The grid values should not be considered as representing the weather conditions at the exact location of the grid point.  They should be considered as a time-space average within a two- or three-dimensional grid box.  The discrepancy between the forecast grid-point value and the verifying observed average value can be both systematic and non-systematic:

• systematic errors reflect the limitations of the NWP model’s ability to simulate the physical and dynamic properties of the system.
• non-systematic errors reflect synoptic phase and intensity errors (as indicated by the left hand green arrow in Fig3.2-1).
• systematic and non-systematic errors occur when the NWP output is verified against point observations.  The NWP output may not be representative of the location, height, aspect of the observation or capture sub-grid scale variability.

Fig3.2-1:  Comparison between NWP model output and observations ought ideally to follow a two-step procedure:

• first step: compare grid point average to observation area average.  The systematic errors are then due to model shortcomings; the non-systematic errors stem from synoptic phase and intensity errors.
• second step: compare the systematic errors between observation average and point observation.  The systematic errors come from station representativeness (i.e. the location, height and aspect of the observation) and the non-systematic errors from sub-grid scale variability.

Fig3.2-2:  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 arising from distinctly different sources.  The effects related to the two green arrows in Fig3.2-1 are here combined into one.

Systematic errors maybe due to model deficiencies and/or observational representativeness.  These can be partly corrected by statistical means (e.g. model output statistics MOS).  A series of forecasts also helps with dealing with uncertainty.

Non-systematic synoptic errors can be dampened by different ensemble approaches (e.g. medium range ensemble, probability considerations, forecast error growth).

Sub-grid variability (notably for rainfall but other parameters too) can be addressed by downscaling.  Downscaling converts the grid box area average probability density functions from the raw ENS into "point rainfall probability density functions" for points within each grid box.

New downscaling techniques are being developed accordingly (see for example the Point Rainfall product).