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A 3D, cross-scale, baroclinic model with implicit vertical transport for the Upper Chesapeake Bay and its tributaries

机译:切萨皮克湾及其支流上游具有隐式垂直传输的3D跨尺度斜斜模型

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We develop a new vertically implicit transport solver, based on two total variation diminishing (TVD) limiters in space and time, inside a 3D unstructured-grid model (SCHISM), and apply it to the Upper Chesapeake Bay (UCB), which has complex geometry and sharp pycnocline. We show that the model is able to accurately and efficiently capture the elevation, velocity, salinity and temperature in both the deep and shallow regions of UCB. Compared with all available CTD casts, the overall model skills have the mean absolute error of 1.08 PSU and 0.85 degrees C, and correlation coefficient of 0.97 and 0.99 for salinity and temperature respectively. More importantly, the new implicit solver better captures the density stratification, which has great implications on biogeochemistry in this estuarine system. The cross-scale capability of the model is demonstrated by extending the high-resolution grids into a tributary (Chester River) and its sub-tributary (Corsica River), with minimal impact on the model efficiency. The model is also able to capture complex 3D structures at the transition zone between the main bay and the tributary, including the three-layered circulation in Baltimore Harbor. As more and more attention is being paid to the productive shallows in the Chesapeake Bay and other estuaries, the model can serve as a very powerful management tool to understand the impact of both local and remote forcing functions. (C) 2016 Elsevier Ltd. All rights reserved.
机译:我们在3D非结构化网格模型(SCHISM)内,基于空间和时间上的两个总变化减小(TVD)限制器,开发了一种新的垂直隐式运输求解器,并将其应用于复杂的上切萨皮克湾(UCB)几何形状和锋利的比索可林。我们表明,该模型能够准确有效地捕获UCB的深部和浅部区域的海拔,速度,盐度和温度。与所有可用的CTD模型相比,总体模型技能的平均绝对误差为1.08 PSU和0.85摄氏度,盐度和温度的相关系数分别为0.97和0.99。更重要的是,新的隐式求解器可以更好地捕获密度分层,这对于该河口系统中的生物地球化学具有重要意义。通过将高分辨率网格扩展到支流(切斯特河)及其子支流(科西嘉河)中,可以证明模型的跨尺度能力,而对模型效率的影响却很小。该模型还能够捕获主海湾和支流之间过渡区域的复杂3D结构,包括巴尔的摩港的三层环流。随着人们越来越关注切萨皮克湾和其他河口的浅滩,该模型可以作为一种非常强大的管理工具来了解本地和远程强迫功能的影响。 (C)2016 Elsevier Ltd.保留所有权利。

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