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Table 4 - PrecisionAnchor Table4 Table4
Definition | Standard deviation (std) of O-B values over a defined period |
Calculation | For each observed variable, the std of all valid data is computed for every station. |
Valid data | Data not flagged as missing value (O-B is not NULL) |
Minimum required valid data | Daily: 2 valid values |
Math expression | , where Nj is number of valid data for variable j and the bar denotes the average as defined in Table 3. |
Both bias and standard deviation contribute to the overall measurement uncertainty. The root mean square error (rmse) is a common metric used to measure accuracy, and it is applied to upper-air observations in the WDQMS web tool (section 6.2) based on the quantitative information for the two vertical layers (mean and standard deviation of O-B departures over Trop and Stra, see section 2.1.2) provided by the NWP reports (see Table 5).
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Table 5 - Root Mean Square Error (rmse)Anchor Table5 Table5
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Definition | rmse of O-B values over a layer |
Calculation | For each observed variable, the rmse is computed based on valid data for every station. |
Valid data | Bias and std not NULL. |
Minimum required valid data | 2 valid value. |
Math expression | where bias and std are the average and standard deviation of O-B departures over a vertical layer (Trop or Stra, in section 2.2) |
Note that quality indicators are applied only to the measured quantities whose O-B departures are available in the NWP monitoring reports, i.e. the ones whose model equivalent is available from the NWP assimilation system (see sections 2.1.1 and 2.1.2). Therefore, if the O-B departures are missing because the model background is not calculated in a particular NWP assimilation (e.g., not all centres compute O-B departures for observations they do not use in assimilation) the quality indicator will not be calculated and the station will not show up on the quality map. This is why some stations appear in the availability map, but not in the quality map.
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