128
applicable to many observing instruments. Tests 1 through 11 are standard for most
instruments. Tests 12 and 13 are specific to T/S data pairs. A test for photic zone
limit for radiance, irradiance, and PAR is recommended for optical instruments
(U.S. Integrated Ocean Observing System 2015b), and a number of other tests referring to antenna performance are recommended for HF radar (U.S. Integrated Ocean
Observing System 2016).
During Q/C testing, an additional data column is added to the array for inclusion
of the standardized UNESCO suite of test flags as listed below:
Pass = 1
Data have passed critical real-time quality control
tests and are deemed adequate for use as
preliminary data.
Not evaluated = 2
Data have not been QC-tested, or the information
on quality is not available.
Suspect or of high interest = 3 Data are considered to be either suspect or of high
interest to data providers and users. They are
flagged suspect to draw further attention to
them by operators.
Fail = 4
Data are considered to have failed one or more
critical real-time QC checks. If they are
disseminated at all, it should be readily
apparent that they are not of acceptable quality.
Missing data = 9
Data are missing; used as a placeholder.
7.3 Experimental Validation of Remote Sensing and Ocean
Model Output Data
A model starts to have skill when the observational and predictive uncertainty halos overlap (http://www.meece.eu/documents/deliverables/WP2/D2.7.pdf Accessed 8/10/2017).
Since observational gaps do not allow for continuous synoptic real-time sampling in time and space, and model output is similarly limited by computational
capacity, both views provide only approximations to the instantaneous state of sampled (or derived) variables. Moreover, inherent instrumental and computational
errors occur in both quasi-synoptic remote sensing and model output. Experimental
validation known as vicarious calibration in the case of remote sensing or as skill
assessment in numerical model forecasting is essential to objective assessment of
instrumental data quality and forecast accuracy. Properly designed validation experiments allow the observer to adjust instrument response and the modeler to identify
and correct model deficiencies.
Assessment of instrument response can sometimes only be achieved via indirect
calibration. Substantial resources for example are expended in calibration of variables such as near-surface Chl a and CDOM derived from spectral reflectance data
7 Coastal Ocean Observing Data Quality Assurance and Quality Control, Data…
applicable to many observing instruments. Tests 1 through 11 are standard for most
instruments. Tests 12 and 13 are specific to T/S data pairs. A test for photic zone
limit for radiance, irradiance, and PAR is recommended for optical instruments
(U.S. Integrated Ocean Observing System 2015b), and a number of other tests referring to antenna performance are recommended for HF radar (U.S. Integrated Ocean
Observing System 2016).
During Q/C testing, an additional data column is added to the array for inclusion
of the standardized UNESCO suite of test flags as listed below:
Pass = 1
Data have passed critical real-time quality control
tests and are deemed adequate for use as
preliminary data.
Not evaluated = 2
Data have not been QC-tested, or the information
on quality is not available.
Suspect or of high interest = 3 Data are considered to be either suspect or of high
interest to data providers and users. They are
flagged suspect to draw further attention to
them by operators.
Fail = 4
Data are considered to have failed one or more
critical real-time QC checks. If they are
disseminated at all, it should be readily
apparent that they are not of acceptable quality.
Missing data = 9
Data are missing; used as a placeholder.
7.3 Experimental Validation of Remote Sensing and Ocean
Model Output Data
A model starts to have skill when the observational and predictive uncertainty halos overlap (http://www.meece.eu/documents/deliverables/WP2/D2.7.pdf Accessed 8/10/2017).
Since observational gaps do not allow for continuous synoptic real-time sampling in time and space, and model output is similarly limited by computational
capacity, both views provide only approximations to the instantaneous state of sampled (or derived) variables. Moreover, inherent instrumental and computational
errors occur in both quasi-synoptic remote sensing and model output. Experimental
validation known as vicarious calibration in the case of remote sensing or as skill
assessment in numerical model forecasting is essential to objective assessment of
instrumental data quality and forecast accuracy. Properly designed validation experiments allow the observer to adjust instrument response and the modeler to identify
and correct model deficiencies.
Assessment of instrument response can sometimes only be achieved via indirect
calibration. Substantial resources for example are expended in calibration of variables such as near-surface Chl a and CDOM derived from spectral reflectance data
7 Coastal Ocean Observing Data Quality Assurance and Quality Control, Data…