The relation between grid point values and observations

The reduced Gaussian grid values, like all other grid values, should not be considered as representing the weather conditions at the exact location of the grid point, but as a time-space average within a two- or three–dimensional grid box (Göber et al, 2008). The discrepancy between the grid-point value and the verifying observed average can be both systematic and non-systematic. The systematic errors reflect the limitations of the model’s ability to simulate the physical and dynamic properties of the system; the non-systematic errors reflect synoptic phase and intensity errors (see Figure 13).

Figure 13:The comparison between NWP model output and observations ought ideally to follow a two-step procedure: first from grid-point average to observation area average. The systematic errors are then due to model shortcomings; the non-systematic stem from synoptic phase and intensity errors. In the next step, the systematic errors between observation average and point observation result from station representativeness and the non-systematic from sub-grid scale variability.

When the NWP model output is compared with point observations, additional systematic and non-systematic errors are introduced, due to the unrepresentativeness of the location and the observations’ sub-grid variability (see Figure 14).

Figure 14: In reality, the comparison between NWP and observations must for simplicity bypass the area average stage. This results in the systematic and non-systematic errors emanating from distinctly different sources.

Systematic errors due to model deficiencies and/or observational representativeness can be partly corrected by statistical means (see Appendix B-6). Non-systematic synoptic errors can be dampened by different ensemble approaches (see Chapter 4), but the errors due to sub-grid variability can only be remedied by new model versions with higher numerical resolutions. A model-independent estimate of the sub-grid “noise” can be made by verifying the observations from one observing station as “forecasts” for a neighbouring observing station. A typical value for homogenous terrain is about 1°C with typical distances of 50-150 km.