Quality Control (QC) procedures for Australia's National Reference Station's sensor data: Comparing semi-autonomous systems to an expert oceanographer
Morello, EB and Galibert, GF and Smith, D and Ridgway, KR and Howell, B and Slawinski, D and Timms, GP and Evans, K and Lynch, TP, Quality Control (QC) procedures for Australia's National Reference Station's sensor data: Comparing semi-autonomous systems to an expert oceanographer, Methods in Oceanography, 9 pp. 17-33. ISSN 2211-1220 (2014) [Refereed Article]
The National Reference Station (NRS) network, part of Australia's Integrated Marine Observing System (IMOS), is designed to provide the baseline multi-decadal time series required to understand how large-scale, long-term change and variability in the global ocean are affecting Australia's coastal ocean ecosystems. High temporal resolution observations of oceanographic variables are taken continuously across the network's nine moored stations using a Water Quality Monitor (WQM) multi-sensor. The data collected are made freely available and thus need to be assessed to ensure their consistency and fitness-for-use prior to release. Here, we describe a hybrid quality control system comprising a series of tests to provide QC flags for these data and an experimental 'fuzzy logic' approach to assessing data. This approach extends the qualitative pass/fail approach of the QC flags to a quantitative system that provides estimates of uncertainty around each data point. We compared the results obtained from running these two assessment schemes on a common dataset to those produced by an independent manual QC undertaken by an expert oceanographer. The qualitative flag and quantitative fuzzy logic QC assessments were shown to be highly correlated and capable of flagging samples that were clearly erroneous. In general, however, the quality assessments of the two QC schemes did not accurately match those of the oceanographer, with the semi-automated QC schemes being far more conservative in flagging samples as 'bad'. The conservative nature of the semi-automated systems does, however, provide a solution for QC with a known risk. Our software systems should thus be seen as robust low-pass filters of the data with subsequent expert review of data flagged as 'bad' to be recommended.