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© Springer International Publishing AG, part of Springer Nature 2018
J. E. Corredor, Coastal Ocean Observing,
https://doi.org/10.1007/978-3-319-78352-9_7
Chapter 7
Coastal Ocean Observing Data Quality
Assurance and Quality Control, Data
Validation, Databases, and Data Presentation
Abstract High value of instrumental databases is attained and maintained through
a rigorous quality assurance/quality control program involving care of the instrument and sensors to assure the quality of the data stream, careful instrument calibration, and continuous data quality control to ensure that data is flowing and to detect
spikes and data gaps or instrument drift. In the case of remote sensing or numerical
model databases, vicarious calibration or field validation is required. Instrument
data and metadata are stored digitally using prescribed character codes and database
formats that facilitate retrieval and visualization of selected data packets from large
databases. Value of the data to the user is affirmed when knowledge is extracted
from the data packet through visualization of data products that may include tables
or time series data but most usually consist of a combination of graphics and text.
Keywords Quality assurance · Quality control · Data validation · Skill assessment
· Databases · Data visualization
7.1 Introduction
Ocean observing data was still recorded by putting pencil to paper only a few
decades ago. Most operational data is now recorded through electronic interrogation
of a sensor and interpretation of the sensor state through calibration algorithms.
Electronic sensors coupled to digital electronic processors vastly improve upon the
performance of the previous generation of mechanical analog readout instruments
in terms of data density and data quality. The glass/mercury thermometer, still in use
in the second part of the twentieth century, is a case in point. These instruments
were deployed by hand and read by eye allowing at best perhaps a few hundred raw
data points per year per thermometer. Laborious calculations were further required
to correct each thermometer reading. The data sampling rate of an electronic field
instrument on the other hand is only governed in theory by the native response time
The original version of this chapter was revised. A correction to this chapter can be found at
https://doi.org/10.1007/978-3-319-78352-9_9
© Springer International Publishing AG, part of Springer Nature 2018
J. E. Corredor, Coastal Ocean Observing,
https://doi.org/10.1007/978-3-319-78352-9_7
Chapter 7
Coastal Ocean Observing Data Quality
Assurance and Quality Control, Data
Validation, Databases, and Data Presentation
Abstract High value of instrumental databases is attained and maintained through
a rigorous quality assurance/quality control program involving care of the instrument and sensors to assure the quality of the data stream, careful instrument calibration, and continuous data quality control to ensure that data is flowing and to detect
spikes and data gaps or instrument drift. In the case of remote sensing or numerical
model databases, vicarious calibration or field validation is required. Instrument
data and metadata are stored digitally using prescribed character codes and database
formats that facilitate retrieval and visualization of selected data packets from large
databases. Value of the data to the user is affirmed when knowledge is extracted
from the data packet through visualization of data products that may include tables
or time series data but most usually consist of a combination of graphics and text.
Keywords Quality assurance · Quality control · Data validation · Skill assessment
· Databases · Data visualization
7.1 Introduction
Ocean observing data was still recorded by putting pencil to paper only a few
decades ago. Most operational data is now recorded through electronic interrogation
of a sensor and interpretation of the sensor state through calibration algorithms.
Electronic sensors coupled to digital electronic processors vastly improve upon the
performance of the previous generation of mechanical analog readout instruments
in terms of data density and data quality. The glass/mercury thermometer, still in use
in the second part of the twentieth century, is a case in point. These instruments
were deployed by hand and read by eye allowing at best perhaps a few hundred raw
data points per year per thermometer. Laborious calculations were further required
to correct each thermometer reading. The data sampling rate of an electronic field
instrument on the other hand is only governed in theory by the native response time
The original version of this chapter was revised. A correction to this chapter can be found at
https://doi.org/10.1007/978-3-319-78352-9_9