Gap-filled, gridded subsurface physical oceanography time series dataset derived from selected mooring measurements off the Western Australia coast during 2009-2023

This collection presents a gap-filled, gridded time series dataset of daily ocean temperature and current, collected from an array of 6 coastal Integrated Marine Observing System (IMOS) moorings off the southwest coast of Western Australia (WA) during 2009-2023, at depths ranging from 47 m to 500 m. Self-Organizing Map (SOM) is used to fill the data gaps.\n\nThe collection also provides a daily gridded mooring dataset of temperature, salinity, and current without gap-filling. Monthly average data are also included. Monthly data were then derived from daily data if there were more than 10 days of data during that month.\n\nThis integrated dataset provides an overview of data availability and allows users to have quick access to the mooring data, without the need of manipulating over one thousand files individually. This unique dataset offers an invaluable baseline perspective on water column properties and temporal variability in WA coastal waters. The data can be used to characterise subsurface features of extreme events such as marine heatwaves, marine cold-spells, and to detect long-term change signals along the WA coast, influenced by the Leeuwin Current and the wind-driven Capes Current.\n\nLineage: This collection includes two data products: the unfilled gridding data and the in-filled gridding data.\nFor the first product, initially raw data (FV00) were processed using IMOS Matlab Toolbox, then Quality Assurance (QA) and Quality Control (QC) of the data were performed using the Toolbox and assessed by oceanographers (https://doi.org/10.25919/9gb1-ne81). After that, quality-controlled data (FV01) were concatenated, and then (linearly) interpolated to a grid of 1m vertical resolution and averaged daily. Monthly data were then derived from daily data if there were more than 10 days of data during that month.\nFor the second product, based on the unfilled data, we firstly had extrapolated temperature and current vertical profiles, and then selected these profiles for training Self-Organizing Map (SOM), thereby improving the accuracy of the input data's topological structure. Daily data vectors containing missing values were mapped onto SOM grids using the best matching unit determined by a similarity function, and the missing data points were filled by replacing them with the corresponding SOM unit. \n

本数据集包含一套经间隙填补的网格化逐日海洋温度与海流时间序列数据，数据采集自2009-2023年间，部署于西澳大利亚西南沿海的6套综合海洋观测系统（Integrated Marine Observing System, IMOS）锚系浮标阵列，观测深度范围为47米至500米。本数据集采用自组织映射（Self-Organizing Map, SOM）方法填补数据缺失值。

本数据集同时提供未进行间隙填补的温度、盐度与海流逐日网格化锚系数据集，并附带月均数据产品。月均数据由当月有效逐日数据量超过10天的逐日数据计算生成。

该集成数据集可直观展示数据可用性概况，使用户无需手动处理上千个独立文件即可快速获取锚系观测数据。本独特数据集为西澳大利亚沿海水柱特性与时间变化特征提供了宝贵的基准视角。其数据可用于刻画海洋热浪、海洋冷害等极端事件的次表层特征，同时可用于检测受吕温洋流（Leeuwin Current）与风驱开普海流（Capes Current）影响的西澳大利亚沿岸长期变化信号。

数据溯源：本数据集包含两类数据产品：未填补间隙的网格化数据与已完成间隙填补的网格化数据。

针对第一类产品，首先使用IMOS Matlab工具箱处理原始数据（FV00）；随后通过该工具箱完成数据质量保证（Quality Assurance, QA）与质量控制（Quality Control, QC），并由海洋学家进行评估（https://doi.org/10.25919/9gb1-ne81）。之后将经过质量控制的数据（FV01）进行拼接，再以1米垂直分辨率进行（线性）插值，并逐日平均。随后按照前述规则，由当月有效逐日数据量超过10天的月度逐日数据生成月均数据。

针对第二类产品，以未填补间隙的数据为基础，首先对温度与海流的垂直剖面进行外推，随后选取这些剖面用于训练自组织映射（Self-Organizing Map, SOM），以提升输入数据拓扑结构的准确性。将包含缺失值的逐日数据向量通过相似度函数确定的最佳匹配单元映射至SOM网格，并将缺失数据点替换为对应SOM单元的数据以完成填补。