Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9057-2022
© 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-15-9057-2022
© 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
| 
20 Dec 2022
Development and technical paper |
| 20 Dec 2022
| 20 Dec 2022
An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI)
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
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, 4648601, 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
Misako Kachi
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, 3058505, Japan
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Cited articles
Abe, H. and Ebuchi, N.: Evaluation of sea-surface salinity observed by
Aquarius, J. Geophys. Res.-Oceans, 119, 8109–8121,
https://doi.org/10.1002/2014JC010094, 2014.
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ngdc.mgg.dem:316 (last access: 24 November 2022), 2009.
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. Japan, 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 (data available at: https://github.com/brodeau/aerobulk, last
access: 24 November 2022).
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.: Joint assimilation of
Aquarius-derived sea surface salinity and AVHRR-derived sea surface
temperature in an ocean general circulation model using SEEK filter:
Implication for mixed layer depth and barrier layer thickness, J. Geophys.
Res.-Oceans, 120, 6927–6942, https://doi.org/10.1002/2015JC010934, 2015.
Copernicus Marine Service: Global Ocean Along Track L 3 Sea Surface Heights Reprocessed 1993 Ongoing Tailored For Data Assimilation, Copernicus [data set], https://doi.org/10.48670/moi-00146, 2012.
Davis, P. E. D., Lique, C., Johnson, H. L., and Guthrie, J. D.: Competing
effects of elevated vertical mixing and increased freshwater input on the
stratification and sea ice cover in a changing Arctic Ocean, J. Phys.
Oceanogr., 46, 1531–1553, https://doi.org/10.1175/JPO-D-15-0174.1, 2016.
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of
observation, background and analysis-error statistics in observation space,
Q. J. Roy. Meteor. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005.
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. Ocean., 121, 2937–2966, https://doi.org/10.1002/2016JC011716, 2016 (data available at: https://www.aoml.noaa.gov/phod/gdp/hourly_data.php,
last access: 24 November 2022).
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.
Hackert, E., Ballabrera-Poy, J., Busalacchi, A. J., Zhang, R. H., and
Murtugudde, R.: Impact of sea surface salinity assimilation on coupled
forecasts in the tropical Pacific, J. Geophys. Res.-Oceans, 116, C05009,
https://doi.org/10.1029/2010JC006708, 2011.
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.
Isoguchi, O., Kawamura, H., and Oka, E.: Quasi-stationary jets transporting
surface warm waters across the transition zone between the subtropical and
the subarctic gyres in the North Pacific, J. Geophys. Res.-Oceans, 111,
C10003, https://doi.org/10.1029/2005JC003402, 2006.
Jerlov, N. G.: Marine Optics, 2nd edn., Elsevier, eBook ISBN: 9780080870502, https://www.elsevier.com/books/marine-optics/jerlov/978-0-444-41490-8 (last access: 24 November 2022), 1976.
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.
Kida, S., Mitsudera, H., Aoki, S., Guo, X., Ito, S., Kobashi, F., Komori,
N., Kubokawa, A., Miyama, T., Morie, R., Nakamura, H., Nakamura, T., Nakano,
H., Nishigaki, H., Nonaka, M., Sasaki, H., Sasaki, Y. N., Suga, T.,
Sugimoto, S., Taguchi, B., Takaya, K., Tozuka, T., Tsujino, H., and Usui, N.:
Oceanic fronts and jets around Japan: a review, J. Oceanogr., 71, 469–497,
https://doi.org/10.1007/s10872-015-0283-7, 2015.
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. Japan, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015 (data available at: http://search.diasjp.net/en/dataset/JRA55
(last access: 24 November 2022).
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.
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,
Mishonov, A., Technical Editor, NOAA Atlas NESDIS, 81, 52, https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/ (last access: 24 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 Le Vine, D. M.: 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 (data available at: https://podaac.jpl.nasa.gov/,
last access: 24 November 2022).
Mellor, G. L., Ezer, T., and Oey, L.-Y.: The pressure gradient conundrum of
sigma coordinate ocean models, J. Atmos. Ocean. Technol., 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., Zhang, R., Guo, X., Tamura, H., Ambe, D., Lee, J. S., Okuno,
A., Yoshinari, H., Setou, T., and Komatsu, K.: Water mass variability in the
western North Pacific detected in a 15-year eddy resolving ocean reanalysis,
J. Oceanogr., 65, 737–756, https://doi.org/10.1007/s10872-009-0063-3, 2009.
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.
Miyazawa, Y., Varlamov, S. M., Miyama, T., Guo, X., Hihara, T., Kiyomatsu,
K., Kachi, M., Kurihara, Y., and Murakami, H.: Assimilation of
high-resolution sea surface temperature data into an operational
nowcast/forecast system around Japan using a multi-scale three-dimensional
variational scheme, Ocean Dynam., 67, 713–728,
https://doi.org/10.1007/s10236-017-1056-1, 2017.
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.
Miyoshi, T., Yamane, S., and Enomoto, T.: Localizing the error covariance by
physical distances within a local ensemble transform Kalman filter (LETKF),
SOLA, 3, 89–92, https://doi.org/10.2151/sola.2007-023, 2007.
NOAA National Geophysical Data Center: ETOPO1 1 Arc-Minute Global Relief Model, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5C8276M, 2009.
Ohishi, S.: shunohishi/sbPOM-LETKF: sbPOM-LETKF (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6482744, 2022.
Ohishi, S., Hihara, T., Aiki, H., Ishizaka, J., Miyazawa, Y., Kachi, M., and Miyoshi, T.: An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0, Geosci. Model Dev., 15, 8395–8410, https://doi.org/10.5194/gmd-15-8395-2022, 2022.
Pacanowski, R. C. and Philander, S. G. H.: Parameterization of vertical
mixing in numerical models of tropical oceans, J. Phys. Oceanogr., 11,
1443–1451, https://doi.org/10.1175/1520-0485(1981)011<1443:POVMIN>2.0.CO;2, 1981.
Paulson, C. A. and Simpson, J. J.: Irradiance measurements in the upper
ocean, J. Phys. Oceanogr., 7, 952–956,
https://doi.org/10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2,
1977.
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.
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.
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 (Vol. 2), Venice, Italy, 21–25 September 2009, edited by: Hall, J., Harrison, D. E., and Stammer, D., ESA Publication WPP-306, 931–938, https://doi.org/10.5270/OceanObs09.cwp.86, 2010.
Talley, L. D.: Distribution and formation of North Pacific intermediate
water, J. Phys. Oceanogr., 23, 517–537,
https://doi.org/10.1175/1520-0485(1993)023<0517:DAFONP>2.0.CO;2,
1993.
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.
Usui, N., Tsujino, H., Fujii, Y., and Kamachi, M.: Short-range prediction
experiments of the Kuroshio path variabilities south of Japan, Ocean Dynam.,
56, 607–623, https://doi.org/10.1007/s10236-006-0084-z, 2006.
Xu, F. H. and Oey, L. Y.: State analysis using the Local Ensemble Transform
Kalman Filter (LETKF) and the three-layer circulation structure of the Luzon
Strait and the South China Sea, Ocean Dynam., 64, 905–923,
https://doi.org/10.1007/s10236-014-0720-y, 2014.
Yasuda, I.: The origin of the North Pacific intermediate water, J. Geophys.
Res.-Oceans, 102, 893–909, https://doi.org/10.1029/96JC02938, 1997.
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.
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.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
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:
Salinity, Mishonov, A., Technical Editor, NOAA Atlas NESDIS, 82, 50, https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/ (last access: 24 November 2022),
2019.
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.
An adaptive observation error inflation (AOEI) method was proposed for atmospheric data...
