ONC-BCF Gridded Sea Surface Ferry Data 2001-2025

Since 2001, several passenger ferries connecting Vancouver Island and mainland British Columbia, Canada have collected sea surface hydrographic data within the Salish Sea. Originally part of two separate monitoring projects in the early 2000s and now maintained by Ocean Networks Canada (ONC) through cooperation with British Columbia (BC) Ferries, archived and real-time data from these vessels are made open to the public (https://data.oceannetworks.ca/). To date, several vessels with scientific underway systems have collected data between Tsawwassen-Nanaimo (TWS-NNO, ~70km) and Tsawwassen-Swartz Bay (TWS-SWB, ~44km), sometimes up to 8 times per day. This repository consists of data collected along these two routes that has been temporally grid by transit start time and spatially binned to 0.01 degrees. TWS-SWB_2001-2025.nc contains temperature, conductivity, salinity, density, dissolved oxygen, and oxygen saturation data collected during transits between Tsawwassen and Swartz Bay from 2001-2025 (33,672 total transits). TWS-NNO_2003-2025.nc contains temperature, intake temperature, conductivity, salinity, density, dissolved oxygen, and oxygen saturation data collected during transits between Tsawwassen and Swartz Bay from 2003-2025 (31,620 total transits). Each file is made up of two groups. The unspecified root group contains the gridded science data. The 'statistics' group contains statistical (e.g. standard error) and supporting information for each grid cell. onc_data_citations.txt contains the DOIs for the original data used to create this curated dataset. file_attributes.yaml and variable_attributes.yaml contains attribute and metadata information used to build the curated datasets. download_and_grid_bcf.ipynb contains an example Python 3 notebook for downloading and gridding recent BC Ferry data. TWDP_TWSB_annotations.json contains a list of annotations related to the TWDP (TWS-NNO) and TWSB (TWS-SWB) locationCodes native to Oceans 3.0. Users can use this to further identify maintenance cleaning cycles and periods that have been flagged by data reviewers as poor quality.