@article {Morley:2016:0025-3324:109,
title = "Detecting and Correcting Biases in Long-Term Ocean Observatory Time Series: Case Study on Current Directions Estimated From Acoustic Doppler Current Profiler Data",
journal = "Marine Technology Society Journal",
parent_itemid = "infobike://mts/mtsj",
publishercode ="mts",
year = "2016",
volume = "50",
number = "3",
publication date ="2016-05-01T00:00:00",
pages = "109-113",
itemtype = "ARTICLE",
issn = "0025-3324",
url = "https://www.ingentaconnect.com/content/mts/mtsj/2016/00000050/00000003/art00016",
doi = "doi:10.4031/MTSJ.50.3.9",
keyword = "Ocean Networks Canada, acoustic Doppler current profiler, ocean observatories, ocean currents, heading bias",
author = "Morley, Michael G. and Jeffries, Marlene A. and Mih{\’a}ly, Steven F. and Jenkyns, Reyna and Biffard, Ben R.",
abstract = "
Abstract
Ocean Networks Canada (ONC) operates the NEPTUNE and VENUS cabled ocean observatories to collect continuous data on physical, chemical, biological, and geological ocean conditions over multiyear time periods. Researchers can download real-time and historical data
from a large variety of instruments to study complex earth and ocean processes from their home laboratories. Ensuring that the users are receiving the most accurate data is a high priority at ONC, requiring QAQC (quality assurance and quality control) procedures to be developed for a variety
of data types (Abeysirigunawardena et al., 2015). Acquiring long-term time series of oceanographic data from remote locations on the seafloor presents significant challenges from a QAQC perspective. In order to identify and study important scientific events and trends, data consolidated from
multiple deployments and instruments need to be self-consistent and free of biases due to changes to instrument configurations, calibrations, metadata, biofouling, or a degradation in instrument performance. As a case study, this paper describes efforts at ONC to identify and correct systematic
biases in ocean current directions measured by ADCPs (acoustic Doppler current profilers), as well as the lessons learned to improve future data quality.",
}