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). The significant improvements in the assimilation techniques and numerical weather forecasts in the 1980’s (short range forecast errors of similar order of magnitude of the 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 - and 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 in the web tool, whereas for upper-air observations both indicators (Trueness and Precision) are combined into a single accuracy metric.
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 3.1 and 3.2). 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.
Table 3 - Trueness
Definition | Average of O-B values over a defined period |
Calculation | For each observed variable, the average 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 | 1 valid value. |
Math expression | where Nj is number of valid data for variable j. |
Precision
The standard deviation (estimate of random error) is the quantitative measure of precision (Table 4). The targets for precision are applied to the standard deviation of O-B over a certain period for each of the observed variables (Table 6). Like trueness, precision will be assessed 6-hourly, daily and monthly. Also, the 5-day moving average (Alert) of daily calculated standard deviation of O-B (Table 9) will be calculated for all variables and compared to the respective prescribed threshold ( Table 6). This together with the performance indicator for Trueness will be used by the Evaluation function on their daily monitoring activities to determine the level of priority for stations showing accuracy/measurement uncertainty issues (see table in Annex2 of WDQMS Guidance Document).