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首页> 外文期刊>Ocean Dynamics >Objective assessment of the contribution of the RECOPESCA network to the monitoring of 3D coastal ocean variables in the Bay of Biscay and the English Channel
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Objective assessment of the contribution of the RECOPESCA network to the monitoring of 3D coastal ocean variables in the Bay of Biscay and the English Channel

机译：客观评估RECOPESCA网络对比斯开湾和英吉利海峡3D沿海海洋变量的监测作用

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摘要

In the Bay of Biscay and the English Channel, in situ observations represent a key element to monitor and to understand the wide range of processes in the coastal ocean and their direct impacts on human activities. An efficient way to measure the hydrological content of the water column over the main part of the continental shelf is to consider ships of opportunity as the surface to cover is wide and could be far from the coast. In the French observation strategy, the RECOPESCA programme, as a component of the High frequency Observation network for the environment in coastal SEAs (HOSEA), aims to collect environmental observations from sensors attached to fishing nets. In the present study, we assess that network using the Array Modes (ArM) method (a stochastic implementation of Le H,naff et al. Ocean Dyn 59: 3-20. doi: 10.1007/s10236-008-0144-7, 2009). That model ensemble-based method is used here to compare model and observation errors and to quantitatively evaluate the performance of the observation network at detecting prior (model) uncertainties, based on hypotheses on error sources. A reference network, based on fishing vessel observations in 2008, is assessed using that method. Considering the various seasons, we show the efficiency of the network at detecting the main model uncertainties. Moreover, three scenarios, based on the reference network, a denser network in 2010 and a fictive network aggregated from a pluri-annual collection of profiles, are also analysed. Our sensitivity study shows the importance of the profile positions with respect to the sheer number of profiles for ensuring the ability of the network to describe the main error modes. More generally, we demonstrate the capacity of this method, with a low computational cost, to assess and to design new in situ observation networks.
机译：在比斯开湾和英吉利海峡，就地观测是监测和了解沿海海洋各种过程及其对人类活动的直接影响的关键因素。一种测量大陆架主要部分水柱水文含量的有效方法是考虑有机会的船，因为其覆盖面很宽且可能远离海岸。在法国的观测策略中，RECOPESCA计划是沿海SEA（HOSEA）环境高频观测网络的组成部分，旨在从连接到渔网的传感器收集环境观测数据。在本研究中，我们使用阵列模式（ArM）方法评估该网络（Le H，naff等人的随机实现方法。OceanDyn 59：3-20。doi：10.1007 / s10236-008-0144-7，2009 ）。在此基于模型集成的方法用于基于误差源的假设，比较模型误差和观测误差，并定量评估观测网络在检测先验（模型）不确定性方面的性能。使用该方法评估了一个参考网络，该网络基于2008年渔船的观测结果。考虑到各个季节，我们显示了网络在检测主要模型不确定性方面的效率。此外，还分析了三种情况，分别基于参考网络，2010年的密集网络和虚构的网络，这些网络是从每年一次的概要文件集合中汇总而来的。我们的敏感性研究表明，配置文件位置相对于配置文件的绝对数量非常重要，以确保网络能够描述主要错误模式。更笼统地说，我们证明了这种方法的能力，以较低的计算成本，可以评估和设计新的原位观测网络。

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