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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 . 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).
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- systematic and non-systematic errors occur when the NWP
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- output is
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- verified against point observations
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- . The NWP output may not be representative of the location, height
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- , aspect of the observation
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- or capture sub-grid scale variability.
Fig3.2-1: The comparison Comparison between NWP model output and observations ought ideally to follow a two-step procedure:
- first
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- 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.
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- second step: compare the systematic errors between observation average and point observation
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- . The systematic errors come from station representativeness (i.e. the location, height and aspect of the observation)
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- . 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 model output statistics statistics MOS). A series of forecasts will also help in 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). However, sub
Sub-grid variability (notably for rainfall but other parameters too) can be addressed through by downscaling. Downscaling converts the
Downscaling converts:
- the average grid box
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- probability density functions from
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- raw ENS into
- "point rainfall probability density functions" for points within
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- the grid box.
New downscaling techniques are being developed accordingly (see for example the Point Rainfall product).