Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2635-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-2635-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 May 2021
Development and technical paper |
| 12 May 2021
| 12 May 2021
Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0
Chao Sun
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
Li Liu
CORRESPONDING AUTHOR
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
Southern Marine Science and Engineering Guangdong Laboratory,
Zhuhai, China
Ruizhe Li
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
Xinzhu Yu
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
Biao Zhao
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
First Institute of Oceanography, Ministry of Natural Resources,
Qingdao, China
Key Lab of Marine Science and Numerical Modeling, Ministry of
Natural Resources, Qingdao, China
Guansuo Wang
First Institute of Oceanography, Ministry of Natural Resources,
Qingdao, China
Key Lab of Marine Science and Numerical Modeling, Ministry of
Natural Resources, Qingdao, China
Juanjuan Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China
Fangli Qiao
First Institute of Oceanography, Ministry of Natural Resources,
Qingdao, China
Key Lab of Marine Science and Numerical Modeling, Ministry of
Natural Resources, Qingdao, China
Bin Wang
CORRESPONDING AUTHOR
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China
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Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-52, https://doi.org/10.5194/gmd-2022-52, 2022
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A new global surface wave-tide-circulation coupled ocean model FIO-COM32 with resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are proved to be important contributors to the significant improvements of FIO-COM32 simulations. It should be the time to merge these separated model components (surface wave, tidal current and ocean circulation) for new generation ocean model development.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
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We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Shihe Ren, Xi Liang, Qizhen Sun, Hao Yu, L. Bruno Tremblay, Bo Lin, Xiaoping Mai, Fu Zhao, Ming Li, Na Liu, Zhikun Chen, and Yunfei Zhang
Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, https://doi.org/10.5194/gmd-14-1101-2021, 2021
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Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of sea ice, we coupled a sea ice model with an atmospheric and ocean model to form a fully coupled system. The sea ice simulation results of this coupled system demonstrated that a two-way coupled model has better performance in terms of sea ice, especially in summer. This indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
Shiming Xu, Jialiang Ma, Lu Zhou, Yan Zhang, Jiping Liu, and Bin Wang
Geosci. Model Dev., 14, 603–628, https://doi.org/10.5194/gmd-14-603-2021, https://doi.org/10.5194/gmd-14-603-2021, 2021
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A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45, 0.15, and 0.05°. Based on atmospherically forced sea ice experiments, the model simulates reasonable sea ice kinematics and scaling properties. Landfast ice thickness can also be systematically shifted due to non-convergent solutions to an
elastic–viscous–plastic (EVP) model. This work is a framework for multi-scale modeling of the ocean and sea ice with CESM.
Hao Yu, Li Liu, Chao Sun, Ruizhe Li, Xinzhu Yu, Cheng Zhang, Zhiyuan Zhang, and Bin Wang
Geosci. Model Dev., 13, 6253–6263, https://doi.org/10.5194/gmd-13-6253-2020, https://doi.org/10.5194/gmd-13-6253-2020, 2020
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Ruizi Shi, Fanghua Xu, Li Liu, Zheng Fan, Hao Yu, Xiang Li, and Yunfei Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-327, https://doi.org/10.5194/gmd-2020-327, 2020
Revised manuscript not accepted
Short summary
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To better understand the effects of surface waves, we developed a coupled global atmosphere-ocean-wave system. Processes of Langmuir circulations and sea surface momentum roughness were considered. Results from a series of 7-day forecasts show the Langmuir circulations can reduce the biases of warm sea surface temperature and shallow mixed layer in the Antarctic circumpolar current during austral summer. Whereas surface roughness enables improvements to overestimated 10-m wind and wave height.
Cited articles
Andersson, E., Haseler, J., Unden, P., Courtier, P., Kelly, G., Vasiljevic,
D., and Thepaut, J.: The ECMWF implementation of three-dimensional
variational assimilation (3D-Var). III: Experimental results, Q. J. Roy.
Meteor. Soc., 124, 1831–1860, 1998.
Anderson, J. and Collins, N.: Scalable implementations of ensemble filter
algorithms for data assimilation, J. Atmos. Ocean Technol., 24, 1452–1463,
2007.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Arellano, A.: The Data Assimilation Research Testbed: A Community Facility,
B. Am. Meteorol. Soc., 90, 1283–1296, 2009.
Bishop, C. and Hodyss, D.: Adaptive ensemble covariance localization in
ensemble 4D-VAR state estimation, Mon. Weather Rev., 139, 1241–1255, 2011.
Blumberg, A. and Mellor, G.: A description of a three-dimensional coastal
ocean circulation model, in: Three-Dimensional Coastal Ocean Models, edited
by: Heaps, N. S., pp. 1–16, AGU, Washington, DC, 1987.
Bonavita, M., Isaksen, L., and Holm, E.: On the use of EDA background-error
variances in the ECMWF 4D-Var, Q. J. Roy. Meteorol. Soc., 138, 1540–1559,
2012.
Bonavita, M., Holm, E., Isaksen, L., and Fisher, M. A.: The evolution of the
ECMWF hybrid data assimilation system, Q. J. Roy. Meteor. Soc., 142,
287–303, 2016.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly,
A.: Unified modeling and prediction of weather and climate: a 25 year
journey, B. Am. Meteorol. Soc., 93, 1865–1877,
https://doi.org/10.1175/BAMS-D-12-00018.1, 2012.
Browne, P. and Wilson, S.: A simple method for integrating a complex model
into an ensemble data assimilation system using MPI, Environ. Modell.
Softw., 68, 122–128, 2015.
Browne, P., de Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly
coupled ocean-atmosphere data assimilation in the ECMWF NWP system, Remote
Sens., 11, 1–24, 2019.
Brunet, G., Jones, S., and Ruti, P.: Seamless prediction of the Earth
System: from minutes to months, Tech. Rep. WWOSC-2014, World Meteorological
Organization, 2015.
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L.,
Heilliette, S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J., and
Zadra, A.: Implementation of deterministic weather forecasting systems based
on ensemble-variational data assimilation at Environment Canada. Part I: the
global system, Mon. Weather Rev., 143, 2532–2559, 2015.
Courtier, P., Thepaut, J., and Hollingsworth, A.: A strategy for operational
implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteor.
Soc., 120, 1367–1387, 1994.
Courtier, P., Andersson, E., Heckley, W., Vasiljevic, D., Hamrud, M.,
Hollingsworth, A., and Pailleux, J.: The ECMWF implementation of
three-dimensional variational assimilation (3D-Var). I: Formulation, Q. J.
Roy. Meteor. Soc., 124, 1783–1807, 1998.
Craig, A., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth
system modeling developed for CCSM4 and CESM1, Int. J. High Perform. Comput.
Appl., 26, 31–42, 2012.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017.
Etherton, B. and Bishop, C.: Resilience of hybrid ensemble/3DVAR analysis
schemes to model error and ensemble covariance error, Mon. Weather Rev.,
132, 1065–1080, 2004.
Evensen, G.: The ensemble kalman filter: theoretical formulation and
practical implementation, Ocean Dyn., 53, 343–367, 2003.
Fisher, M.: Background error covariance modelling, in: Proceedings of
Seminar on Recent Developments in Data Assimilation for Atmosphere and
Ocean, Reading, UK, 8–12 September 2003, 45–63, 2003.
Fujii, Y., Nakaegawa, T., Matsumoto, S., Yasuda, T., Yamanaka, G., and
Kamachi, M.: Coupled climate simulation by constraining ocean fields in a
coupled model with ocean data, J. Clim., 22, 5541–5557, 2009.
Fujii, Y., Kamachi, M., Nakaegawa, T., Yasuda, T., Yamanaka, G., Toyoda, T.,
Ando, K. and Matsumoto, S.: Assimilating ocean observation data for ENSO
monitoring and forecasting, in: Climate Variability – Some Aspects,
Challenges and Prospects, edited by: Hannachi, A., InTechOpen, Rijeka,
Croatia, 75–98, 2011.
Gandin, L.: Objective analysis of meteorological fields. By L. S. Gandin. Translated from the Russian. Jerusalem (Israel Program for Scientific Translations), Q. J. Roy. Meteor. Soc., 393, 447–447, https://doi.org/10.1002/qj.49709239320, 1966.
Gauthier, P., Charette, C., Fillion, L., Koclas, P., and Laroche, S.:
Implementation of a 3D variational data assimilation system at the Canadian
Meteorological Center. Part I: The global analysis, Atmos. Ocean, 37,
103–156, 1999.
Goodliff, M., Bruening, T., Schwichtenberg, F., Li, X., Lindenthal, A.,
Lorkowski, I., and Nerger, L.: Temperature assimilation into a coastal
ocean-biogeochemical model: assessment of weakly and strongly coupled data
assimilation, Ocean Dyn., 69, 1217–1237, 2019.
Hamill, T.: A hybrid ensemble kalman filter-3D variational analysis scheme,
Mon. Weather Rev., 128, 2905–2919, 2000.
Heinzeller, D., Duda, M. G., and Kunstmann, H.: Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: an extreme scaling experiment, Geosci. Model Dev., 9, 77–110, https://doi.org/10.5194/gmd-9-77-2016, 2016.
Hoke, J. and Anthes, R.: The initialization of numerical models by a dynamic
initialization technique, Mon. Weather Rev., 104, 1551–1556, 1976.
Hoskins, B.: The potential for skill across the range of the seamless
weather-climate prediction problem: a stimulus for our science, Q. J. Roy.
Meteor. Soc., 139, 573–584, 2013.
Houtekamer, P. and Mitchell, H.: Data assimilation using an ensemble kalman
filter technique, Mon. Weather Rev., 126, 796–811, 1998.
Hunt, B., Kostelich, E., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform kalman filter, Phys. D Nonlinear Phenom., 230, 112–126, 2007.
Kalnay, E.: Atmospheric modeling, data assimilation and predictability,
Cambridge University Press, Cambridge, UK, 364 pp., 2002.
Laloyaux, P., Thepaut, J., and Dee, D.: Impact of scatterometer surface wind
data in the ECMWF coupled assimilation system, Mon. Weather Rev., 144,
1203–1217, 2016.
Laloyaux, P., Frolov, S., Benjamin Ménétrier, and Bonavita, M.:
Implicit and explicit cross-correlations in coupled data assimilation, Q. J.
Roy. Meteor. Soc., 144, 1851–1863, https://doi.org/10.1002/qj.3373, 2018.
Lea, D., Mirouze, I., Martin, M., King, R., Hines, A., Walters, D., and
Thurlow, M.: Assessing a new coupled data assimilation system based on the
met office coupled atmosphere-land-ocean-sea ice model, Mon. Weather
Rev., 143, 4678–4694, 2015.
Liu, H., Hu, M., Ge, G., Stark, D., Shao, H., Newman, K., and Whitaker, J.:
Ensemble Kalman Filter (EnKF) User's Guide Version 1.3, Developmental
Testbed Center, available at:
https://dtcenter.org/community-code/ensemble-kalman-filter-system-enkf/documentation (last access: 15 April 2020),
80 pp., 2018.
Liu, L., Yang, G., Wang, B., Zhang, C., Li, R., Zhang, Z., Ji, Y., and Wang, L.: C-Coupler1: a Chinese community coupler for Earth system modeling, Geosci. Model Dev., 7, 2281–2302, https://doi.org/10.5194/gmd-7-2281-2014, 2014.
Liu, L., Zhang, C., Li, R., Wang, B., and Yang, G.: C-Coupler2: a flexible and user-friendly community coupler for model coupling and nesting, Geosci. Model Dev., 11, 3557–3586, https://doi.org/10.5194/gmd-11-3557-2018, 2018.
Lorenc, A.: The potential of the ensemble Kalman filter for NWP-A comparison
with 4D-VAR, Q. J. Roy. Meteor. Soc., 129, 3183–3203, 2003a.
Lorenc, A.: Modelling of error covariances by 4D-Var data assimilation, Q.
J. Roy. Meteor. Soc., 129, 3167–3182, 2003b.
Lorenc, A., Ballard, S., Bell, R., Ingleby, N., Andrews, P., Barker, D.,
Bray, J., Clayton, A., Dalby, T., Li, D., Payne, T., and Saunders, F.: The
Met. Office global three-dimensional variational data assimilation scheme,
Q. J. Roy. Meteor. Soc., 126, 2991–3012, 2000.
Lu, F., Liu, Z., Zhang S., and Liu Y.: Strongly coupled data assimilation
using leading averaged coupled covariance (LACC). Part I: Simple model
study, Mon. Weather Rev., 143, 3823–3837, https://doi.org/10.1175/MWR-D-14-00322.1,
2015a.
Lu, F., Liu, Z., Zhang, S., Liu, Y., and Jacob, R.: Strongly coupled data
assimilation using leading averaged coupled covariance (LACC). Part II: GCM
Experiments, Mon. Weather Rev., 143, 4645–4659, https://doi.org/10.1175/MWR-D-14-00322.1, 2015b.
Ma, X., Lu, X., Yu, M., Zhu, H., and Chen, J.: Progress on hybrid
ensemble-variational data assimilation in numerical weather prediction, J.
Trop. Meteorol., 30, 1188–1195, 2014.
Mochizuki, T., Masuda, S., Ishikawa, Y., and Awaji, T.: Multiyear climate
prediction with initialization based on 4D-Var data assimilation, Geophys.
Res. Lett., 43, 3903–3910, 2016.
Mulholland, D., Laloyaux, P., Haines, K., and Balmaseda, M.: Origin and
impact of initialization shocks in coupled atmosphere–ocean forecasts, Mon.
Weather Rev., 143, 4631–4644, https://doi.org/10.1175/MWR-D-15-0076.1, 2015.
Nerger, L. and Hiller, W.: Software for Ensemble-based Data Assimilation
Systems – Implementation Strategies and Scalability, Comput. Geosci., 55,
110–118, 2013.
Nerger, L., Hiller, W., and Schröter, J.: PDAF – The Parallel Data
Assimilation Framework: Experiences with Kalman filtering, in: Use of High
Performance Computing in Meteorology – Proceedings of the 11, ECMWF
Workshop, edited by: Zwieflhofer, W. and Mozdzynski, G., pp. 63–83, World
Scientific, 2005.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
Oke, P., Allen, J., Miller, R., Egbert, G., and Kosro, P.: Assimilation of
surface velocity data into a primitive equation coastal ocean model, J.
Geophys. Res., 107, 3122, https://doi.org/10.1029/2000JC000511, 2002.
Palmer, T., Doblas-Reyes, F., Weisheimer, A. and Rodwell, M.: Toward
seamless prediction: Calibration of climate change projections using
seasonal forecasts, B. Am. Meteorol. Soc., 89, 459–470, 2008.
Penny, S., Akella, S., Alves, O., Bishop, C., Buehner, M., Chevalier, M.,
Counillon, F., Drper, C., Frolov, S., Fujii, Y., Kumar, A., Laloyaux, P.,
Mahfouf, J.-F., MArtin, M., Pena, M., de Rosnay, P., Subramanian, A.,
Tardif, R., Wang, Y., and Wu, X.: Coupled data assimilation for integrated
Earth system analysis and prediction: Goals, Challenges and Recommendations,
Tech. Rep. WWRP 2017-3, World Meteorological Organization, 2017.
Qiao, F., Zhao, W., Yin, X., Huang, X., Liu, X., Shu, Q., Wang, G., Song,
Z., Liu, H., Yang, G., and Yuan, Y.: A highly effective global surface wave
numerical simulation with ultra-high resolution, in: Proceedings of the
International Conference for High Performance Computing, Networking, Storage
and Analysis (SC '16), IEEE Press, Piscataway, NJ, USA, https://doi.org/10.1109/SC.2016.4, 2016.
Rabier, F., Jarvinen, H., Klinker, E., Mahfouf, J., and Simmons, A.: The
ECMWF operational implementation of four-dimensional variational
assimilation. I: Experimental results with simplified physics, Q. J. Roy.
Meteor. Soc., 126, 1143–1170, 2007.
Saha, S., Moorthi, S., Pan, H., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y., Chuang, H., Juang, H. H., Sela, J., Iredell,
M. T. R., Kleist, D., van Delst, P., Keyser, D., Derber, J., Ek, M., Meng,
J., Wei, H., Yang, R., Lord, St., Van Den Dool, H., Kumar, A., Wang, W.,
Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J., Ebisuzaki, W., Lin,
R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C., Liu, Q., Chen, Y.,
Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The
NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91,
1015–1057, 2010.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M., Van Den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The
NCEP Climate Forecast System Version 2, J. Clim., 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
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.
Shao, H., Derber, J., Huang, X. Y., Hu, M., Newman, K., Stark, D., Lueken,
M., Zhou, C., Nance, L., Kuo, Y. H., and Brown, B.: Bridging Research to
Operations Transitions: Status and Plans of Community GSI, B. Am.
Meteor. Soc., 97, 1427–1440, https://doi.org/10.1175/BAMS-D-13-00245.1, 2016.
Skachko, S., Buehner, M., Laroche, S., Lapalme, E., Smith, G., Roy, F., Surcel-Colan, D., Bélanger, J.-M., and Garand, L.: Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1), Geosci. Model Dev., 12, 5097–5112, https://doi.org/10.5194/gmd-12-5097-2019, 2019.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Version 4, NCAR Tech. Note NCAR/TN-556+STR, 145 pp.,
https://doi.org/10.5065/1dfh-6p97, 2019.
Sluka, T., Penny, S., Kalnay, E., and Miyoshi, T.: Assimilating atmospheric
observations into the ocean using strongly coupled ensemble data
assimilation, Geophys. Res. Lett., 43, 752–759, 2016.
Sugiura, N., Awaji, T., Masuda, S., Mochizuki, T., Toyoda, T., Miyama, T.,
Igarashi, H., and Ishikawa, Y.: Development of a 4-dimensional variational
coupled data assimilation system for enhanced analysis and prediction of
seasonal to interannual climate variations, J. Geophys. Res., 113, C10017,
https://doi.org/10.1029/2008JC004741, 2008.
Sun, C.: ChaoSun14/DAFCC: First release of DAFCC (Version v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.3739729, 2020a.
Sun, C.: ChaoSun14/Sample_DA_system_with_DAFCC1: Sample_DA_system_with_DAFCC1 (Version v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.3774710, 2020b.
Tardif, R., Hakim, G., and Snyder, C.: Coupled atmosphere–ocean data
assimilation experiments with a low-order climate model, Clim. Dyn., 43,
1631–1643, https://doi.org/10.1007/s00382-013-1989-0, 2014.
Tardif, R., Hakim, G., and Snyder, C.: Coupled atmosphere-ocean data
assimilation experiments with a low-order model and CMIP5 model data, Clim.
Dyn., 45, 1415–1427, https://doi.org/10.1007/s00382-014-2390-3, 2015.
Valcke, S., Balaji, V., Craig, A., DeLuca, C., Dunlap, R., Ford, R. W., Jacob, R., Larson, J., O'Kuinghttons, R., Riley, G. D., and Vertenstein, M.: Coupling technologies for Earth System Modelling, Geosci. Model Dev., 5, 1589–1596, https://doi.org/10.5194/gmd-5-1589-2012, 2012.
Vidard, P. A., Le Dimet, F. X., and Piacentini, A.: Determination of optimal
nudging coefficients, Tellus, 55A, 1–15, 2003.
Wang, G., Qiao F., and Xia C.: Parallelization of a coupled wave-circulation
model and its application, Ocean Dyn., 60, 331–339,
https://doi.org/10.1007/s10236-010-0274-6, 2010.
Wang, G., Zhao, B., Qiao, F., and Zhao, C.: Rapid intensification of Super
Typhoon Haiyan: the important role of a warm-core ocean eddy, Ocean Dyn., 68,
1649–1661, 2018.
Wang, W., Barker, D., Bray J., Bruye`re C., Duda M., Dudhia J., Gill D., and
Michalakes J.: WRF Version 3 Modeling System User's Guide, available at:
http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/contents.html (last access: 15 April 2020), 2014.
Wang, X., Barker, D., Snyder, C., and Hamill, T.: A hybrid ETKF-3DVAR data
assimilation scheme for the WRF model. Part I: Observing system simulation
experiment, Mon. Weather Rev., 136, 5116–5131, 2008.
Wang, X., Parrish, D., Kleist, D., and Whitaker, J.: GSI 3DVar-based
ensemble-variational hybrid data assimilation for NCEP global forecast
system: Single-resolution experiments, Mon. Weather Rev., 141, 4098–4117,
2013.
Whitaker, J. and Hamill, T. M.: Ensemble Data Assimilation without Perturbed
Observations, Mon. Weather Rev., 130, 1913–1924, 2012.
Yang, X., Rosati, A., Zhang, S., Delworth, T., Gudgel, R., Zhang, R.,
Vecchi, G., Anderson, W., Chang, Y., DelSole, T., Dixon, K., Msadek, R.,
Stern, W., Wittenberg, A., and Zeng, F.: A predictable AMO-like pattern in
the GFDL fully coupled ensemble initialization and decadal forecasting
system, J. Clim., 26, 650–661, 2013.
Yang, Y., Qiao, F., Zhao, W., Teng, Y., and Yuan, Y.: MASNUM ocean wave
numerical model in spherical coordinates and its application, Acta Oceanol.
Sin., 27, 1–7, 2005.
Zhao, B., Qiao, F., Cavaleri, L., Wang, G., Bertotti, L., and Liu, L:
Sensitivity of typhoon modeling to surface waves and rainfall, J. Geophys.
Res.-Oceans, 122, 1702–1723, https://doi.org/10.1002/2016JC012262, 2017.
Zhang, S., Harrison, M., Wittenberg, A., Rosati, A., Anderson, J., and
Balaji, V.: Initialization of an ENSO forecast system using a parallelized
ensemble filter, Mon. Weather Rev., 133, 3176–3201, 2005.
Zhang, S., Harrison, M., Rosati, A., and Wittenberg, A.: System design and
evaluation of coupled ensemble data assimilation for global oceanic climate
studies, Mon. Weather Rev., 135, 3541–3564, https://doi.org/10.1175/MWR3466.1, 2007.
Short summary
Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
Data assimilation (DA) provides better initial states of model runs by combining observations...
