Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8395–8410, 2022
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.
Geosci. Model Dev., 15, 8395–8410, 2022
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
An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0
Shun Ohishi et al.
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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
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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.
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
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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
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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
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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.
Qiwen Sun, Takemasa Miyoshi, and Serge Richard
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2022-12, https://doi.org/10.5194/npg-2022-12, 2022
Preprint under review for NPG
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This paper is a follow up of a work by T. Miyoshi and Q. Sun which was published as 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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Moana Ocean Hindcast – a > 25-year simulation for New Zealand waters using the Regional Ocean Modeling System (ROMS) v3.9 model
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GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf
The Baltic Sea Model Intercomparison Project (BMIP) – a platform for model development, evaluation, and uncertainty assessment
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The bulk parameterizations of turbulent air–sea fluxes in NEMO4: the origin of sea surface temperature differences in a global model study
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Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
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Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
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We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Joao Marcos Azevedo Correia de Souza, Sutara H. Suanda, Phellipe P. Couto, Robert O. Smith, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 211–231, https://doi.org/10.5194/gmd-16-211-2023, https://doi.org/10.5194/gmd-16-211-2023, 2023
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The current paper describes the configuration and evaluation of the Moana Ocean Hindcast, a > 25-year simulation of the ocean state around New Zealand using the Regional Ocean Modeling System v3.9. This is the first open-access, long-term, continuous, realistic ocean simulation for this region and provides information for improving the understanding of the ocean processes that affect the New Zealand exclusive economic zone.
Hao Huang, Pengyang Song, Shi Qiu, Jiaqi Guo, and Xueen Chen
Geosci. Model Dev., 16, 109–133, https://doi.org/10.5194/gmd-16-109-2023, https://doi.org/10.5194/gmd-16-109-2023, 2023
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The Oceanic Regional Circulation and Tide Model (ORCTM) is developed to reproduce internal solitary wave dynamics. The three-dimensional nonlinear momentum equations are involved with the nonhydrostatic pressure obtained via solving the Poisson equation. The validation experimental results agree with the internal wave theories and observations, demonstrating that the ORCTM can successfully describe the life cycle of nonlinear internal solitary waves under different oceanic environments.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
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Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
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
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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.
Diego Bruciaferri, Marina Tonani, Isabella Ascione, Fahad Al Senafi, Enda O'Dea, Helene T. Hewitt, and Andrew Saulter
Geosci. Model Dev., 15, 8705–8730, https://doi.org/10.5194/gmd-15-8705-2022, https://doi.org/10.5194/gmd-15-8705-2022, 2022
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More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on the predictive skills of a numerical shelf sea model of the Gulf after changing a few key aspects. Increasing the lateral and vertical resolution and optimising the vertical coordinate system to best represent the leading physical processes at stake significantly improve the accuracy of the simulated dynamics. Additional work may be needed to get real benefit from using a more realistic bathymetry.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Hector S. Torres, Patrice Klein, Jinbo Wang, Alexander Wineteer, Bo Qiu, Andrew F. Thompson, Lionel Renault, Ernesto Rodriguez, Dimitris Menemenlis, Andrea Molod, Christopher N. Hill, Ehud Strobach, Hong Zhang, Mar Flexas, and Dragana Perkovic-Martin
Geosci. Model Dev., 15, 8041–8058, https://doi.org/10.5194/gmd-15-8041-2022, https://doi.org/10.5194/gmd-15-8041-2022, 2022
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Wind work at the air-sea interface is the scalar product of winds and currents and is the transfer of kinetic energy between the ocean and the atmosphere. Using a new global coupled ocean-atmosphere simulation performed at kilometer resolution, we show that all scales of winds and currents impact the ocean dynamics at spatial and temporal scales. The consequential interplay of surface winds and currents in the numerical simulation motivates the need for a winds and currents satellite mission.
Zhanpeng Zhuang, Quanan Zheng, Yongzeng Yang, Zhenya Song, Yeli Yuan, Chaojie Zhou, Xinhua Zhao, Ting Zhang, and Jing Xie
Geosci. Model Dev., 15, 7221–7241, https://doi.org/10.5194/gmd-15-7221-2022, https://doi.org/10.5194/gmd-15-7221-2022, 2022
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We evaluate the impacts of surface waves and internal tides on the upper-ocean mixing in the Indian Ocean. The surface-wave-generated turbulent mixing is dominant if depth is < 30 m, while the internal-tide-induced mixing is larger than surface waves in the ocean interior from 40
to 130 m. The simulated thermal structure, mixed layer depth and surface current are all improved when the mixing schemes are jointly incorporated into the ocean model because of the strengthened vertical mixing.
Giulia Bonino, Doroteaciro Iovino, Laurent Brodeau, and Simona Masina
Geosci. Model Dev., 15, 6873–6889, https://doi.org/10.5194/gmd-15-6873-2022, https://doi.org/10.5194/gmd-15-6873-2022, 2022
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The sea surface temperature (SST) is highly influenced by the transfer of energy driven by turbulent air–sea fluxes (TASFs). In the NEMO ocean general circulation model, TASFs are computed by means of bulk formulas. Bulk formulas require the choice of a given bulk parameterization, which influences the magnitudes of the TASFs. Our results show that parameterization-related SST differences are primarily sensitive to the wind stress differences across parameterizations.
Gustavo M. Marques, Nora Loose, Elizabeth Yankovsky, Jacob M. Steinberg, Chiung-Yin Chang, Neeraja Bhamidipati, Alistair Adcroft, Baylor Fox-Kemper, Stephen M. Griffies, Robert W. Hallberg, Malte F. Jansen, Hemant Khatri, and Laure Zanna
Geosci. Model Dev., 15, 6567–6579, https://doi.org/10.5194/gmd-15-6567-2022, https://doi.org/10.5194/gmd-15-6567-2022, 2022
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We present an idealized ocean model configuration and a set of simulations performed using varying horizontal grid spacing. While the model domain is idealized, it resembles important geometric features of the Atlantic and Southern oceans. The simulations described here serve as a framework to effectively study mesoscale eddy dynamics, to investigate the effect of mesoscale eddies on the large-scale dynamics, and to test and evaluate eddy parameterizations.
David E. Gwyther, Colette Kerry, Moninya Roughan, and Shane R. Keating
Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, https://doi.org/10.5194/gmd-15-6541-2022, 2022
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The ocean current flowing along the southeastern coast of Australia is called the East Australian Current (EAC). Using computer simulations, we tested how surface and subsurface observations might improve models of the EAC. Subsurface observations are particularly important for improving simulations, and if made in the correct location and time, can have impact 600 km upstream. The stability of the current affects model estimates could be capitalized upon in future observing strategies.
Giorgio Micaletto, Ivano Barletta, Silvia Mocavero, Ivan Federico, Italo Epicoco, Giorgia Verri, Giovanni Coppini, Pasquale Schiano, Giovanni Aloisio, and Nadia Pinardi
Geosci. Model Dev., 15, 6025–6046, https://doi.org/10.5194/gmd-15-6025-2022, https://doi.org/10.5194/gmd-15-6025-2022, 2022
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The full exploitation of supercomputing architectures requires a deep revision of the current climate models. This paper presents the parallelization of the three-dimensional hydrodynamic model SHYFEM (System of HydrodYnamic Finite Element Modules). Optimized numerical libraries were used to partition the model domain and solve the sparse linear system of equations in parallel. The performance assessment demonstrates a good level of scalability with a realistic configuration used as a benchmark.
Young-Kwang Choi, Fengyan Shi, Matt Malej, Jane M. Smith, James T. Kirby, and Stephan T. Grilli
Geosci. Model Dev., 15, 5441–5459, https://doi.org/10.5194/gmd-15-5441-2022, https://doi.org/10.5194/gmd-15-5441-2022, 2022
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The multi-grid-nesting technique is an important methodology used for modeling transoceanic tsunamis and coastal effects. In this study, we developed a two-way nesting interface in a multi-grid-nesting system for the Boussinesq wave model FUNWAVE-TVD. The interface acts as a
backboneof the nesting framework, handling data input, output, time sequencing, and internal interactions between grids at different scales.
Chen Zhao, Rupert Gladstone, Benjamin Keith Galton-Fenzi, David Gwyther, and Tore Hattermann
Geosci. Model Dev., 15, 5421–5439, https://doi.org/10.5194/gmd-15-5421-2022, https://doi.org/10.5194/gmd-15-5421-2022, 2022
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We use a coupled ice–ocean model to explore an oscillation feature found in several contributing models to MISOMIP1. The oscillation is closely related to the discretized grounding line retreat and likely strengthened by the buoyancy–melt feedback and/or melt–geometry feedback near the grounding line, and frequent ice–ocean coupling. Our model choices have a non-trivial impact on mean melt and ocean circulation strength, which might be interesting for the coupled ice–ocean community.
Benoît Pasquier, Sophia K. V. Hines, Hengdi Liang, Yingzhe Wu, Steven L. Goldstein, and Seth G. John
Geosci. Model Dev., 15, 4625–4656, https://doi.org/10.5194/gmd-15-4625-2022, https://doi.org/10.5194/gmd-15-4625-2022, 2022
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Neodymium isotopes in seawater have the potential to provide key information about ocean circulation, both today and in the past. This can shed light on the underlying drivers of global climate, which will improve our ability to predict future climate change, but uncertainties in our understanding of neodymium cycling have limited use of this tracer. We present a new model of neodymium in the modern ocean that runs extremely fast, matches observations, and is freely available for development.
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, https://doi.org/10.5194/gmd-15-4373-2022, 2022
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Sea ice models are often implemented for very large domains beyond the regions of sea ice formation, such as the whole Arctic or all of Antarctica. In this study, we implement changes in the Los Alamos Sea Ice Model, allowing it to be implemented for relatively small regions within the Arctic or Antarctica and yet considering the presence and influence of sea ice outside the represented areas. Such regional implementations are important when spatially detailed results are required.
Elias J. Hunter, Heidi L. Fuchs, John L. Wilkin, Gregory P. Gerbi, Robert J. Chant, and Jessica C. Garwood
Geosci. Model Dev., 15, 4297–4311, https://doi.org/10.5194/gmd-15-4297-2022, https://doi.org/10.5194/gmd-15-4297-2022, 2022
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ROMSPath is an offline particle tracking model tailored for use with output from Regional Ocean Modeling System (ROMS) simulations. It is an update to an established system, the Lagrangian TRANSport (LTRANS) model, including a number of improvements. These include a modification of the model coordinate system which improved accuracy and numerical efficiency, and added functionality for nested grids and Stokes drift.
Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, https://doi.org/10.5194/gmd-15-2183-2022, 2022
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Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Olivier Sulpis, Matthew P. Humphreys, Monica M. Wilhelmus, Dustin Carroll, William M. Berelson, Dimitris Menemenlis, Jack J. Middelburg, and Jess F. Adkins
Geosci. Model Dev., 15, 2105–2131, https://doi.org/10.5194/gmd-15-2105-2022, https://doi.org/10.5194/gmd-15-2105-2022, 2022
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A quarter of the surface of the Earth is covered by marine sediments rich in calcium carbonates, and their dissolution acts as a giant antacid tablet protecting the ocean against human-made acidification caused by massive CO2 emissions. Here, we present a new model of sediment chemistry that incorporates the latest experimental findings on calcium carbonate dissolution kinetics. This model can be used to predict how marine sediments evolve through time in response to environmental perturbations.
Alisée A. Chaigneau, Guillaume Reffray, Aurore Voldoire, and Angélique Melet
Geosci. Model Dev., 15, 2035–2062, https://doi.org/10.5194/gmd-15-2035-2022, https://doi.org/10.5194/gmd-15-2035-2022, 2022
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Climate-change-induced sea level rise is a major threat for coastal and low-lying regions. Projections of coastal sea level changes are thus of great interest for coastal risk assessment and have significantly developed in recent years. In this paper, the objective is to provide high-resolution (6 km) projections of sea level changes in the northeastern Atlantic region bordering western Europe. For that purpose, a regional model is used to refine existing coarse global projections.
Victor Onink, Erik van Sebille, and Charlotte Laufkötter
Geosci. Model Dev., 15, 1995–2012, https://doi.org/10.5194/gmd-15-1995-2022, https://doi.org/10.5194/gmd-15-1995-2022, 2022
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Turbulent mixing is a vital process in 3D modeling of particle transport in the ocean. However, since turbulence occurs on very short spatial scales and timescales, large-scale ocean models generally have highly simplified turbulence representations. We have developed parametrizations for the vertical turbulent transport of buoyant particles that can be easily applied in a large-scale particle tracking model. The predicted vertical concentration profiles match microplastic observations well.
Gaston Irrmann, Sébastien Masson, Éric Maisonnave, David Guibert, and Erwan Raffin
Geosci. Model Dev., 15, 1567–1582, https://doi.org/10.5194/gmd-15-1567-2022, https://doi.org/10.5194/gmd-15-1567-2022, 2022
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To be efficient on supercomputers, software must be high-performance at computing many concurrent tasks. Communications between tasks is often necessary but time consuming, and ocean modelling software NEMO 4.0 is no exception.
In this work we describe approaches enabling fewer communications, an optimization to share the workload more equally between tasks and a new flexible configuration to assess NEMO's performance easily.
Xueming Zhu, Ziqing Zu, Shihe Ren, Miaoyin Zhang, Yunfei Zhang, Hui Wang, and Ang Li
Geosci. Model Dev., 15, 995–1015, https://doi.org/10.5194/gmd-15-995-2022, https://doi.org/10.5194/gmd-15-995-2022, 2022
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SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d since 2013. Comprehensive updates have been conducted to the configurations of SCSOFS's physical model and data assimilation scheme in order to improve its forecasting skill. The three most sensitive updates are highlighted. Scientific comparison and accuracy assessment results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1.
Tingfeng Wu, Boqiang Qin, Anning Huang, Yongwei Sheng, Shunxin Feng, and Céline Casenave
Geosci. Model Dev., 15, 745–769, https://doi.org/10.5194/gmd-15-745-2022, https://doi.org/10.5194/gmd-15-745-2022, 2022
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Most hydrodynamic models were initially developed based in marine environments. They cannot be directly applied to large lakes. Based on field observations and numerical experiments of a large shallow lake, we developed a hydrodynamic model by adopting new schemes of wind stress, wind waves, and turbulence for large lakes. Our model can greatly improve the simulation of lake currents. This study will be a reminder to limnologists to prudently use ocean models to study lake hydrodynamics.
Jingyuan Li, Qinghe Zhang, and Tongqing Chen
Geosci. Model Dev., 15, 105–127, https://doi.org/10.5194/gmd-15-105-2022, https://doi.org/10.5194/gmd-15-105-2022, 2022
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A numerical model, ISWFoam with a modified k–ω SST model, is developed to simulate internal solitary waves (ISWs) in continuously stratified, incompressible, viscous fluids based on a fully three-dimensional (3D) Navier–Stokes equation with the finite-volume method. ISWFoam can accurately simulate the generation and evolution of ISWs, the ISW breaking phenomenon, waveform inversion of ISWs, and the interaction between ISWs and complex topography.
Matthew P. Humphreys, Ernie R. Lewis, Jonathan D. Sharp, and Denis Pierrot
Geosci. Model Dev., 15, 15–43, https://doi.org/10.5194/gmd-15-15-2022, https://doi.org/10.5194/gmd-15-15-2022, 2022
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The ocean helps to mitigate our impact on Earth's climate by absorbing about a quarter of the carbon dioxide (CO2) released by human activities each year. However, once absorbed, chemical reactions between CO2 and water reduce seawater pH (
ocean acidification), which may have adverse effects on marine ecosystems. Our Python package, PyCO2SYS, models the chemical reactions of CO2 in seawater, allowing us to quantify the corresponding changes in pH and related chemical properties.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
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We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Trevor J. McDougall, Paul M. Barker, Ryan M. Holmes, Rich Pawlowicz, Stephen M. Griffies, and Paul J. Durack
Geosci. Model Dev., 14, 6445–6466, https://doi.org/10.5194/gmd-14-6445-2021, https://doi.org/10.5194/gmd-14-6445-2021, 2021
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We show that the way that the air–sea heat flux is treated in ocean models means that the model's temperature variable should be interpreted as being Conservative Temperature, irrespective of whether the equation of state used in an ocean model is EOS-80 or TEOS-10.
Paul R. Halloran, Jennifer K. McWhorter, Beatriz Arellano Nava, Robert Marsh, and William Skirving
Geosci. Model Dev., 14, 6177–6195, https://doi.org/10.5194/gmd-14-6177-2021, https://doi.org/10.5194/gmd-14-6177-2021, 2021
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This paper describes the latest version of a simple model for simulating coastal oceanography in response to changes in weather and climate. The latest revision of this model makes scientific improvements but focuses on improvements that allow the model to be run simply at large scales and for long periods of time to explore the implications of (for example) future climate change along large areas of coastline.
Fucang Zhou, Jianzhong Ge, Dongyan Liu, Pingxing Ding, Changsheng Chen, and Xiaodao Wei
Geosci. Model Dev., 14, 6049–6070, https://doi.org/10.5194/gmd-14-6049-2021, https://doi.org/10.5194/gmd-14-6049-2021, 2021
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In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed to determine the dynamic growth and drifting pattern of floating macroalgae. Based on Lagrangian tracking, the macroalgae bloom is jointly controlled by ocean flows, sea surface wind, temperature, irradiation, and nutrients. The FMGDM was robust in successfully reproducing the spatial and temporal dynamics of the massive green tide around the Yellow Sea.
Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
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We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
David A. Griffin, Mike Herzfeld, Mark Hemer, and Darren Engwirda
Geosci. Model Dev., 14, 5561–5582, https://doi.org/10.5194/gmd-14-5561-2021, https://doi.org/10.5194/gmd-14-5561-2021, 2021
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In support of the developing ocean renewable energy sector, and indeed all mariners, we have developed a new tidal model for Australian waters and thoroughly evaluated it using a new compilation of tide gauge and current meter data. We show that while there is certainly room for improvement, the model provides useful predictions of tidal currents for about 80 % (by area) of Australian shelf waters. So we intend to commence publishing tidal current predictions for those regions soon.
Jingzhe Li, Piyang Liu, Shuyu Sun, Zhifeng Sun, Yongzhang Zhou, Liang Gong, Jinliang Zhang, and Dongxing Du
Geosci. Model Dev., 14, 4925–4937, https://doi.org/10.5194/gmd-14-4925-2021, https://doi.org/10.5194/gmd-14-4925-2021, 2021
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This paper introduces Sedapp, a basin fill simulation tool. Sedapp is an open-source computer code written in R language. Using this program, one can simulate the formation of sedimentary strata, especially in shallow marine environments injected by rivers. With proper parameter settings, the simulation results are very similar to the real geological bodies. Sedapp can also be used in continental fault basin environments, which may serve as a tool for oil exploration.
Gwenaëlle Gremion, Louis-Philippe Nadeau, Christiane Dufresne, Irene R. Schloss, Philippe Archambault, and Dany Dumont
Geosci. Model Dev., 14, 4535–4554, https://doi.org/10.5194/gmd-14-4535-2021, https://doi.org/10.5194/gmd-14-4535-2021, 2021
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An accurate description of detritic organic particles is key to improving estimations of carbon export into the ocean abyss in ocean general circulation models. Yet, most parametrization are numerically impractical due to the required number of tracers needed to resolve the particle size spectrum. Here, a new parametrization that aims to minimize the tracers number while accurately describing the particles dynamics is developed and tested in a series of idealized numerical experiments.
Christo Rautenbach, Julia C. Mullarney, and Karin R. Bryan
Geosci. Model Dev., 14, 4241–4247, https://doi.org/10.5194/gmd-14-4241-2021, https://doi.org/10.5194/gmd-14-4241-2021, 2021
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The simulation of ocean waves is important for various reasons, e.g. ship route safety and coastal vulnerability assessments. SWAN is a popular tool with which ocean waves may be predicted. Simulations using this tool can be computationally expensive. The present study thus aimed to understand which typical parallel-computing SWAN model set-up will be most effective. There thus do exist configurations where these simulations are most time-saving and effective.
Qing Li, Jorn Bruggeman, Hans Burchard, Knut Klingbeil, Lars Umlauf, and Karsten Bolding
Geosci. Model Dev., 14, 4261–4282, https://doi.org/10.5194/gmd-14-4261-2021, https://doi.org/10.5194/gmd-14-4261-2021, 2021
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Different ocean vertical mixing schemes are usually developed in different modeling framework, making the comparison across such schemes difficult. Here, we develop a consistent framework for testing, comparing, and applying different ocean mixing schemes by integrating CVMix into GOTM, which also extends the capability of GOTM towards including the effects of ocean surface waves. A suite of test cases and toolsets for developing and evaluating ocean mixing schemes is also described.
Julien Jouanno, Rachid Benshila, Léo Berline, Antonin Soulié, Marie-Hélène Radenac, Guillaume Morvan, Frédéric Diaz, Julio Sheinbaum, Cristele Chevalier, Thierry Thibaut, Thomas Changeux, Frédéric Menard, Sarah Berthet, Olivier Aumont, Christian Ethé, Pierre Nabat, and Marc Mallet
Geosci. Model Dev., 14, 4069–4086, https://doi.org/10.5194/gmd-14-4069-2021, https://doi.org/10.5194/gmd-14-4069-2021, 2021
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The tropical Atlantic has been facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. We developed a modeling framework based on the NEMO ocean model, which integrates transport by currents and waves, and physiology of Sargassum with varying internal nutrient quota, and considers stranding at the coast. Results demonstrate the ability of the model to reproduce and forecast the seasonal cycle and large-scale distribution of Sargassum biomass.
Andrew Yool, Julien Palmiéri, Colin G. Jones, Lee de Mora, Till Kuhlbrodt, Ekatarina E. Popova, A. J. George Nurser, Joel Hirschi, Adam T. Blaker, Andrew C. Coward, Edward W. Blockley, and Alistair A. Sellar
Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, https://doi.org/10.5194/gmd-14-3437-2021, 2021
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The ocean plays a key role in modulating the Earth’s climate. Understanding this role is critical when using models to project future climate change. Consequently, it is necessary to evaluate their realism against the ocean's observed state. Here we validate UKESM1, a new Earth system model, focusing on the realism of its ocean physics and circulation, as well as its biological cycles and productivity. While we identify biases, generally the model performs well over a wide range of properties.
Sebastiaan J. van de Velde, Dominik Hülse, Christopher T. Reinhard, and Andy Ridgwell
Geosci. Model Dev., 14, 2713–2745, https://doi.org/10.5194/gmd-14-2713-2021, https://doi.org/10.5194/gmd-14-2713-2021, 2021
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Biogeochemical interactions between iron and sulfur are central to the long-term biogeochemical evolution of Earth’s oceans. Here, we introduce an iron–sulphur cycle in a model of Earth's oceans. Our analyses show that the results of the model are robust towards parameter choices and that simulated concentrations and reactions are comparable to those observed in ancient ocean analogues (anoxic lakes). Our model represents an important step forward in the study of iron–sulfur cycling.
Katherine M. Smith, Skyler Kern, Peter E. Hamlington, Marco Zavatarelli, Nadia Pinardi, Emily F. Klee, and Kyle E. Niemeyer
Geosci. Model Dev., 14, 2419–2442, https://doi.org/10.5194/gmd-14-2419-2021, https://doi.org/10.5194/gmd-14-2419-2021, 2021
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We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The model provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of real-world data can be achieved with a small number of variables.
Chia-Wei Hsu, Jianjun Yin, Stephen M. Griffies, and Raphael Dussin
Geosci. Model Dev., 14, 2471–2502, https://doi.org/10.5194/gmd-14-2471-2021, https://doi.org/10.5194/gmd-14-2471-2021, 2021
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The new surface forcing from JRA55-do (OMIP II) significantly improved the underestimated sea level trend across the entire Pacific Ocean along 10° N in the simulation forced by CORE (OMIP I). We summarize and list out the reasons for the existing sea level biases across all studied timescales as a reference for improving the sea level simulation in the future. This study on the evaluation and improvement of ocean climate models should be of broad interest to a large modeling community.
Oliver Gutjahr, Nils Brüggemann, Helmuth Haak, Johann H. Jungclaus, Dian A. Putrasahan, Katja Lohmann, and Jin-Song von Storch
Geosci. Model Dev., 14, 2317–2349, https://doi.org/10.5194/gmd-14-2317-2021, https://doi.org/10.5194/gmd-14-2317-2021, 2021
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We compare four ocean vertical mixing schemes in 100-year coupled simulations with the Max Planck Institute Earth System Model (MPI-ESM1.2) and analyse their model biases. Overall, the mixing schemes modify biases in the ocean interior that vary with region and variable but produce a similar global bias pattern. We therefore cannot classify any scheme as superior but conclude that the chosen mixing scheme may be important for regional biases.
Katsumi Matsumoto, Tatsuro Tanioka, and Jacob Zahn
Geosci. Model Dev., 14, 2265–2288, https://doi.org/10.5194/gmd-14-2265-2021, https://doi.org/10.5194/gmd-14-2265-2021, 2021
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MESMO is a mathematical model that represents the essential components of the Earth, such as the global ocean, atmosphere, and sea ice. It is used to study the global climate and the global carbon cycle. We describe the third version of MESMO. A novel feature of the new version is its mathematical representations of the chemical composition of marine phytoplankton and the marine dissolved organic matter, which are both recognized as important for the global ocean carbon cycle.
Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 14, 2057–2074, https://doi.org/10.5194/gmd-14-2057-2021, https://doi.org/10.5194/gmd-14-2057-2021, 2021
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Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
Qing Li and Luke Van Roekel
Geosci. Model Dev., 14, 2011–2028, https://doi.org/10.5194/gmd-14-2011-2021, https://doi.org/10.5194/gmd-14-2011-2021, 2021
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Physical processes in the ocean span multiple spatial and temporal scales. Simultaneously resolving all these in a simulation is computationally challenging. Here we develop a more efficient technique to better study the interactions across scales, particularly focusing on the ocean surface turbulent mixing, by coupling a global ocean circulation model MPAS-Ocean and a large eddy simulation model PALM. The latter is customized and ported on a GPU to further accelerate the computation.
Yang Feng, Dimitris Menemenlis, Huijie Xue, Hong Zhang, Dustin Carroll, Yan Du, and Hui Wu
Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, https://doi.org/10.5194/gmd-14-1801-2021, 2021
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Simulation of coastal plume regions was improved in global ECCOv4 with a series of sensitivity tests. We find modeled SSS is closer to SMAP when using daily point-source runoff as well as increasing the resolution from coarse to intermediate. The plume characteristics, freshwater transport, and critical water properties are modified greatly. But this may not happen with a further increase to high resolution. The study will advance the seamless modeling of land–ocean–atmosphere feedback in ESMs.
Gregory C. Smith, Yimin Liu, Mounir Benkiran, Kamel Chikhar, Dorina Surcel Colan, Audrey-Anne Gauthier, Charles-Emmanuel Testut, Frederic Dupont, Ji Lei, François Roy, Jean-François Lemieux, and Fraser Davidson
Geosci. Model Dev., 14, 1445–1467, https://doi.org/10.5194/gmd-14-1445-2021, https://doi.org/10.5194/gmd-14-1445-2021, 2021
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Canada's coastlines include diverse ocean environments. In response to the strong need to support marine activities and security, we present the first pan-Canadian operational regional ocean analysis system. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach. Innovations are compared to those from the Canadian global analysis system. Particular improvements are found near the Gulf Stream due to the higher model grid resolution.
William J. Pringle, Damrongsak Wirasaet, Keith J. Roberts, and Joannes J. Westerink
Geosci. Model Dev., 14, 1125–1145, https://doi.org/10.5194/gmd-14-1125-2021, https://doi.org/10.5194/gmd-14-1125-2021, 2021
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We improve and test a computer model that simulates tides and storm surge over all of Earth's oceans and seas. The model varies mesh resolution (triangular element sizes) freely so that coastal areas, especially storm landfall locations, are well-described. We develop systematic tests of the resolution in order to suggest good mesh design criteria that balance computational efficiency with accuracy for both global astronomical tides and coastal storm tides under extreme weather forcing.
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...
