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The 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.  The discrepancy between the forecast grid-point value and the verifying observed average value can be both systematic and non-systematic.  The systematic errors reflect the limitations of the NWP model’s ability to simulate the physical and dynamic properties of the system; the non-systematic errors reflect synoptic phase and intensity errors (as indicated by the left hand green arrow in Fig3.2.1).  When the NWP model output is compared with point observations (as commonly happens in verification), additional systematic and non-systematic errors are introduced.  This is due to location of the model NWP output not being representative of the location, height and aspect of the observation, and also to sub-grid scale variability.


Fig3.2.1:  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 (i.e. the location, height and aspect of the observation) and the non-systematic 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 emanating from distinctly different sources.  The effects related to the two green arrows in Fig3.2.1 are here combined into one.

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

Non-systematic synoptic errors can be dampened by different ensemble approaches (e.g. ENS alone, ENS and HRES, probability considerations, forecast error growth), but sub-grid variability, notably for rainfall but other parameters too, can be addressed through downscaling, which converts grid box area average rainfall Probability Density Function from the raw ENS to "point rainfall Probability Density Function" for points within each grid box. 

Sub-grid variability, notably for rainfall but other parameters too, can be addressed through downscaling, which converts grid box area average probability density functions from the raw ENS to "point rainfall probability density functions" for points within each grid box.

New downscaling techniques are being developed accordingly.


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