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
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
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.
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...
