Abstract
Tidal characteristics in the well-known Coastal Marine Proving Ground near Chudao Island located in Shandong Province, China, are firstly investigated based on Princeton Ocean Model (POM) with a generalized coordinate system. Numerical results having been validated by available observations, the ensemble transform–based sensitivity method that calculates the gradient of forecast error variance reduction is used to identify sensitive areas of the water level and the current velocity in the Marine Proving Ground and its vicinity. Sensitive areas of the water level are mainly distributed around Chudao Island, the spatial range of which distributes smaller than that of the current velocity. When sensitivities of the water level and the current velocity are considered together, the coincidence areas serve as the most appropriate areas for adaptively deploying observation instruments. We found that a particular area west of Chudao Island is the most appropriate area for the hydrological observations in the Marine Proving Ground, which provide an insight into rational targeted observation analysis in tide-dominated shallow water areas.
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References
Bishop CH, Toth Z (1999) Ensemble transformation and adaptive observations. J Atmos Sci 56(11):1748–1765. https://doi.org/10.1175/1520-0469(1999)056<1748:ETAAO>2.0.CO;2
Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon Weather Rev 129(3):420–436
Blumberg AF, Mellor GL (1987) A description of a three-dimensional coastal ocean circulation model. Three-dimensional coast. Ocean Model Coast Estuar Sci 4:1–16
Buizza R, Montani A (1999) Targeting observations using singular vectors. J Atmos Sci 56(17):2965–2985. https://doi.org/10.1175/1520-0469(1999)056<2965:TOUSV>2.0.CO;2
Cao AZ, Wang DS, Lv XQ (2015) Harmonic analysis in the simulation of multiple constituents: determination of the optimum length of time series. J Atmos Ocean Technol 32(5):1112–1118. https://doi.org/10.1175/JTECH-D-14-00148.1
Chang EKM, Zheng M, Raeder K (2013) Medium-range ensemble sensitivity analysis of two extreme pacific extratropical cyclones. Mon Weather Rev 141(1):211–231. https://doi.org/10.1175/MWR-D-11-00304.1
Chavez FP, Brewer PG, Scholin CA (2017) Celebrating 30 years of ocean science and technology at the Monterey Bay Aquarium Research Institute. Oceanography 30(4):18–25. https://doi.org/10.5670/oceanog.2017.420
Data Announcement 88-MGG-02, Digital relief of the surface of the earth. NOAA, National Geophysical Data Center, Boulder, Colorado, 1988
Duan W, Hu J (2016) The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model. Clim Dyn 46(11–12):3599–3615. https://doi.org/10.1007/s00382-015-2789-5
Duan W, Wu Y (2014) Season-dependent predictability and error growth dynamics of Pacific Decadal Oscillation-related sea surface temperature anomalies. Clim Dyn 44(3–4):1053–1072. https://doi.org/10.1007/s00382-014-2364-5
Duan W, Li X, Tian B (2018) Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño. Clim Dyn 51(9–10):3351–3368. https://doi.org/10.1007/s00382-018-4082-x
Ezer T, Mellor GL (2004) A generalized coordinate ocean model and a comparison of the bottom boundary layer dynamics in terrain-following and in z-level grids. Ocean Model 6(3–4):379–403. https://doi.org/10.1016/S1463-5003(03)00026-X
Feng R, Duan W, Mu M (2017) Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions. Clim Dyn 48(3–4):1173–1185. https://doi.org/10.1007/s00382-016-3134-3
Hamill TM, Synder C (2002) Using improved background-error covariances from an ensemble Kalman filter for adaptive observations. Mon Weather Rev 130(6):1552–1572. https://doi.org/10.1175/1520-0493(2002)130<1552:UIBECF>2.0.CO;2
Han G, Li W, He Z, Liu K, Ma J (2006) Assimilated tidal results of tide gauge and TOPEX/POSEIDON data over the China seas using a variational adjoint approach with a nonlinear numerical model. Adv Atmos Sci 23:449–460
Han G, Li W, Zhang X, Li D, He Z, Wang X, Wu X, Yu T, Ma J (2011) A regional ocean reanalysis system for coastal waters of China and adjacent seas. Adv Atmos Sci 28(3):682–690. https://doi.org/10.1007/s00376-010-9184-2
Huang C, Wu M, Sun W, Bian G, He J, Deng K, Zhai G (2019) Improving the definition and algorithms of China’s coastline considering the diversity of tidal characteristics. Mar Geod 42(4):382–405. https://doi.org/10.1080/01490419.2019.1610816
Ito K, Wu CC (2013) Typhoon-position-oriented sensitivity analysis. part I: Theory and verification. J Atmos Sci 70(8):2525–2546. https://doi.org/10.1175/JAS-D-12-0301.1
Köhl A (2005) Anomalies of meridional overturning: mechanisms in the North Atlantic. J Phys Oceanogr 35(8):1455–1472. https://doi.org/10.1175/JPO2767.1
Köhl A, Stammer D (2004) Optimal observations for variational data assimilation. J Phys Oceanogr 34(3):529–542. https://doi.org/10.1175/2513.1
Langland RH, Rohaly GD (1996) Adjoint-based targeting of observations for FASTEX cyclones. Naval Research Lab Monterey Ca. 9–11
Liu D, Zhu J, Shu Y, Wang D, Wang W, Yan C, Zhou W (2018) Targeted observation analysis of a Northwestern Tropical Pacific Ocean mooring array using an ensemble-based method. Ocean Dyn 68(9):1109–1119. https://doi.org/10.1007/s10236-018-1188-y
Lorenz EN, Emanuel KA (1998) Optimal sites for supplementary weather observations: simulation with a small model. J Atmos Sci 55(3):399–414. https://doi.org/10.1175/1520-0469(1998)055<0399:OSFSWO>2.0.CO;2
Majumdar SJ (2016) A review of targeted observations. Bull Am Meteorol Soc 97(12):2287–2303. https://doi.org/10.1175/BAMS-D-14-00259.1
Majumdar SJ, Bishop CH, Etherton BJ, Szunyogh I, Toth Z (2001) Can an ensemble transform Kalman filter predict the reduction in forecast-error variance produced by targeted observations? Q J R Meteorol Soc 127(578):2803–2820. https://doi.org/10.1002/qj.49712757815
Majumdar SJ, Bishop CH, Etherton BJ, Toth Z (2002) Adaptive sampling with the ensemble transform Kalman filter. Part II: Field program implementation. Mon Weather Rev 130(5):1356–1369
Majumdar SJ, Bishop C, Caughey J, Doerenbecher A (2011) Targeted observations for improving numerical weather prediction: an overview WWRP/THORPEX No 15
Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys 20(4):851–875. https://doi.org/10.1029/RG020i004p00851
Mellor GL, Häkkinen SM, Ezer T, Patchen RC (2002) A generalization of a sigma coordinate ocean model and an intercomparison of model vertical grids. Ocean Forecast:55–72. https://doi.org/10.1007/978-3-662-22648-3_4
Mu M (2013) Methods, current status, and prospect of targeted observation. Sci China Earth Sci 56(12):1997–2005. https://doi.org/10.1007/s11430-013-4727-x
Mu M, Duan WS, Wang B (2003) Conditional nonlinear optimal perturbation and its applications. Nonlinear Process Geophys 10(6):493–501. https://doi.org/10.5194/npg-10-493-2003
Mu M, Zhou F, Wang H (2009) Method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: conditional nonlinear optimal perturbation. Mon Weather Rev 137(5):1623–1639. https://doi.org/10.1175/2008MWR2640.1
Mu M, Yu Y, Xu H, Gong T (2014) Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions. Theor Appl Climatol 115(3):461–469. https://doi.org/10.1007/s00704-013-0909-x
Mu M, Duan W, Chen D, Yu W (2015) Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting. Natl Sci Rev 2(2):226–236. https://doi.org/10.1093/nsr/nwv021
Palmer TN, Gelaro R, Barkmeijer J, Buizza R (1998) Singular vectors, metrics, and adaptive observations. J Atmos Sci 55(4):633–653. https://doi.org/10.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2
Pirooznia M, Rouhollah Emadi S, Najafi Alamdari M (2016) Caspian sea tidal modelling using coastal tide gauge data. J Geol Res 2016(1):1–10. https://doi.org/10.1155/2016/6416917
Pu ZX, Kalnay E, Sela J, Szunyogh I (1997) Sensitivity of forecast errors to initial conditions with a quasi-inverse linear method. Mon Weather Rev 125(10):2479–2503. https://doi.org/10.1175/1520-0493(1997)125<2479:SOFETI>2.0.CO;2
Qin X, Duan W, Mu M (2013) Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations. Q J R Meteorol Soc 139(675):1544–1554. https://doi.org/10.1002/qj.2109
Snyder C (1996) Summary of an informal workshop on adaptive observations and FASTEX. Bull Am Meteorol Soc 77(SUPPL. 5):953–961. https://doi.org/10.1177/1120672107017005s07
Wang Q, Mu M, Dijkstra HA (2013) The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. J Geophys Res Oceans 118(2):869–884. https://doi.org/10.1002/jgrc.20084
Wu CC, Chen JH, Lin PH, Chou KH (2007) Targeted observations of tropical cyclone movement based on the adjoint-derived sensitivity steering vector. J Atmos Sci 64(7):2611–2626. https://doi.org/10.1175/JAS3974.1
Wu CC, Chen SG, Chen JH, Chou KH, Lin PH (2009) Interaction of typhoon Shanshan (2006) with the midlatitude trough from both adjoint-derived sensitivity steering vector and potential vorticity perspectives. Mon Weather Rev 137(3):852–862. https://doi.org/10.1175/2008MWR2585.1
Xie B, Zhang F, Zhang Q, Poterjoy J, Weng Y (2013) Observing strategy and observation targeting for tropical cyclones using ensemble-based sensitivity analysis and data assimilation. Mon Weather Rev 141(5):1437–1453. https://doi.org/10.1175/MWR-D-12-00188.1
Yanagi T, Inoue K (1995) A numerical experiment on the sedimentation processes in the Yellow Sea and the East China Sea. J Oceanogr 51(5):537–552. https://doi.org/10.1007/BF02270523
Zhang X, Han G, Wang D, Deng Z, Li W (2012) Summer surface layer thermal response to surface gravity waves in the Yellow Sea. Ocean Dyn 62(7):983–1000. https://doi.org/10.1007/s10236-012-0547-3
Zhang Y, Xie Y, Wang H, Chen D, Toth Z (2016) Ensemble transform sensitivity method for adaptive observations. Adv Atmos Sci 33(1):10–20. https://doi.org/10.1007/s00376-015-5031-9
Zhang K, Mu M, Wang Q (2017) Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model. Sci China Earth Sci 60(5):866–875. https://doi.org/10.1007/s11430-016-9020-8
Zhang X, Chu PC, Li W, Liu C, Zhang L, Shao C, Zhang X, Chao G, Zhao Y (2018) Impact of Langmuir turbulence on the thermal response of the ocean surface mixed layer to Supertyphoon Haitang (2005). J Phys Oceanogr 48(8):1651–1674. https://doi.org/10.1175/JPO-D-17-0132.1
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Zhang, J., Zhang, A., Zhang, X. et al. Targeted observation analysis of the tides and currents in a Coastal Marine Proving Ground. Ocean Dynamics 70, 1303–1313 (2020). https://doi.org/10.1007/s10236-020-01398-w
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DOI: https://doi.org/10.1007/s10236-020-01398-w