditioned the event. This study is thus a useful tool for warning systems for Atlantic Niño events.
Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023, https://doi.org/10.5194/npg-30-457-2023, 2023
Short summary
Short summary
This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data into a global weather model. By assimilating atmospheric observations and soil moisture data, the accuracy of forecasts was improved, and certain biases were reduced. The method was found to be particularly beneficial in areas like the Sahel and equatorial Africa, where precipitation patterns vary seasonally. This new approach has the potential to improve the precision of weather predictions.
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
Qiwen Sun, Takemasa Miyoshi, and Serge Richard
Nonlin. Processes Geophys., 30, 117–128, https://doi.org/10.5194/npg-30-117-2023, https://doi.org/10.5194/npg-30-117-2023, 2023
Short summary
Short summary
This paper is a follow-up of a work by Miyoshi and Sun which was published in NPG Letters in 2022. The control simulation experiment is applied to the Lorenz-96 model for avoiding extreme events. The results show that extreme events of this partially and imperfectly observed chaotic system can be avoided by applying pre-designed small perturbations. These investigations may be extended to more realistic numerical weather prediction models.
Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann
Nonlin. Processes Geophys., 30, 13–29, https://doi.org/10.5194/npg-30-13-2023, https://doi.org/10.5194/npg-30-13-2023, 2023
Short summary
Short summary
This study investigates vertical localization based on a convection-permitting 1000-member ensemble simulation. We derive an empirical optimal localization (EOL) that minimizes sampling error in 40-member sub-sample correlations assuming 1000-member correlations as truth. The results will provide guidance for localization in convective-scale ensemble data assimilation systems.
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
Geosci. Model Dev., 15, 9057–9073, https://doi.org/10.5194/gmd-15-9057-2022, https://doi.org/10.5194/gmd-15-9057-2022, 2022
Short summary
Short summary
An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
Short summary
Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022, https://doi.org/10.5194/npg-29-133-2022, 2022
Short summary
Short summary
The weather is chaotic and hard to predict, but the chaos implies an effective control where a small control signal grows rapidly to make a big difference. This study proposes a control simulation experiment where we apply a small signal to control
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626, https://doi.org/10.5194/npg-28-615-2021, https://doi.org/10.5194/npg-28-615-2021, 2021
Short summary
Short summary
Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
Hideharu Sasaki, Shinichiro Kida, Ryo Furue, Hidenori Aiki, Nobumasa Komori, Yukio Masumoto, Toru Miyama, Masami Nonaka, Yoshikazu Sasai, and Bunmei Taguchi
Geosci. Model Dev., 13, 3319–3336, https://doi.org/10.5194/gmd-13-3319-2020, https://doi.org/10.5194/gmd-13-3319-2020, 2020
Short summary
Short summary
A quasi-global eddying ocean hindcast simulation using a new version of our model, called OFES2, was conducted to overcome several issues in its previous version. OFES2 simulated oceanic fields from 1958 to 2016 with improved global sea surface temperature and salinity, water mass properties in the Indonesian and Arabian seas, and Niño3.4 and Indian Ocean Dipole indexes. The output from OFES2 will be useful in studying various oceanic phenomena with broad spatiotemporal scales.
Miho Ishizu, Yasumasa Miyazawa, Tomohiko Tsunoda, and Tsuneo Ono
Biogeosciences, 16, 4747–4763, https://doi.org/10.5194/bg-16-4747-2019, https://doi.org/10.5194/bg-16-4747-2019, 2019
Short summary
Short summary
Using water quality data collected at 289 monitoring sites as part of the Water Pollution Control Program, we evaluated the long-term trends of pH in Japanese coastal seawater at ambient temperature from 1978 to 2009. We found that the annual maximum pH, which generally represents the pH of surface waters in winter, had decreased at 75 % of the sites, but had increased at the remaining sites. The annual maximum pH decreased at an average rate of −0.0024 yr−1, with relatively large deviations.
Keiichi Kondo and Takemasa Miyoshi
Nonlin. Processes Geophys., 26, 211–225, https://doi.org/10.5194/npg-26-211-2019, https://doi.org/10.5194/npg-26-211-2019, 2019
Short summary
Short summary
This study investigates non-Gaussian statistics of the data from a 10240-member ensemble Kalman filter. The large ensemble size can resolve the detailed structures of the probability density functions (PDFs) and indicates that the non-Gaussian PDF is caused by multimodality and outliers. While the outliers appear randomly, large multimodality corresponds well with large analysis error, mainly in the tropical regions and storm track regions where highly nonlinear processes appear frequently.
Atsushi Okazaki, Takumi Honda, Shunji Kotsuki, Moeka Yamaji, Takuji Kubota, Riko Oki, Toshio Iguchi, and Takemasa Miyoshi
Atmos. Meas. Tech., 12, 3985–3996, https://doi.org/10.5194/amt-12-3985-2019, https://doi.org/10.5194/amt-12-3985-2019, 2019
Short summary
Short summary
The JAXA is surveying the feasibility of a potential satellite mission equipped with a precipitation radar on a geostationary orbit, as a successor of the GPM Core Observatory. We investigate what kind of observation data will be available from the radar using simulation techniques. Although the quality of the observation depends on the radar specifications and the position of precipitation systems, the results demonstrate that it would be possible to obtain three-dimensional precipitation data.
Hailong Zhang, Shengqiang Wang, Zhongfeng Qiu, Deyong Sun, Joji Ishizaka, Shaojie Sun, and Yijun He
Biogeosciences, 15, 4271–4289, https://doi.org/10.5194/bg-15-4271-2018, https://doi.org/10.5194/bg-15-4271-2018, 2018
Short summary
Short summary
The PSC model was re-tuned for regional application in the East China Sea, and successfully applied to MODIS data. We investigated previously unknown temporal–spatial patterns of the PSC in the ECS and analyzed their responses to environmental factors. The results show the PSC varied across both spatial and temporal scales, and was probably affected by the water column stability, upwelling, and Kuroshio. In addition, human activity and riverine discharge may impact the PSC dynamics.
Marc Schröder, Maarit Lockhoff, Frank Fell, John Forsythe, Tim Trent, Ralf Bennartz, Eva Borbas, Michael G. Bosilovich, Elisa Castelli, Hans Hersbach, Misako Kachi, Shinya Kobayashi, E. Robert Kursinski, Diego Loyola, Carl Mears, Rene Preusker, William B. Rossow, and Suranjana Saha
Earth Syst. Sci. Data, 10, 1093–1117, https://doi.org/10.5194/essd-10-1093-2018, https://doi.org/10.5194/essd-10-1093-2018, 2018
Short summary
Short summary
This publication presents results achieved within the GEWEX Water Vapor Assessment (G-VAP). An overview of available water vapour data records based on satellite observations and reanalysis is given. If a minimum temporal coverage of 10 years is applied, 22 data records remain. These form the G-VAP data archive, which contains total column water vapour, specific humidity profiles and temperature profiles. The G-VAP data archive is designed to ease intercomparison and climate model evaluation.
Guo-Yuan Lien, Daisuke Hotta, Eugenia Kalnay, Takemasa Miyoshi, and Tse-Chun Chen
Nonlin. Processes Geophys., 25, 129–143, https://doi.org/10.5194/npg-25-129-2018, https://doi.org/10.5194/npg-25-129-2018, 2018
Short summary
Short summary
The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under what conditions observations are beneficial or detrimental for assimilation. Based on EFSO, an offline assimilation method is proposed to accelerate the development of data selection strategies for new observing systems. The usefulness of this method is demonstrated with the assimilation of global satellite precipitation data.
Hazuki Arakida, Takemasa Miyoshi, Takeshi Ise, Shin-ichiro Shima, and Shunji Kotsuki
Nonlin. Processes Geophys., 24, 553–567, https://doi.org/10.5194/npg-24-553-2017, https://doi.org/10.5194/npg-24-553-2017, 2017
Short summary
Short summary
This is the first study assimilating the satellite-based leaf area index observations every 4 days into a numerical model simulating the growth and death of individual plants. The newly developed data assimilation system successfully reduced the uncertainties of the model parameters related to phenology and carbon dynamics. It also provides better estimates of the present vegetation structure which can be used as the initial states for the simulation of the future vegetation change.
Stephen G. Penny and Takemasa Miyoshi
Nonlin. Processes Geophys., 23, 391–405, https://doi.org/10.5194/npg-23-391-2016, https://doi.org/10.5194/npg-23-391-2016, 2016
Short summary
Short summary
Particle filters in their basic form have been shown to be unusable for large geophysical systems because the number of required particles grows exponentially with the size of the system. We have applied the ideas of localized analyses at each model grid point and use ensemble weight smoothing to blend each local analysis with its neighbors. This new local particle filter (LPF) makes large geophysical applications tractable for particle filters and is competitive with a popular EnKF alternative.
Hisashi Yashiro, Koji Terasaki, Takemasa Miyoshi, and Hirofumi Tomita
Geosci. Model Dev., 9, 2293–2300, https://doi.org/10.5194/gmd-9-2293-2016, https://doi.org/10.5194/gmd-9-2293-2016, 2016
Short summary
Short summary
We propose the design and implementation of an ensemble data assimilation framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file I/O and multi-node communication. With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework promises drastic reduction of total execution time.
X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, and H.-J. H. Franssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
T. Waseda, K. In, K. Kiyomatsu, H. Tamura, Y. Miyazawa, and K. Iyama
Nat. Hazards Earth Syst. Sci., 14, 945–957, https://doi.org/10.5194/nhess-14-945-2014, https://doi.org/10.5194/nhess-14-945-2014, 2014
S. Q. Wang, J. Ishizaka, H. Yamaguchi, S. C. Tripathy, M. Hayashi, Y. J. Xu, Y. Mino, T. Matsuno, Y. Watanabe, and S. J. Yoo
Biogeosciences, 11, 1759–1773, https://doi.org/10.5194/bg-11-1759-2014, https://doi.org/10.5194/bg-11-1759-2014, 2014
Y. Umezawa, A. Yamaguchi, J. Ishizaka, T. Hasegawa, C. Yoshimizu, I. Tayasu, H. Yoshimura, Y. Morii, T. Aoshima, and N. Yamawaki
Biogeosciences, 11, 1297–1317, https://doi.org/10.5194/bg-11-1297-2014, https://doi.org/10.5194/bg-11-1297-2014, 2014
S. G. Penny, E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi, and G. A. Chepurin
Nonlin. Processes Geophys., 20, 1031–1046, https://doi.org/10.5194/npg-20-1031-2013, https://doi.org/10.5194/npg-20-1031-2013, 2013
Y. Miyazawa, Y. Masumoto, S. M. Varlamov, T. Miyama, M. Takigawa, M. Honda, and T. Saino
Biogeosciences, 10, 2349–2363, https://doi.org/10.5194/bg-10-2349-2013, https://doi.org/10.5194/bg-10-2349-2013, 2013
Related subject area
Oceanography
GREAT v1.0: Global Real-time Early Assessment of Tsunamis
Using automatic calibration to improve the physics behind complex numerical models: an example from a 3D lake model using Delft3D (v6.02.10) and DYNO-PODS (v1.0)
Improvements to the Met Office's global ocean–sea ice forecasting system including model and data assimilation changes
Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models
A new global high-resolution wave model for the tropical ocean using WAVEWATCH III version 7.14
sedInterFoam 1.0: a three-phase numerical model for sediment transport applications with free surfaces
Offline Fennel: A High-Performance and Computationally Efficient Biogeochemical Model within the Regional Ocean Modeling System (ROMS)
The Ross Sea and Amundsen Sea Ice–Sea Model (RAISE v1.0): a high-resolution ocean–sea ice–ice shelf coupling model for simulating the Dense Shelf Water and Antarctic Bottom Water in the Ross Sea, Antarctica
Sensitivity of the tropical Atlantic to vertical mixing in two ocean models (ICON-O v2.6.6 and FESOM v2.5)
CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
A wave-resolving two-dimensional vertical Lagrangian approach to model microplastic transport in nearshore waters based on TrackMPD 3.0
HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
Comparing an idealized deterministic-stochastic model (SUP model, version 1) of the tide-and-wind driven sea surface currents in the Gulf of Trieste to HF Radar observations
ISWFM-NSCS v2.0: advancing the internal solitary wave forecasting model with background currents and horizontally inhomogeneous stratifications
Impacts of CICE sea ice model and ERA atmosphere on an Antarctic MetROMS ocean model, MetROMS-UHel-v1.0
DalROMS-NWA12 v1.0, a coupled circulation–ice–biogeochemistry modelling system for the northwest Atlantic Ocean: development and validation
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
Evaluating an accelerated forcing approach for improving computational efficiency in coupled ice sheet–ocean modelling
An optimal transformation method for inferring ocean tracer sources and sinks
Data-driven rolling model for global wave height
PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
Wave forecast investigations on downscaling, source terms, and tides for Aotearoa New Zealand
An Effective Communication Topology for Performance Optimization: A Case Study of the Finite Volume WAve Modeling (FVWAM)
Experimental design for the Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2)
Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids
Spurious numerical mixing under strong tidal forcing: a case study in the south-east Asian seas using the Symphonie model (v3.1.2)
Modelling the water isotope distribution in the Mediterranean Sea using a high-resolution oceanic model (NEMO-MED12-watiso v1.0): evaluation of model results against in situ observations
LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry
PIBM 1.0: An individual-based model for simulating phytoplankton acclimation, diversity, and evolution in the ocean
Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0
A simple approach to represent precipitation-derived freshwater fluxes into nearshore ocean models: an FVCOM4.1 case study of Quatsino Sound, British Columbia
An optimal transformation method applied to diagnose the ocean carbon budget
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 2: Towards a better representation of total alkalinity when modeling the carbonate system and air–sea CO2 fluxes
Development of a novel storm surge inundation model framework for efficient prediction
Skin sea surface temperature schemes in coupled ocean–atmosphere modelling: the impact of chlorophyll-interactive e-folding depth
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
StraitFlux – precise computations of water strait fluxes on various modeling grids
Comparison of the Coastal and Regional Ocean COmmunity model (CROCO) and NCAR-LES in non-hydrostatic simulations
Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago
Implementation of additional spectral wave field exchanges in a three-dimensional wave–current coupled WAVEWATCH-III (version 6.07) and CROCO (version 1.2) configuration: assessment of their implications for macro-tidal coastal hydrodynamics
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
Great Lakes wave forecast system on high-resolution unstructured meshes
Impact of increased resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2)
A high-resolution physical–biogeochemical model for marine resource applications in the northwest Atlantic (MOM6-COBALT-NWA12 v1.0)
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev., 18, 3487–3507, https://doi.org/10.5194/gmd-18-3487-2025, https://doi.org/10.5194/gmd-18-3487-2025, 2025
Short summary
Short summary
The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, maps risk areas, and estimates tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev., 18, 3473–3486, https://doi.org/10.5194/gmd-18-3473-2025, https://doi.org/10.5194/gmd-18-3473-2025, 2025
Short summary
Short summary
Models simplify reality using assumptions, which can sometimes introduce flaws and affect their accuracy. Properly calibrating model parameters is essential, and although automated tools can speed up this process, they may occasionally produce incorrect values due to inconsistencies in the model. We demonstrate that by carefully applying automated tools, we were able to identify and correct a flaw in a widely used model for lake environments.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Axelle Gaffet, Xavier Bertin, Damien Sous, Héloïse Michaud, Aron Roland, and Emmanuel Cordier
Geosci. Model Dev., 18, 1929–1946, https://doi.org/10.5194/gmd-18-1929-2025, https://doi.org/10.5194/gmd-18-1929-2025, 2025
Short summary
Short summary
This study presents a new global wave model that improves predictions of sea states in tropical areas by using a high-resolution grid and corrected wind fields. The model is validated globally with satellite data and nearshore using in situ data. The model allows for the first time direct comparisons with in situ data collected at 10–30 m water depth, which is very close to shore due to the steep slope usually surrounding volcanic islands.
Antoine Mathieu, Yeulwoo Kim, Tian-Jian Hsu, Cyrille Bonamy, and Julien Chauchat
Geosci. Model Dev., 18, 1561–1573, https://doi.org/10.5194/gmd-18-1561-2025, https://doi.org/10.5194/gmd-18-1561-2025, 2025
Short summary
Short summary
Most of the tools available to model sediment transport do not account for complex physical mechanisms such as surface-wave-driven processes. In this study, a new model, sedInterFoam, allows us to reproduce numerically complex configurations in order to investigate coastal sediment transport applications dominated by surface waves and to gain insight into the complex physical processes associated with breaking waves and morphodynamics.
Júlia Crespin, Jordi Solé, and Miquel Canals
EGUsphere, https://doi.org/10.5194/egusphere-2025-865, https://doi.org/10.5194/egusphere-2025-865, 2025
Short summary
Short summary
This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical processes efficiently. By using existing hydrodynamic data, the model significantly reduces computation time from 6 hours to just 30 minutes. We tested its accuracy in the Northern Gulf of Mexico and found it closely matches physical-biogeochemical coupled simulations. This model allows researchers to conduct more tests and simulations without the need for extensive computational resources.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev., 18, 1375–1393, https://doi.org/10.5194/gmd-18-1375-2025, https://doi.org/10.5194/gmd-18-1375-2025, 2025
Short summary
Short summary
A coupled fine-resolution ocean–ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water, the Antarctic Bottom Water (AABW), which affects global ocean circulation. The model has a high skill level in simulating sea ice production driving the AABW source water formation and AABW properties when assessed against observations. A model experiment shows a significant impact of ice shelf melting on the AABW characteristics.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
Short summary
Short summary
Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
Short summary
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev., 18, 319–336, https://doi.org/10.5194/gmd-18-319-2025, https://doi.org/10.5194/gmd-18-319-2025, 2025
Short summary
Short summary
This study presents a novel modeling approach for understanding microplastic transport in coastal waters. The model accurately replicates experimental data and reveals key transport mechanisms. The findings enhance our knowledge of how microplastics move in nearshore environments, aiding in coastal management and efforts to combat plastic pollution globally.
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
Geosci. Model Dev., 18, 211–237, https://doi.org/10.5194/gmd-18-211-2025, https://doi.org/10.5194/gmd-18-211-2025, 2025
Short summary
Short summary
We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates physical conditions throughout the Salish Sea from 1980 to 2018. It was not clear that acceptable accuracy could be achieved because of computational and data limitations, but the model's predictions agreed well with observations. When we used the model to examine ocean temperature trends in areas that lack observations, it indicated that some seasons and areas are warming faster than others.
Sofia Flora, Laura Ursella, and Achim Wirth
EGUsphere, https://doi.org/10.5194/egusphere-2024-3391, https://doi.org/10.5194/egusphere-2024-3391, 2025
Short summary
Short summary
We developed a hierarchy of idealized deterministic-stochastic models to simulate the sea surface currents in the Gulf of Trieste. They include tide-and-wind driven sea surface current components, resolving the slowly varying part of the flow and a stochastic signal, representing the fast-varying small-scale dynamics. The comparison with High Frequency Radar observations shows that the non-Gaussian stochastic model captures the essential dynamics and permits to mimic the observed fat-tailed PDF.
Yankun Gong, Xueen Chen, Jiexin Xu, Zhiwu Chen, Qingyou He, Ruixiang Zhao, Xiao-Hua Zhu, and Shuqun Cai
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-165, https://doi.org/10.5194/gmd-2024-165, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
A new internal solitary wave forecasting (ISW) model in the northern South China Sea (ISWFM-NSCS v2.0) improves ISW predictions by incorporating background currents and inhomogeneous stratifications. Additionally, viscosity and diffusivity coefficients are optimized to maintain stable stratifications, extending the forecasting period. Sensitivity experiments illustrate that ISWFM-NSCS v2.0 significantly enhances predictions of various wave properties.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David A. Bailey, and Petteri Uotila
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-213, https://doi.org/10.5194/gmd-2024-213, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024, https://doi.org/10.5194/gmd-17-8697-2024, 2024
Short summary
Short summary
We developed a modelling system of the northwest Atlantic Ocean that simulates the currents, temperature, salinity, and parts of the biochemical cycle of the ocean, as well as sea ice. The system combines advanced, open-source models and can be used to study, for example, the ocean capture of atmospheric carbon dioxide, which is a key process in the global climate. The system produces realistic results, and we use it to investigate the roles of tides and sea ice in the northwest Atlantic Ocean.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
Short summary
Short summary
Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
Short summary
Short summary
We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Jan D. Zika and Taimoor Sohail
Geosci. Model Dev., 17, 8049–8068, https://doi.org/10.5194/gmd-17-8049-2024, https://doi.org/10.5194/gmd-17-8049-2024, 2024
Short summary
Short summary
We describe a method to relate fluxes of heat and freshwater at the sea surface to the resulting distribution of seawater among categories such as warm and salty or cold and salty. The method exploits the laws that govern how heat and salt change when water mixes. The method will allow the climate community to improve estimates of how much heat the ocean is absorbing and how rainfall and evaporation are changing across the globe.
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-181, https://doi.org/10.5194/gmd-2024-181, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
Short summary
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.
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-110, https://doi.org/10.5194/gmd-2024-110, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This research explores improving wave forecasts in New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models, finding that forecasts at Baring Head improved significantly due to its strong tidal currents, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
Renbo Pang, Fujiang Yu, Yuanyong Gao, Ye Yuan, Liang Yuan, and Zhiyi Gao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2515, https://doi.org/10.5194/egusphere-2024-2515, 2024
Short summary
Short summary
The application of the distributed graph communication topology in earth models has been rarely studied. We tested and compared this topology with the traditional point-to-point communication method using a global wave model. We found that this topology is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other earth models.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
Short summary
Short summary
Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, and Lorenzo Zampieri
Geosci. Model Dev., 17, 7067–7081, https://doi.org/10.5194/gmd-17-7067-2024, https://doi.org/10.5194/gmd-17-7067-2024, 2024
Short summary
Short summary
We coupled an unstructured hydro-model with an advanced column sea ice model to meet the growing demand for increased resolution and complexity in unstructured sea ice models. Additionally, we present a novel tracer transport scheme for the sea ice coupled model and demonstrate that this scheme fulfills the requirements for conservation, accuracy, efficiency, and monotonicity in an idealized test. Our new coupled model also has good performance in realistic tests.
Adrien Garinet, Marine Herrmann, Patrick Marsaleix, and Juliette Pénicaud
Geosci. Model Dev., 17, 6967–6986, https://doi.org/10.5194/gmd-17-6967-2024, https://doi.org/10.5194/gmd-17-6967-2024, 2024
Short summary
Short summary
Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mohamed Ayache, Jean-Claude Dutay, Anne Mouchet, Kazuyo Tachikawa, Camille Risi, and Gilles Ramstein
Geosci. Model Dev., 17, 6627–6655, https://doi.org/10.5194/gmd-17-6627-2024, https://doi.org/10.5194/gmd-17-6627-2024, 2024
Short summary
Short summary
Water isotopes (δ18O, δD) are one of the most widely used proxies in ocean climate research. Previous studies using water isotope observations and modelling have highlighted the importance of understanding spatial and temporal isotopic variability for a quantitative interpretation of these tracers. Here we present the first results of a high-resolution regional dynamical model (at 1/12° horizontal resolution) developed for the Mediterranean Sea, one of the hotspots of ongoing climate change.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
Short summary
Short summary
Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Iria Sala and Bingzhang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-130, https://doi.org/10.5194/gmd-2024-130, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits, and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
Short summary
Short summary
Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
Short summary
Short summary
Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
Short summary
Short summary
The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
Short summary
Short summary
The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
Short summary
Short summary
Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
Short summary
Short summary
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
Short summary
Short summary
Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
Short summary
Short summary
Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
Short summary
Short summary
The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
Short summary
Short summary
In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
Short summary
Short summary
Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Short summary
Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
Short summary
Short summary
The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
Short summary
Short summary
A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
Short summary
Short summary
We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
Short summary
Short summary
Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
Short summary
Short summary
This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
Short summary
Short summary
We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS NGDC-24, National Geophysical Data Center, NOAA, https://doi.org/10.7289/V5C8276M, 2009.
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation,
Mon. Weather Rev., 129, 2884–2903,
https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2,
2001.
Baduru, B., Paul, B., Banerjee, D. S., Sanikommu, S., and Paul, A.: Ensemble
based regional ocean data assimilation system for the Indian Ocean:
Implementation and evaluation, Ocean Model., 143, 101470,
https://doi.org/10.1016/j.ocemod.2019.101470, 2019.
Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi,
L., Smith, G. C., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D.,
Boyer, T., Chang, Y. S., Chepurin, G. A., Ferry, N., Forget, G., Fujii, Y.,
Good, S., Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A.,
Lee, T., Martin, M. J., Masina, S., Masuda, S., Meyssignac, B., Mogensen,
K., Parent, L., Peterson, K. A., Tang, Y. M., Yin, Y., Vernieres, G., Wang,
X., Waters, J., Wedd, R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J. F.,
Dupont, F., Kuragano, T., Kamachi, M., Awaji, T., Caltabiano, A.,
Wilmer-Becker, K., and Gaillard, F.: The ocean reanalyses intercomparison
project (ORA-IP), J. Oper. Oceanogr., 8, s80–s97,
https://doi.org/10.1080/1755876X.2015.1022329, 2015.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai,
Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y.,
Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama,
H., Uesawa, D., Yokota, H., and Yoshida, R.: An introduction to Himawari-8/9
– Japan's new-generation geostationary meteorological satellites, J.
Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016.
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data
assimilation using incremental analysis updates, Mon. Weather Rev., 124,
1256–1271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2, 1996.
Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically
significant effects of some approximations in the bulk parameterizations of
turbulent air–sea fluxes, J. Phys. Oceanogr., 47, 5–28,
https://doi.org/10.1175/JPO-D-16-0169.1, 2017.
Brune, S., Nerger, L., and Baehr, J.: Assimilation of oceanic observations in
a global coupled Earth system model with the SEIK filter, Ocean Model., 96,
254–264, https://doi.org/10.1016/j.ocemod.2015.09.011, 2015.
Brüning, T., Li, X., Schwichtenberg, F., and Lorkowski, I.: An
operational, assimilative model system for hydrodynamic and biogeochemical
applications for German coastal waters, Hydrogr. Nachrichten 118. Rostock
Dtsch. Hydrogr. Gesellschaft e.V., 6–15, https://doi.org/10.23784/HN118-01, 2021.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A new ocean climate
reanalysis, J. Climate, 31, 6967–6983, https://doi.org/10.1175/JCLI-D-17-0149.1,
2018.
Chakraborty, A., Sharma, R., Kumar, R., and Basu, S.: A SEEK filter
assimilation of sea surface salinity from Aquarius in an OGCM: Implication
for surface dynamics and thermohaline structure, J. Geophys. Res.-Oceans,
119, 4777–4796, https://doi.org/10.1002/2014JC009984, 2014.
Chang, Y. S., Zhang, S., Rosati, A., Delworth, T. L., and Stern, W. F.: An
assessment of oceanic variability for 1960–2010 from the GFDL ensemble
coupled data assimilation, Clim. Dynam., 40, 775–803,
https://doi.org/10.1007/s00382-012-1412-2, 2013.
Counillon, F., Keenlyside, N., Bethke, I., Wang, Y., Billeau, S., Shen, M.
L., and Bentsen, M.: Flow-dependent assimilation of sea surface temperature
in isopycnal coordinates with the Norwegian Climate Prediction Model,
Tellus A, 68, 32437,
https://doi.org/10.3402/tellusa.v68.32437, 2016.
Cronin, M. F. and Tozuka, T.: Steady state ocean response to wind forcing in
extratropical frontal regions, Sci. Rep., 6, 28842,
https://doi.org/10.1038/srep28842, 2016.
Ducet, N., Le Traon, P. Y., and Reverdin, G.: Global high-resolution mapping
of ocean circulation from TOPEX/Poseidon and ERS-1 and -2, J. Geophys. Res.,
105, 19477–19498, https://doi.org/10.1029/2000JC900063, 2000.
Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A.
J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach, H.:
On the exchange of momentum over the open ocean, J. Phys. Oceanogr., 43,
1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1, 2013.
Elipot, S., Lumpkin, R., Perez, R. C., Lilly, J. M., Early, J. J., and
Sykulski, A. M.: A global surface drifter data set at hourly resolution, J.
Geophys. Res.-Oceans, 121, 2937–2966, https://doi.org/10.1002/2016JC011716, 2016.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143–10162, 1994.
Evensen, G.: The Ensemble Kalman Filter: Theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003.
Guo, X., Hukuda, H., Miyazawa, Y., and Yamagata, T.: A triply nested ocean
model for simulating the Kuroshio–Roles of horizontal resolution on JEBAR,
J. Phys. Oceanogr., 33, 146–169, https://doi.org/10.1175/1520-0485(2003)033<0146:ATNOMF>2.0.CO;2, 2003.
Hackert, E., Busalacchi, A. J., and Ballabrera-Poy, J.: Impact of Aquarius
sea surface salinity observations on coupled forecasts for the tropical
Indo-Pacific Ocean, J. Geophys. Res.-Oceans, 119, 4045–4067,
https://doi.org/10.1002/2013JC009697, 2014.
He, H., Lei, L., Whitaker, J. S., and Tan, Z. M.: Impacts of assimilation
frequency on ensemble Kalman filter data assimilation and imbalances,
J. Adv. Model. Earth Sy., 12, e2020MS002187, https://doi.org/10.1029/2020MS002187, 2020.
Houtekamer, P. L. and Zhang, F.: Review of the ensemble Kalman filter for
atmospheric data assimilation, Mon. Weather Rev., 144, 4489–4532,
https://doi.org/10.1175/MWR-D-15-0440.1, 2016.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Phys. D,
230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007.
Jordi, A. and Wang, D. P.: sbPOM: A parallel implementation of Princenton
Ocean Model, Environ. Model. Softw., 38, 59–61,
https://doi.org/10.1016/j.envsoft.2012.05.013, 2012.
Karspeck, A. R., Yeager, S., Danabasoglu, G., Hoar, T., Collins, N., Raeder,
K., Anderson, J., and Tribbia, J.: An ensemble adjustment kalman filter for
the CCSM4 ocean component, J. Climate, 26, 7392–7413,
https://doi.org/10.1175/JCLI-D-12-00402.1, 2013.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 reanalysis: General specifications and basic characteristics, J.
Meteorol. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Kotsuki, S., Ota, Y., and Miyoshi, T.: Adaptive covariance relaxation methods
for ensemble data assimilation: experiments in the real atmosphere, Q. J. Roy.
Meteor. Soc., 143, 2001–2015, https://doi.org/10.1002/qj.3060, 2017.
Kunii, M. and Miyoshi, T.: Including uncertainties of sea surface
temperature in an ensemble Kalman filter: A case study of typhoon Sinlaku
(2008), Weather Forecast., 27, 1586–1597, https://doi.org/10.1175/WAF-D-11-00136.1,
2012.
Kurihara, Y., Murakami, H., and Kachi, M.: Sea surface temperature from the
new Japanese geostationary meteorological Himawari-8 satellite, Geophys.
Res. Lett., 43, 1234–1240, https://doi.org/10.1002/2015GL067159, 2016.
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M.
M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K. W., Paver, C. R.,
and Smolyar, I. V.: World Ocean Atlas 2018, Volume 1: Temperature. A.
Mishonov, Technical Editor, NOAA Atlas NESDIS, 81, 52, https://www.ncei.noaa.gov/sites/default/files/2021-03/woa18_vol1.pdf (last access: 11 November 2022), 2019.
Martin, M. J., Balmaseda, M., Bertino, L., Brasseur, P., Brassington, G.,
Cummings, J., Fujii, Y., Lea, D. J., Lellouche, J. M., Mogensen, K., Oke, P.
R., Smith, G. C., Testut, C. E., Waagbø, G. A., Waters, J., and Weaver, A.
T.: Status and future of data assimilation in operational oceanography, J.
Oper. Oceanogr., 8, s28–s48, https://doi.org/10.1080/1755876X.2015.1022055, 2015.
Meissner, T., Wentz, F. J., and Vine, D. M. Le: The salinity retrieval
algorithms for the NASA Aquarius version 5 and SMAP version 3 releases,
Remote Sens., 10, 1121, https://doi.org/10.3390/rs10071121, 2018.
Mellor, G. L., Ezer, T., and Oey, L.-Y.: The pressure gradient conundrum of
sigma coordinate ocean models, J. Atmos. Ocean. Tech., 11, 1126–1134,
https://doi.org/10.1175/1520-0426(1994)011<1126:TPGCOS>2.0.CO;2,
1994.
Minamide, M. and Zhang, F.: Adaptive observation error inflation for
assimilating all-sky satellite radiance, Mon. Weather Rev., 145,
1063–1081, https://doi.org/10.1175/MWR-D-16-0257.1, 2017.
Miyazawa, Y., Miyama, T., Varlamov, S. M., Guo, X., and Waseda, T.: Open and
coastal seas interactions south of Japan represented by an ensemble Kalman
filter, Ocean Dynam., 62, 645–659, https://doi.org/10.1007/s10236-011-0516-2, 2012.
Miyoshi, T.: The gaussian approach to adaptive covariance inflation and its
implementation with the local ensemble transform Kalman filter, Mon. Weather
Rev., 139, 1519–1535, https://doi.org/10.1175/2010MWR3570.1, 2011.
Miyoshi, T. and Yamane, S.: Local ensemble transform Kalman filtering with
an AGCM at a T159/L48 resolution, Mon. Weather Rev., 135, 3841–3861,
https://doi.org/10.1175/2007MWR1873.1, 2007.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn., 87,
895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A unification
of ensemble square root Kalman filters, Mon. Weather Rev., 140,
2335–2345, https://doi.org/10.1175/MWR-D-11-00102.1, 2012.
Ohishi, S.: shunohishi/sbPOM-LETKF: sbPOM-LETKF (v1.0), Zenodo, https://doi.org/10.5281/zenodo.6482744, 2022.
Ohishi, S., Miyoshi, T., and Kachi, M.: An EnKF-based ocean data assimilation system improved by adaptive observation error inflation (AOEI), Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-91, in review, 2022.
Penny, S. G., Kalnay, E., Carton, J. A., Hunt, B. R., Ide, K., Miyoshi, T., and Chepurin, G. A.: The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model, Nonlin. Processes Geophys., 20, 1031–1046, https://doi.org/10.5194/npg-20-1031-2013, 2013.
Penny, S. G., Behringer, D. W., Carton, J. A., and Kalnay, E.: A hybrid
global ocean data assimilation system at NCEP, Mon. Weather Rev., 143,
4660–4677, https://doi.org/10.1175/MWR-D-14-00376.1, 2015.
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble Kalman
filter: An alternative to ensemble square root filters, Tellus A, 60, 361–371,
https://doi.org/10.1111/j.1600-0870.2007.00299.x, 2008.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012.
Shibuya, R., Sato, K., Tomikawa, Y., Tsutsumi, M., and Sato, T.: A study of
multiple tropopause structures caused by inertia-gravity waves in the
antarctic, J. Atmos. Sci., 72, 2109–2130, https://doi.org/10.1175/JAS-D-14-0228.1,
2015.
Smagorinsky, J., Manabe, S., and Holloway, J. L.: Numerical results from a
nine-level general circulation model of the atmosphere, Mon. Weather Rev.,
93, 727–768, https://doi.org/10.1175/1520-0493(1965)093<0727:NRFANL>2.3.CO;2, 1965.
Sun, C., Thresher, A., Keeley, R., Hall, N., Hamilton, M., Chinn, P.,
A.Tran, Goni, G., Villeon, L. P. de la, Carval, T., Cowen, L., Manzella, G.,
Gopalakrishna, V., Guerrero, R., Reseghetti, F., Kanno, Y., Klein, B.,
Rickard, L., Baldoni, A., Lin, S., Ji, F., and Nagaya, Y.: The data
management system for the global temperature and salinity profile programme,
in Proceedings of OceanObs'09: Sustained Ocean Observations and Information
for Society, 931–938, European Space Agency, https://doi.org/10.5270/OceanObs09.cwp.86, 2010.
Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmler, T., and Nerger, L.:
Improving the ocean and atmosphere in a coupled ocean–atmosphere model by
assimilating satellite sea-surface temperature and subsurface profile data,
Q. J. Roy. Meteor. Soc., 146, 4014–4029, https://doi.org/10.1002/qj.3885, 2020.
Torn, R. D., Hakim, G. J., and Snyder, C.: Boundary conditions for
limited-area ensemble Kalman filters, Mon. Weather Rev., 134, 2490–2502,
https://doi.org/10.1175/MWR3187.1, 2006.
Toyoda, T., Fujii, Y., Kuragano, T., Matthews, J. P., Abe, H., Ebuchi, N.,
Usui, N., Ogawa, K., and Kamachi, M.: Improvements to a global ocean data
assimilation system through the incorporation of Aquarius surface salinity
data, Q. J. Roy. Meteor. Soc., 141, 2750–2759, https://doi.org/10.1002/qj.2561,
2015.
Whitaker, J. S. and Hamill, T. M.: Evaluating methods to account for system
errors in ensemble data assimilation, Mon. Weather Rev., 140, 3078–3089,
https://doi.org/10.1175/MWR-D-11-00276.1, 2012.
Yan, Y., Barth, A., and Beckers, J. M.: Comparison of different assimilation
schemes in a sequential Kalman filter assimilation system, Ocean Model., 73,
123–137, https://doi.org/10.1016/j.ocemod.2013.11.002, 2014.
Yin, Y., Alves, O., and Oke, P. R.: An ensemble ocean data assimilation
system for seasonal prediction, Mon. Weather Rev., 139, 786–808,
https://doi.org/10.1175/2010MWR3419.1, 2011.
Ying, Y. and Zhang, F.: An adaptive covariance relaxation method for
ensemble data assimilation, Q. J. Roy. Meteor. Soc., 141, 2898–2906,
https://doi.org/10.1002/qj.2576, 2015.
Zhang, F., Davis, C. A., Kaplan, M. L., and Koch, S. E.: Wavelet analysis and
the governing dynamics of a large-amplitude mesoscale gravity-wave event
along the east coast of the United States, Q. J. Roy. Meteor. Soc.,
127, 2209–2245, https://doi.org/10.1002/qj.49712757702, 2001.
Zhang, F., Snyder, C., and Sun, J.: Impacts of initial estimate and
observation availability on convective-scale data assimilation with an
ensemble Kalman filter, Mon. Weather Rev., 132, 1238–1253,
https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2,
2004.
Zhang, F., Minamide, M., and Clothiaux, E. E.: Potential impacts of
assimilating all-sky infrared satellite radiances from GOES-R on
convection-permitting analysis and prediction of tropical cyclones, Geophys.
Res. Lett., 43, 2954–2963, https://doi.org/10.1002/2016GL068468, 2016.
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Antonov, J. I.,
Locarnini, R. A., Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers,
K. W., Paver, C. R., and Smolyar, I. V.: World Ocean Atlas 2018, Volume 2,
NOAA Atlas NESDIS, 82, 50, https://www.ncei.noaa.gov/sites/default/files/2020-04/woa18_vol2.pdf (last access: 11 November 2022),
2019.
Short summary
We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.
We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which...
