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).  The non-systematic errors come 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 average grid box probability density functions from raw ENS into
• "point rainfall probability density functions" for points within the grid box.

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