1. Infilling Radar CAPPIs
Geoff Pegram, Scott Sinclair, Stephen Wesson & Pieter Visser

2. What we’ve done …
We can remove ground-clutter and have improved the estimation of rainfall by radar at ground level
We have refined the merged fields of radar with raingauge data
We think that the combined fields are good out to 75 km from the radar with a reasonably dense network of gauges, but we’re happy to take advice!

3. NATIONAL WEATHER RADAR NETWORK see Deon’s presentation
Existing radars
Radars added (2004)
Planned radars

4. Problems with Radar CAPPI Data
Parts of radar volume scan where data is unknown
Rainfall estimates at ground level unknown
Ground clutter contamination can be extensive
Results in poor quality rainfall estimates

5. Summary of Infilling Strategy
Choose Rainfall classification algorithm
Devise Bright band correction algorithm
Semivariogram parameters determined by rainfall type. Climatological semivariograms.
Ordinary and Universal Kriging to extrapolate rain information. Universal Kriging utilised in mixed zone.
Cascade Kriging to progressively infill data down to ground.

6. Rainfall Classification
Rainfall separated into two zones:
(1) Convective Zone
(2) Stratiform Zone
Criteria of classification set out in table below.

7. Examples of Rainfall Classification
Classified Images
Reflectivity Images
18 km
0 km
CROSS SECTION X-X
dBZ
Classification X

8. Characteristics of Classified Rainfall
Stratiform – low average height, low variability and intensity.
Convective – considerable vertical extent, high variability and intensity.
Increase of rainfall intensity nearer ground level

9. Bright Band Correction
Bright Band – melting snow & ice crystals
Need to correct bright band to obtain accurate rainfall estimates at ground level
Proposed correction procedure: pixel by pixel approach
Corrected Climatological Profile
New Rainfall Estimate at Ground Level
Rainfall Estimate at Ground Level
Climatological Profile Affected by Bright Band with Extrapolation to Ground Level
CAPPI level affected by bright band corrected
Climatological Profile Correction Procedure
CAPPI level affected by bright band
Climatological Profile Affected by Bright Band
Height (km)
Typical Climatologial Profile
4 km
3 km
2 km
1 km
Reflectivity (dBZ)

10. Bright Band Correction
Testing of bright band correction
Results: improved rainfall estimates at ground level
2km CAPPI before bright band correction
2km CAPPI pixels marked which are affected by bright band
2km CAPPI after bright band correction

11. Semivariogram Modeling
Semivariogram model parameters computed for convective & stratiform rain in horizontal & vertical directions
Reflectivity Image
SILL
RANGE
30km

12. Table of Average Parameters:
Graphs indicating clustering of alpha and correlation length parameters by rainfall type
(15 Rain Events over 4 different years)
Table of Average Parameters:

13. Sensitivity Analysis of Stratiform, Horizontal Parameters
Convective Cluster: Lc  , c  
Stratiform Cluster: Ls  , s  
Missing data infilled with different combinations of α and L that represent the spread of parameter values.
No significant difference between Kriging estimates returned for spread of parameter values
L, α + σα
L, α - σα
L, α
L + σL, α
L - σL, α
L, α + σα
L, α - σα
L, α
L + σL, α
L - σL, α

14. Kriging to Infill Missing Rain Data
KRIGING used to extrapolate/interpolate horizontal and vertical rainfall information to infill unknown data points
Considered to be the optimal technique for interpolation of Gaussian data
Computational Efficiency & Stability:
Nearest 25 rainfall values used in Kriging
Singular Value Decomposition (SVD) with trimming of small singular values to ensure computational stability

15. Summary: Three Rainfall Zones
Stratiform Zone
All controls stratiform.
OK used to infill target point.
Convective Zone
All controls convective.
OK used to infill target point.
Mixed Zone
Controls stratiform & convective.
UK used to infill target point.
stratiform pixel
convective pixel
target pixel

16. Validation: Universal & Ordinary Kriging
Observed Rainfall
Kriging Estimate
Reflectivity (dBZ)
Rainrate (mm/hr)
Rainrate (mm/hr)
Reflectivity (dBZ)
All Errors Rainrate (mm/hr)
RAINRATE ERROR MAPS
Absolute Error
Reflectivity (dBZ) &
|Rainrate Errors| (mm/hr)
100 50
Stratiform Rainrate Errors (mm/hr)
Convective Rainrate Errors (mm/hr)
Mixed Rainrate Errors (mm/hr)

17. UK & OK Effectiveness
UK & OK tested on three different rainfall zones on a variety of instantaneous images
Effectiveness evaluated by comparing mean,  and Σdifference2 of estimated & observed rainfall
UK in mixed zone provides a superior estimate than OK and reduced Σdifference2

18. KRIGING directly to Ground Level
Unexpected problems with CAPPI edges
Higher Kriged values returned than expected and serious discontinuity also evident
Example: 24 hour accumulation
Rainfall Accumulation (mm)
100 80 60 40 20
Discontinuities
Inflation of Kriged values

19. Radar Volume Scan Data After Cascade Kriging
3D CASCADE
KRIGING EXAMPLE
Radar Volume Scan Data
Radar Volume Scan Data After Cascade Kriging

20. CASCADE KRIGING: Ground Clutter
Ground Clutter contaminates radar volume scan data up to 5km above ground level.
Ground Clutter 3km above ground level
Ground Clutter infilled on 3km level
Reflectivity estimation at ground level

21. Testing: Ground Clutter Infilling
Original Reflectivity Image
Ground Clutter Map
Superimposed
Ground clutter segments to be estimated
Estimated reflectivity data
Tested on 3D Bethlehem ground clutter map
Ground clutter placed onto known rain
Tested on three different rain events over 24hr period
Convert to rain rate by Marshall-Palmer equation
Store estimated and observed rain rate values and proceed to next image in sequence

22. Results: Ground Clutter Infilling
Accumulations over 6, 12 and 24 hours show close correspondence between observed and estimated values

23. Testing: Rainfall Estimation at Ground Level
MRL5 Weather Radar
Bethlehem
Raingauge Locations
Liebenbergsvlei Catchment
Polokwane
Irene
Ermelo
Bloemfontein
Bethlehem
De Aar
Durban
2 L
Selection Range
Radar Pixel Locations
1 km
Rainguage Locations
East London
Port Elizabeth
Cape Town
Extrapolated radar estimates at ground level compared to raingauge estimates

24. Results: Rainfall Estimation at Ground Level
Two rain events selected of different rainfall types – 12h & 24 h accumulations
Results indicate fair estimation of rainfall at ground level
We’ve got a handle on the errors

25. The Conditional Merging algorithm
To combine radar and gauge data optimally:
Krige the gauges to give best guess field, MG
Krige the radar pixels at gauge locations, MR
If RR is the measured radar rainfield,
Conditional Merged Field is: RC = RR + MG – MR which coincides with the gauges and interpolates intelligently

26. Conditional merging
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27. Simulation experiment
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28. Simulation experiment
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29. A real cross-validation field experiment
Compare straight Kriging and Conditional Merging on 45 rain gauges on a 4600 km2 catchment
Use cross-validation – estimation of daily total at each gauge separately using the remaining data
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30. Layout of the Liebenbergsvlei gauge network
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Bethlehem

31. Comparison of daily mean errors
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32. Errors with range – how good is the radar?
22 new gauges
4 different days of accums

33. Rainfall 9 January 2005

34. Rainfall 12 January 2005

35. Rainfall 13 January 2005

36. Rainfall 21 January 2005

39. Concluding Remarks
With intelligent extrapolation and climatoloical variograms we can get good ground estimates
With conditional merging of radar and gauge data we can get good interpolation to adjust for errors in the Z-R formula
Within 75 km from the radars, we can offer sound areas in varying climates and land cover in our expanding radar and gauge network
Check spelling and wording here. 29/06/2005