Data assimilation systems are based on methods that combine prior knowledge of the atmosphere (background) with observations in an optimal way taking into account statistical information about the errors of both pieces of information (Kalnay, 2003). Significant improvements in assimilation techniques and numerical weather forecasts in the 1980’s (short range forecast errors of similar magnitude as observation errors) allowed the use of data assimilation systems to provide diagnostic facilities to monitor the quality performance of the observational network (Hollingsworth, et al. 1986). The monitoring of data quality in WDQMS relies on the feedback from several NWP data assimilation systems - mainly the O-B departures. The quality/accuracy indicators to be considered are trueness, precision and gross error (WDQMS Guidance Document). However, for surface observations only trueness has been implemented included in the web tool, whereas for upper-air observations both indicators (Trueness and Precision) are combined into a single accuracy metric6-hourly and daily aggregations for surface observations, while both trueness and precision are integrated into a single accuracy metric (root mean square error) for upper-air observations. In surface monthly aggregations, the percentage of gross errors for the month is calculated in addition to the accuracy metric root mean square error.
Trueness
The bias (an estimate of systematic error) is used as the measure of trueness (Table 3). The targets regarding trueness are stated so that the bias (average of O-B over a certain period) should be close to zero for all measured variables (sections 2.1.1 and 2.1.2). The trueness is assessed for all the temporal intervals considered in the tool (section 4): 6-hourly, daily and, in the future, monthly. Also, a 5-day moving average (Alert) of the absolute value of daily calculated O-B (Table 9) needs to be calculated daily for all observed variables and compared against the prescribed thresholds (Table 6). This is used as one of the main performance indicators on the daily monitoring activities.
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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 |
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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 expressionwhere |
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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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