Targeted observation analysis of the tides and currents in a Coastal Marine Proving Ground
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Targeted observation analysis of the tides and currents in a Coastal Marine Proving Ground Jiali Zhang 1 & Anmin Zhang 1 & Xuefeng Zhang 1 & Liang Zhang 1,2 & Dong Li 3 & Zheqi Shen 4 & Chaohui Sun 4 Received: 6 March 2020 / Accepted: 3 August 2020 / Published online: 10 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Targeted observation . Ensemble transform–based sensitivity . Marine Proving Ground . Tide
1 Introduction Targeted observation is an observation strategy by which the concerned phenomenon is observed (Mu 2013). Targeted observation improves the accuracy of predictions in a validation area by taking additional observations in sensitive areas, which have a greater impact on the validation area predictions (Snyder 1996). In many numerical forecast events, the initial errors of sensitive areas grow rapidly and present different types (Duan and Hu 2016; Duan and Wu 2014). Numerical
forecast performances are sensitive to the accuracy of initial conditions (ICs) (Majumdar 2016; Mu et al. 2015), and highprecision ICs may provide better forecast skills. Using a data assimilation system in these sensitive areas will improve the ICs for numerical prediction to obtain more accurate predictions. However, assimilating observation data in other regions will not improve the accuracy of ICs significantly. Several field campaigns have shown that observations sampled in dynamically sensitive areas have positive impacts on numerical weather prediction (Majumdar et al. 2001; Majumdar et al.
Responsible Editor: Guoping Gao * Xuefeng Zhang [email protected] Jiali Zhang [email protected] Anmin Zhang [email protected] Liang Zhang [email protected]
Chaohui Sun [email protected] 1
School of Marine Science and Technology, Tianjin University, Tianjin, China
2
Texas A&M University Visiting Program, College Station, TX, USA
3
Key Laboratory of
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