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kvalobs:kvoss:system:qc2:requirements:nordklim:qc2algorithms [2009-06-25 19:59:27] paule |
kvalobs:kvoss:system:qc2:requirements:nordklim:qc2algorithms [2022-05-31 09:29:32] (current) |
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- | ====== Inverse Distance Squared ====== | + | ====== Inverse Distance Squared ====== |
- | //... in preparation ...// | + | ☚ WORKING |
- | ^Results | + | Data from 2007.05.31 to 2007.12.31. For every observed value of 24 hour precipitation at a station a model value is calulated at the same point performing an inverse distance squared weighted interpolation over the nearest neighbours. |
- | |{{: | + | |
- | | - | - | | + | Interpolated_Value = // |
+ | |||
+ | **D** : The distance between the modeled point and the neighbour station. | ||
+ | The sum is performed over only the neighbours lieing within 50 km of the model point. | ||
+ | The graph below depicts the interpolated value at the model point against the actual observed value at that station. | ||
+ | |||
+ | ^Results | ||
+ | |{{kvalobs: | ||
+ | |||
+ | The same algorithm is applied below but with all neighbours within 100 km distance. | ||
+ | |||
+ | ^Results | ||
+ | |{{kvalobs: | ||
+ | |||
+ | ... and if no range limit is applied (i.e. interpolate over all possible neighbours). | ||
+ | |||
+ | ^Results | ||
+ | |{{kvalobs: | ||
+ | |||
+ | ====== Inverse Distance Squared (with Height Correction) ====== | ||
+ | |||
+ | The same algorithm is applied as for normal inverse distance square weighting except that all observations are reduced to a modelled value at sea-level, the interpolation is performed, and the result is scaled back to the interpolated point real altitude. | ||
+ | For all altitudes below 1000 m the precipitation value decreases by 10% per 100m altitude, above 1000m the change is 5% per 100m. | ||
+ | |||
+ | ^Results | ||
+ | |{{kvalobs: | ||
+ | |||
+ | |||
+ | ====== Linear Interpolation over neighbours selected by Delaunay Interpolation ====== | ||
+ | |||
+ | For a given point all neighbours with valid measurements (i.e. no missing values) are analyzed. A Delaunay Triangulation is applied to find the best three neighbours corresponding to the point of interest (e.g. [[.qc2algorithms: | ||
+ | |||
+ | Linear interpolation is then performed as follows (details courtesy of MM). | ||
+ | < | ||
+ | Denom=(x3-x1)*(y2-y1)-(x2-x1)*(y3-y1) | ||
+ | d_rr_over_d_x=((y2-y1)*(rr3-rr1)-(y3-y1)*(rr2-rr1))/ | ||
+ | d_rr_over_d_y=((x3-x1)*(rr2-rr1)-(x2-x1)*(rr3-rr1))/ | ||
+ | rr=rr1+(x-x1)*d_rr_over_d_x+(y-y1)*d_rr_over_d_y | ||
+ | </ | ||
+ | where x1, x2, x3 and y1, y2, y3 are the x and y coordinates of the three | ||
+ | points of the triangle of interest. x and y are the coordinates of the point | ||
+ | where RR is to be calculated. rr is the calculated precipitation. | ||
+ | |||
+ | |||
+ | ^Results | ||
+ | |{{kvalobs: | ||
+ | |||
+ | ====== Wet or Dry Separation ====== | ||
+ | |||
+ | FIXME | ||
+ | |||
+ | ^Results | ||
+ | |{{kvalobs: | ||
+ | |||
+ | ====== Sanity Check ====== | ||
+ | |||
+ | The following graphs includes simulated data to check the validity of the statistical values encoded. | ||
+ | |||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | |||
+ | |||
+ | ====== Binned Averages and Standard Deviation Bars ====== | ||
+ | |||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | |||
+ | --- // 2009/11/01 01:09 // | ||
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+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
+ | {{kvalobs: | ||
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