Within the Gulf of Mexico (GOM), measurements of ocean pH are limited across space and time. This has hindered our ability to robustly monitor and study regional carbon dynamics, inclusive of ocean acidification, over this biogeochemically variable sea. The 2021 launch of Biogeochemical-Argo (BGC-Argo) ocean profiling floats that carry five sensors represented the entry of this particular ocean observing technology into this region. The GOM BGC-Argo floats have vastly increased the number of oxygen, nitrate, pH, chlorophyll-a fluorescence, and particulate backscattering profile observations within the “open GOM” region (>1,000 m water column depth). To circumvent a set of uncertainties associated with the collected sensor pH data, regionally trained neural network algorithms were developed to skillfully predict GOM pH (total scale, in situ temperature and pressure), which served as a secondary QC and sensor performance assessment tool. The GOM neural network pH (GOM-NNpH) algorithms were trained using a selection of climate quality CTD and bottle data (temperature, salinity, oxygen, nitrate, pressure, and location) collected as a part of NOAA’s Gulf of Mexico Ecosystems and Carbon Cruises (GOMECC). Neural network pH estimates were generated using the newly developed GOMNNpH algorithm and two widely used, globally trained neural network algorithms (Empirical Seawater Property Estimation Routines (ESPER) and CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network (CANYON-B)) to compare algorithm performance against validation data. The results demonstrate the advanced skill of the GOM-NNpH in capturing water column variability and robustly reconstructing GOM pH profiles. Using a combination of concurrent float-measured seawater values of pressure, temperature, salinity, and oxygen, a GOM-NNpH algorithm was applied to two years of BGC-Argo float data. Resulting data were used to diagnose the performance of float pH sensors and to generate a time series of neural network estimated pH based on the collected float profiles. These algorithms emphasize the value of regionally-trained neural networks and their utility by the BGC-Argo community. Further, the GOM-NNpH algorithms can also be applied by a variety of users to estimate pH values in the open GOM in the absence of direct pH observations.

中文翻译：

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使用开阔墨西哥湾的生物地球化学 Argo 浮标量化海水 pH 值的神经网络算法


在墨西哥湾 （GOM） 内，海洋 pH 值的测量在空间和时间上受到限制。这阻碍了我们在这个生物地球化学变化的海洋上稳健地监测和研究区域碳动力学的能力，包括海洋酸化。2021 年推出的生物地球化学-Argo （BGC-Argo） 海洋剖面浮标，该浮标携带五个传感器，标志着这种特殊的海洋观测技术进入该地区。GOM BGC-Argo 浮标大大增加了“开放 GOM”区域（x3E1,000 m 水柱深度）内氧气、硝酸盐、pH 值、叶绿素-a 荧光和颗粒背散射剖面观测的数量。为了规避与收集的传感器 pH 数据相关的一系列不确定性，开发了区域训练的神经网络算法来巧妙地预测 GOM pH 值（总量程、原位温度和压力），作为辅助 QC 和传感器性能评估工具。GOM 神经网络 pH 值 （GOM-NNpH） 算法使用作为 NOAA 墨西哥湾生态系统和碳游轮 （GOMECC） 的一部分收集的一系列气候质量 CTD 和瓶子数据（温度、盐度、氧气、硝酸盐、压力和位置）进行训练。使用新开发的 GOMNNpH 算法和两种广泛使用的、经过全球训练的神经网络算法（经验海水特性估计例程 （ESPER） 和 CArbonate 系统以及来自细胞学特性和氧气的营养浓度使用神经网络 （CANYON-B） 生成神经网络 pH 值估计值），以将算法性能与验证数据进行比较。结果表明，GOM-NNpH 在捕获水柱变化和稳健重建 GOM pH 值曲线方面的先进技术。 通过结合同时测量的浮子测得的压力、温度、盐度和氧气值，将 GOM-NNpH 算法应用于两年的 BGC-Argo 浮子数据。所得数据用于诊断浮子 pH 传感器的性能，并根据收集的浮子曲线生成神经网络估计 pH 值的时间序列。这些算法强调了区域训练神经网络的价值及其在 BGC-Argo 社区中的实用性。此外，GOM-NNpH 算法也可以被各种用户应用于在没有直接 pH 值观测的情况下估计开放式 GOM 中的 pH 值。