Accurate rain rate measurements are essential for many hydrological applications. Although rain gauge remains the reference instrument for the measurement of rain rate, the strong spatial and temporal variability of rainfall makes it difficult to spot faulty rain gauges. Due to the poor spatial representativeness of the point rainfall measurements, this is particularly difficult where their density is low. Taking advantage of the high density of telecommunication microwave links in urban areas, a consistency check is proposed to identify faulty rain gauges using nearby microwave links. The methodology is tested on a data set from operational rain gauges and microwave links, in Zürich (Switzerland). The malfunctioning of rain gauges leading to errors in the occurrence of dry/rainy periods are well identified. In addition, the gross errors affecting quantitative rain gauge measurements during rainy periods, such as blocking at a constant value, random noise and systematic bias, can be detected. The proposed approach can be implemented in real time.

1. Detection of faulty rain gauges using
telecommunication microwave links
Blandine Bianchi, EPFL
Alexis Berne, EPFL
Jörg Rieckermann, SWW

2. Take-home message!
We need to ensure good data quality!
• Create redundant information
• Use advanced data analysis techniques

3. Motivation
The Hochrhein-bridge

4. Motivation
The Hochrhein-bridge

5. Motivation
The Hochrhein-bridge
27 cm

6. Motivation
The Hochrhein-bridge
54 cm

7. Motivation
The Hochrhein-bridge
54 cm

8. Urban Hydrology
Data quality control?
• At least every two years
• Dynamic calibration recommended
Stransky, D. et al. (2007) The effect of rainfall measurement uncertainties on rainfall-runoff process
modelling Water Science and Technology Vol 55 No 4

9. Urban Water Management
Concentrations
Control charts for online sensors
40
30
20
10
Sensor value
Reference measurement = grab sample
0
11.Jun.01
11. june 01
35
09.Jul.01
06.Aug.01
9. july 01
6. aug 01
63
91
4
Differences
In-control
0
-4
Change of
membrane
Thomann, M. et al. (2002) An efficient monitoring concept with control charts for on-line sensors
Water Science and Technology Vol 46 No 4–5 pp 107–116

10. Urban Hydrology
Concentrations
Quality control for rain gauges
40
30
20
10
Sensor value
Reference measurement = grab sample
0
11.Jun.01
11. june 01
35
09.Jul.01
06.Aug.01
9. july 01
6. aug 01
63
91
4
Differences
In-control
0
-4
Change of
membrane
Thomann et al. (2002)
We need
• Redundant information on precipitation
• Statistical quality control methods
• Automated procedures

11. Microwave links from telecommunication networks
MWL signals are attenuated by rainfall

12. Microwave links from telecommunication networks
MWL signals are attenuated by rainfall
Rain gauge
MWL

13. Microwave links from telecommunication networks
MWL signals are attenuated by rainfall

14. Microwave links from telecommunication networks
MWL signals are attenuated by rainfall

15. Microwave links from telecommunication networks
MWL signals are attenuated by rainfall

16. Idea
Using MWL signals for quality control
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Friedhof Affoltern
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ARA Glatt
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Friedhof Nordheim
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ARA Werdhölzli
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Glattzentrum
ARA Neugut
Friedhof Schwamendingen
Josefstrasse
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Friedhof Fluntern
Text
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Völkerkundemuseum
Friedhof Friesenberg
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Legende
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0
Regenmesser ERZ und Eawag
ORANGE Richtfunkantennen
0.5
1
2
Kilometer
3
4
5
!
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Wasserwerk Moos
Friedhof Enzenbühl
#
*

17. Case study Zurich
Quality control of rain gauges
Material
• 14 operational links (Orange CH),
frequencies 23-58 GHz, 0.3 – 8.4 Km,
• 14 rain gauges
(13 tipping-buckets and 1 weighing).

18. Case study Zurich
Quality control of rain gauges
Material
• 14 operational links (Orange CH),
frequencies 23-58 GHz, 0.3 – 8.4 Km,
• 14 rain gauges
(13 tipping-buckets and 1 weighing).
Goal and Methods
1. Detect occurrence of dry/rainy periods
=> Ratio test
2. Detect quantitative errors
=> CUSUM control charts

19. Results (1)
Detection of dry and rainy periods
Ratio test
1. Compute contingency
table for reference
periods (both sensors are
in agreement)
Rain Gauge No. 8
[%]

20. Results (1)
Detection of dry and rainy periods
Ratio test
1. Compute contingency
table for reference
periods (both sensors are
in agreement)
2. Define thresholds
faulty: 1%
suspicious: 5%
Rain Gauge No. 8
[%]

21. Results (1)
Detection of dry and rainy periods
Ratio test
1. Compute contingency
table for reference
periods (both sensors are
in agreement)
2. Define thresholds
faulty: 1%
suspicious: 5%
3. Apply classification rule
during monitoring period

22. Results (1)
Detection of dry and rainy periods
Ratio test
1. Compute contingency
table for reference
periods (both sensors are
in agreement)
2. Define thresholds
faulty: 1%
suspicious: 5%
3. Apply classification rule
during monitoring period
Rain Gauge No.13

23. Results (2)
Detection of quantitative errors
CUSUM* control chart
Suited to identify small
biases/ shifts in process
1. Compute MWL rain rates
and target value
(reference period)
*Montgomery, D.C. (2000) Introduction to Statistical Quality Control, 4th ed. New York: John Wiley & Sons.

24. Results (2)
Detection of quantitative errors
Rain Gauge No.13 vs. Link No.7
CUSUM
CUSUM* control chart
Suited to identify small
biases/ shifts in process
1. Compute MWL rain rates
and target value
(reference period)
2. Plotting CUSUM statistic
of differences of sensors
to check out-of-control
*Montgomery, D.C. (2000) Introduction to Statistical Quality Control, 4th ed. New York: John Wiley & Sons.

25. Results (2)
Detection of quantitative errors
Rain Gauge No.13 vs. Link No.7
CUSUM
CUSUM* control chart
Suited to identify small
biases/ shifts in process
1. Compute MWL rain rates
and target value
(reference period)
2. Plotting CUSUM statistic
of differences of sensors
to check out-of-control
3. Apply classification rule
during monitoring period
*Montgomery, D.C. (2000) Introduction to Statistical Quality Control, 4th ed. New York: John Wiley & Sons.

26. Scenario analysis
Testing the sensitivity of the methodology
Introducing virtual errors

27. Scenario analyis
Testing the sensitivity of the methodology
Introducing virtual errors
1. Blockage at 1 [mm/h]

28. Scenario analyis
Testing the sensitivity of the methodology
Introducing virtual errors
1. Blockage at 1 [mm/h]
2. Relative error of 170%

29. Scenario analyis
Testing the sensitivity of the methodology
Introducing virtual errors
1. Blockage at 1 [mm/h]
2. Relative error of 170%
3. Random error of 1-10 [mm/h]

30. Conclusions
We need to ensure good data quality!
• Create redundant information
• Use advanced data analysis techniques

31. Conclusions
We need to ensure good data quality!
• Create redundant information
• Use advanced data analysis techniques
• We demonstrated the possibility to identify faulty
rain gauges using operational telecommunication
microwave links (MWL)
• The algorithms for process control are straight
forward to apply, also in real-time
• MWL data are not (yet) generally available
Bianchi, B. et al., Detection of faulty rain gauges using telecommunication microwave
links (submitted to Journal of Hydrology)