interpolation of meteodata using the method of regression-kriging
TRANSCRIPT
Alexander Mkrtchian ([email protected]),
Interpolation of meteodata using the method of regression-kriging
The regression-kriging method is based on the combination of multiple regression modeling, which utilizes DEM-derived morphometric data interpreted as factors influencing precipitation, and the geostatistical interpolation of regression residuals.
Modeling stages:
1) Choosing predictors;
2) Choosing scale (floating window size);
3) Calculating (multiple) regression model and deriving regressed surface;
4) Calculating residuals on station locations;
5) Interpolating residuals using kriging;
6) Adding interpolated residuals to regression surface.
Meteostations locations, overlaid on DEM
Source of elevation data: SRTM DEM http://srtm.usgs.gov
resampled to 720 m resolution
Definition of variables chosen as the best predictors of annual precipitation data:
Elevation- average value for the floating window window size 7 km;
Aspect ratio (NW/SE)- difference of average elevation values btw. two
opposite floating window quadrantswindow size 50 km;
Elevation variability- standard deviation of elevation values in floating
windowwindow size 10 km
Aspect ratio,averaged on 50 kmfloating window
Elevation, averaged on 7 kmfloating window Elevation variability,
averaged on 10 kmfloating window
Predictor maps
Predictorbeta t(31) p
Absolute elevation 0.222 1.781 0.0847
Aspect ratio (NW/SE) -0.306 -4.253 0.0002
Elevation variability 0.565 4.515 0.0001
Predictor
beta t(29) p
Absolute elevation 0.336 2.746 0.0102
Aspect ratio (NW/SE) -0.220 -2.961 0.0061
Elevation variability 0.526 4.325 0.0001
19611961
19701970
Annual precipitation value predictors,regression analysis results
Annual precipitation value predictors,regression analysis results
Pr1961
300 350 400 450 500 550 600 650 700 750 800
Predicted
200
300
400
500
600
700
800
900
Obs
erve
d
Pr_1970
600 800 1000 1200 1400 1600 1800
Predicted
400
600
800
1000
1200
1400
1600
1800
2000
Obs
erve
d
1961 1970
Multiple regression graphs for the relationships between the annual precipitation values and morphometric
parameters(observed vs. predicted values)
Multiple regression graphs for the relationships between the annual precipitation values and morphometric
parameters(observed vs. predicted values)
Regression surfaces and their residualsat stations locations
1961 1970
Predicted annual precipitation and residuals, mm
1961Multiple regression model
Multiple regression model + kriging
Annual precipitation, mm
Annual precipitation, mm
Residuals interpolated with kriging and added to regression surfaces
Multiple regression model
Multiple regression model + kriging
1970
Annual precipitation, mm
Annual precipitation, mm
Multiple regression model
Residuals interpolated with kriging and added to regression surfaces
Variance/Mean Square Error (MSE)
1961 1970
Value % Value %
Overall variance 18360 100 107650 100
MSE of the multiple regression 2537 13,8 15500 14,4
MSE after the geostatistical residual interpolation
1767 9,6 8593 8,1
The effectiveness of the modeling of the spatial distribution of annual precipitation values by the regression-kriging
Over 90% of the spatial variance of precipitation has been explainedand taken into account.Monthly average temperature fields has also beencalculated by this method.Interpolation of other climatic parameters is possible.