Quality control coding: Difference between revisions
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Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. <br /> | Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. <br /> | ||
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<PARAM>_QC <br /> | <PARAM>_QC <br /> | ||
<PARAM>_QC:long_name = “quality flag of <PARAM>”; <br /> | <PARAM>_QC:long_name = “quality flag of <PARAM>”; <br /> |
Revision as of 16:28, 28 May 2021
Quality control coding
We suggest to follow Ocean Sites for QC coding. The flagging scheme is mostly compatible with the primary level flagging recommended by IOC.
Flag | Meaning | Comment |
---|---|---|
0 | unknown | No QC was performed. |
1 | good data | All QC tests passed. |
2 | probably good data | Data have failed one or more QC tests but visual examination suggests data is good. |
3 | potentially correctable bad data | These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity). |
4 | bad data | Data have failed one or more tests. |
5 | - | Not used |
6 | - | Not used |
7 | nominal value | Data were not observed but reported (e.g. instrument target depth.). |
8 | interpolated value | Missing data may be interpolated from neighboring data in space or time. |
9 | missing value | This is a fill value |
Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>.
<PARAM>_QC
<PARAM>_QC:long_name = “quality flag of <PARAM>”;
<PARAM>_QC:conventions = “OceanSITES QC Flags”;
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9;
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”