Detection of faulty rain gauges using telecommunication microwave links

Blandine Bianchi, EPFL

Alexis Berne, EPFL

Jörg Rieckermann, SWW

Take-home message!

• Create redundant information• Use advanced data analysis techniques

We need to ensure good data quality!

MotivationThe Hochrhein-bridge

MotivationThe Hochrhein-bridge

MotivationThe Hochrhein-bridge

27 cm

MotivationThe Hochrhein-bridge

54 cm

MotivationThe Hochrhein-bridge

54 cm

Urban HydrologyData 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

Urban Water ManagementControl charts for online sensors

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435 63 91

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11.Jun.01 09.Jul.01 06.Aug.01

Sensor valueReference measurement = grab sample

6. aug 019. july 0111. june 01

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Change ofmembrane

In-control

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

Urban HydrologyQuality control for rain gauges

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0

435 63 91

0

10

20

30

40

11.Jun.01 09.Jul.01 06.Aug.01

Sensor valueReference measurement = grab sample

6. aug 019. july 0111. june 01

Diff

eren

ces

Con

cent

ratio

ns

Change ofmembrane

In-control

We need• Redundant information on precipitation• Statistical quality control methods• Automated procedures

Thomann et al. (2002)

Microwave links from telecommunication networksMWL signals are attenuated by rainfall

Microwave links from telecommunication networksMWL signals are attenuated by rainfall

Rain gauge

MWL

Microwave links from telecommunication networksMWL signals are attenuated by rainfall

Microwave links from telecommunication networksMWL signals are attenuated by rainfall

Microwave links from telecommunication networksMWL signals are attenuated by rainfall

IdeaUsing MWL signals for quality control

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ARA Glatt

ARA Neugut

Glattzentrum

Josefstrasse

ARA Werdhölzli

Wasserwerk Moos

Völkerkundemuseum

Friedhof Fluntern

Friedhof Nordheim

Friedhof Enzenbühl

Friedhof Affoltern

Friedhof Friesenberg

Friedhof Schwamendingen

0 1 2 3 4 50.5

Kilometer

TextText

Legende

!. Regenmesser ERZ und Eawag

ORANGE Richtfunkantennen

Case study ZurichQuality 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).

Case study ZurichQuality 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

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

[%]

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

[%]

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

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

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.

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)

2. Plotting CUSUM statistic of differences of sensors to check out-of-control

Rain Gauge No.13 vs. Link No.7

*Montgomery, D.C. (2000) Introduction to Statistical Quality Control, 4th ed. New York: John Wiley & Sons.

CU

SU

M

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)

2. Plotting CUSUM statistic of differences of sensors to check out-of-control

3. Apply classification rule during monitoring period

Rain Gauge No.13 vs. Link No.7

*Montgomery, D.C. (2000) Introduction to Statistical Quality Control, 4th ed. New York: John Wiley & Sons.

CU

SU

M

Scenario analysisTesting the sensitivity of the methodology

Introducing virtual errors

Scenario analyisTesting the sensitivity of the methodology

Introducing virtual errors

1. Blockage at 1 [mm/h]

Scenario analyisTesting the sensitivity of the methodology

Introducing virtual errors

1. Blockage at 1 [mm/h]

2. Relative error of 170%

Scenario analyisTesting 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]

Conclusions

• Create redundant information• Use advanced data analysis techniques

We need to ensure good data quality!

Conclusions

• Create redundant information• Use advanced data analysis techniques

We need to ensure good data quality!

Bianchi, B. et al., Detection of faulty rain gauges using telecommunication microwave links (submitted to Journal of Hydrology)

• 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