Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas

WANG Guansuo; ZHAO Chang; XU Jiangling; QIAO Fangli; XIA Changshui

doi:10.1007/s13131-016-0810-4

Volume 35 Issue 2

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Article Navigation > Acta Oceanologica Sinica > 2016 > 35(2): 19-28

WANG Guansuo, ZHAO Chang, XU Jiangling, QIAO Fangli, XIA Changshui. Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas[J]. Acta Oceanologica Sinica, 2016, 35(2): 19-28. doi: 10.1007/s13131-016-0810-4

Citation:

WANG Guansuo, ZHAO Chang, XU Jiangling, QIAO Fangli, XIA Changshui. Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas[J]. Acta Oceanologica Sinica, 2016, 35(2): 19-28. doi: 10.1007/s13131-016-0810-4

Citation:

WANG Guansuo, ZHAO Chang, XU Jiangling, QIAO Fangli, XIA Changshui. Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas[J]. Acta Oceanologica Sinica, 2016, 35(2): 19-28. doi: 10.1007/s13131-016-0810-4

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Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas

doi: 10.1007/s13131-016-0810-4

1.
College of Oceanic and Atmospheric Science, Ocean University of China, Qingdao 266003, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;Key Laboratory of Marine Science and Numerical Modeling (MASNUM), State Oceanic Administration, Qingdao 266061, China
2.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;Key Laboratory of Marine Science and Numerical Modeling (MASNUM), State Oceanic Administration, Qingdao 266061, China
3.
North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266033, China

Received Date: 2015-07-20
Rev Recd Date: 2015-09-21

Abstract

Abstract

An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas (OCFS-C) is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China's seas has been updated to (1/24)° from the global model with (1/2)° resolution. Besides, daily remote sensing sea surface temperature (SST) data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth (MLD) and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores (SS) is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value (more than 0.90). Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.
- operational forecast,
- sea surface temperature,
- mixed layer depth,
- lead time,
- subsurface temperature,
- ocean circulation-surface wave coupled forecast system,
- China's seas

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References

Bathen K H. 1972. On the seasonal changes in the depth of the mixed layer in the North Pacific Ocean. Journal of Geophysical Re-search, 77: 7138-7150

Belkin I M, Filyushkin B N. 1986. Seasonal Variability of thermal structure of oceanic upper layer in the POLYMODE area. Okeanologiya, 26(2): 204-211

Brassington G B, Pugh T, Spillman C, et al. 2007. BLUElink> develop-ment of operational oceanography and servicing in Australia. Journal of Research and Practice in Information Technology, 39(2): 151-164

de Boyer Montégut C, Madec G, Fischer A S, et al. 2004. Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. Journal of Geophysical Research, 109: C12003, doi: 10.1029/2004JC002378

Egbert G D, Bennett A F, Foreman M G G. 1994. TOPEX/Poseidon tides estimated using a global inverse model. Journal of Geo-physical Research, 99(C12): 24821-24852

Gentemann C L. 2007. Global 9 km multi-satellite, multi-sensor sea surface temperatures from MODIS, AMSR-E, and TMI. In: American Geophysical Union, Spring Meeting 2007, abstract #OS34A-05

Holte J, Talley L. 2009. A new algorithm for finding mixed layer depths with applications to Argo data and subantarctic mode water formation. Journal of Atmospheric and Oceanic Techno-logy, 26: 1920-1939

Hurlburt H E, Brassington G B, Yann D, et al. 2009. High-resolution global and basin-scale ocean analyses and forecasts. Oceano-graphy, 22(3): doi: 10.5670/oceanog.2009.70

Lin Xiaopei, Xie Shangping, Chen Xinping, et al. 2006. A well-mixed warm water column in the central Bohai Sea in summer: Ef-fects of tidal and surface wave mixing. Journal of Geophysical Research, 111(C11): doi: 10.1029/2006JC003504

Lorbacher K, Dommenget D, Niiler P P, et al. 2006. Ocean mixed lay-er depth: A subsurface proxy of ocean-atmosphere variability. Journal of Geophysical Research, 111: C07010, doi: 10.1029.2003JC002157

Lü Xinggang, Qiao Fangli, Xia Changshui, et al. 2006. Upwelling off Yangtze River estuary in summer. Journal of Geophysical Re-search, 111(C11), doi: 10.1029/2005JC003250

Matsuno T, Lee J S, Shimizu M, et al. 2006. Measurements of the tur-bulent energy dissipation rate . and an evaluation of the disper-sion process of the Changjiang Diluted Water in the East China Sea. Journal of Geophysical Research, 111(C11): doi: 10.1029/2005JC003196

Monterey G I, deWitt L M. 2000. Seasonal variability of global mixed layer depth from WOD98 temperature and salinity profiles. NOAA-TM-NMFS-SWFSC-296, U.S. Dep. Of Commerce, Na-tional Marine Fisheries Service, Pacific Grove, CA 93950

Murphy A H. 1988. Skill Scores based on the mean square error and their relationships to the correlation coefficient. Monthly Weather Review, 116(12): 2417-2424

Murphy A H. 1993. What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather and Forecasting, 8(2): 281-293

Qiao Fangli, Ma Jian, Yang Yongzeng, et al. 2004a. Simulation of the temperature and salinity along 36°N in the Yellow Sea with a wave-current coupled model. Journal of the Korean Society of Oceanography, 39: 35-45

Qiao Fangli, Wang Guansuo, Lü Xingang, et al. 2011a. Drift character-istics of green macroalgae in the Yellow Sea in 2008 and 2010. Chinese Science Bulletin, 56(21): 2236-2242

Qiao Fangli, Wang Guansuo, Zhao Wei, et al. 2011b. Predicting the spread of nuclear radiation from the damaged Fukushima Nuc-lear Power Plant. Chinese Science Bulletin, 56(18): 1890-1896

Qiao Fangli, Xia Changshui, Shi Jianwei, et al. 2004b. Seasonal variab-ility of thermocline in the Yellow Sea. Chinese Journal of Oceanology and Limnology, 22(3): 299-305

Sun Yujuan, Qiao Fangli, Wang Guansuo, et al. 2009. Forecast Opera-tion and verification of MASNUM Surface Wave Numerical Model. Advances in Marine Science (in Chinese), 27(3): 281-294

Usui N, Ishizaki S, Fujii Y, et al. 2006. Meteorological Research Insti-tute multivariate ocean variational estimaton (MOVE) system: Some early results. Advances in Space Research, 37(4): 806-822

Wang Guansuo, Qiao Fangli, Yang Yongzeng. 2007. Study on parallel algorithm for MPI-based LAGFD-WAM numerical wave model. Advances in Marine Science (in Chinese), 25(4): 401-407

Wang Guansuo, Qiao Fangli. 2008. Ocean temperature responses to Typhoon Mstsa in the East China Sea. Acta Oceanologica Sin-ica, 27(4): 26-38

Wang Guansuo, Qiao Fangli, Xia Changshui. 2010. Parallelization of a coupled wave-circulation model and its application. Ocean Dy-namics, 60(2): 331-339

Xia Changshui, Chen Xianyao, Qiao Fangli, et al. 2003. C grid nesting technique and its application in simulating the ocean wave propagation. Advances in Marine Science (in Chinese), 21(4): 401-406

Xia Changshui, Qiao Fangli, Yang Yongzeng, et al. 2006. Three-di-mensional structure of the summer circulation in the Yellow Sea from a wave-tide circulation coupled model. Journal of Geophysical Research, 111(C11): doi: 10.1029/2005JC003218

Xia Changshui, Qiao Fangli, Zhang Qinghua, et al. 2004a. Numerical modelling of the quasi-global ocean circulation based on POM. Journal of Hydrodynamics, Ser B, 16(5): 537-543

Xia Changshui, Qiao Fangli, Zhang Mengning, et al. 2004b. Simula-tion of double cold cores of the 35°N section in the Yellow Sea with a wave-tide-circulation coupled model. Chinese Journal of Oceanology and Limnology, 22(3): 292-298

Yang Yongzeng, Qiao Fangli, Zhao Wei, et al. 2005. MASNUM ocean wave numerical model in spherical coordinates and its applica-tion. Acta Oceanologica Sinica, 22(2): 1-7

Yuan Yeli, Hua Feng, Pan Zengdi, et al. 1991. LAGDF-WAM numeric-al wave model-I. Basic physical model. Acta Oceanologica Sin-ica, 10(4): 483-488

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