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 6-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.
...