Metoden bak gridding av klimadata som benyttes av Meteorologisk institutt forklart av Ole Einar Tveito

1. What makes observation gridding
challenging?
· Resolution
- Spatial – sampling problem.
- Temporal – signal to noise ratio.
· Observation density and representativity
Networks are
- Sparse
- Biased
· Elevation zones
· Environment/Land Use: Are the environments of the
observation stations representative for all types of
environments? (Open field vs. forests or cities?)
· Statistical assumption vs physical processes
- Assume stationarity (2.order spatial …)
- Assume isotropy

2. Standard deviation of terrain
~10 km influence area
Elevation

3. The distribution of elevation
0 500 1000 1500 2000
0.00.20.40.60.81.0
Elevation
Frequency

4. The distribution of elevation
0 500 1000 1500 2000
0.00.20.40.60.81.0
Elevation
Frequency

5. The distribution of elevation &
temperature stations
0 500 1000 1500 2000
0.00.20.40.60.81.0
Elevation
Frequency

6. The distribution of elevation &
temperature stations
0 500 1000 1500 2000
0.00.20.40.60.81.0
Elevation
Frequency
57/195 masl 480/554 masl

7. Gridding challenges (i)
Observation gridding is basically based on statistical relations
• Quality depends on station density and representativity of the station network.
The choice of external predictor should be based on a good understanding of the
physical processes of the predictand (on a local scale).

8.  Make the observations stationary
Two approaches:
• Anomaly approach:
• Analysis of normalized (relative) values (% of normal,
deviation from normal):
The normals include the non-stationarity, and by normalizing by
the normals, the values are in principle stationary. Any statistical
interpolation method can be applied more or less automatically.
• Detrending approach:
• Analysis of absolute values
Find, and remove trends caused by topography and/or other
terrain or surface characteristics. The residual field can be
interpolated by using e.g. kriging or other suitable interpolation
techniques. This approach is often referred to as residual
kriging or detrended kriging.

9. Interpolation of monthly anomalies
· Precipitation sums: RRanom = RR / RRmean
· Temperature: TManom = TM – TMmean
· Grids of normal values are necessary to obtain
absolute values.
· Interpolation method: E.g. Topogrid
- Discretised thin plate spline technique (Hutchinson,
1988,1989, Wahba, 1990)
- Local interpolator
- Comparison with kriging and IDW shows similar
performance. The input ensure robust estimates!
- Gives smooth surfaces, (avoiding strange spatial
patterns)!

11. Time series of climate grids
Use the zonal statistics function in a GIS to
establish areal statistics.
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Norge - Årstemperatur
Norge 10 per. Mov. Avg. (Norge) Linear (Norge)