Download - Bayesian assimilation of rainfall sensors with fundamentally different integration characteristics
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Eawag: Swiss Federal Institute of Aquatic Science and Technology
Bayesian assimilation of rainfall sensors with fundamentally different integration characteristics
High resolution rain maps of urban catchments
April 8, 2014
Andreas Scheidegger
Continuous Assimilation of Integrating Rain Sensors
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Andreas Scheidegger – Eawag
Models of urban catchments need high-resolution rainfall input
https://flic.kr/p/wYJxB, Guillaume Bertocchi
?
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Andreas Scheidegger – Eawag
Many ways to measure rain
Rasmussen et al. (2008)
www.unidata.com.au/ww
w.o
tt.co
m
Building automation sensor
Microwave Links
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Rabiei et al. (2013)
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Andreas Scheidegger – Eawag
Sensors that measure integrated intensities
Simple Gauge: integrates over time
Radar: integrates over area (pixels)
Microwave link: integrates along path
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Andreas Scheidegger – Eawag
Integration matters
time
Rai
n in
tens
ity
integration domaint2t1
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Andreas Scheidegger – Eawag
Prior knowledge matters
time
Rai
n in
tens
ity
integration domaint2t1
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Andreas Scheidegger – Eawag
Goal: Assimilation of all available information
Signals• Different sensors
• Consider integrating
• Consider different scales (continuous, binary, …)
Prior knowledge• Temporal correlation
• Spatial correlation
Rain map• high resolution • small areas
+ =
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Andreas Scheidegger – Eawag
Sensor characterization
Point measurement: Integrated measurement:
Describe the signal noise assuming we know the true rain field
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Andreas Scheidegger – Eawag
Prior knowledge
Gaussian process with three dimensions: x, y, and time
How “likely” is a combination of rain intensities?
How “likely” is a combination of rain intensities, if something is known?
Mainly defined by the temporal and the spatial correlation length
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time or space
Rai
n in
tens
ity
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Andreas Scheidegger – Eawag
Bayesian Assimilation
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1) Infer the rain at the measured coordinates and domains
2) Extrapolation to other points
Arbitrary distributions
→ adaptive Metropolis-within-Gibbs sampler (Roberts and Rosenthal, 2009)
Gaussian
= set of all measured locations
= set of predicted locations
= set all signals
prior
signal distribution
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Andreas Scheidegger – Eawag
Microwave Links
2013-06-09 21:38:00 2013-06-09 21:38:00
x-coordinate [m] x-coordinate [m]
y-co
ordi
nate
[m]
4 k
m (2
.49
mile
s)
Rain intensities Uncertainty of rain intensities
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3 km (1.86 miles)
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Andreas Scheidegger – Eawag
Microwave Links + Pluviometers
2013-06-09 21:38:00 2013-06-09 21:38:00
x-coordinate [m] x-coordinate [m]
y-co
ordi
nate
[m]
y-co
ordi
nate
[m]
Rain intensities Uncertainty of rain intensities
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Andreas Scheidegger – Eawag
Microwave Links + Radar + Pluviometers
2013-06-09 21:38:00 2013-06-09 21:38:00
x-coordinate [m] x-coordinate [m]
y-co
ordi
nate
[m]
y-co
ordi
nate
[m]
Rain intensities Uncertainty of rain intensities
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Andreas Scheidegger – Eawag
Measure roof run-off?
maps.google.com
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Andreas Scheidegger – Eawag
Signals in arbitrary time resolution
Time resolution of predicted rain maps: 10 seconds
Measurement intervals:MWLs: 174 – 276 secondsGauges: 60 seconds
13time
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Andreas Scheidegger – Eawag
Arbitrary prediction points
Compute higher resolution for critical areas
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Andreas Scheidegger – Eawag
Arbitrary location of integration domains
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Useful to combine different radar products
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Andreas Scheidegger – Eawag
Predict integrated rain intensities directly
Predict integrated rain intensities• in space and/or • in time
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Computationally comparable to a single point on the rain map
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Andreas Scheidegger – Eawag
ConclusionsAssimilation very different (novel) sensors possible Asses benefits of
additional sensors
CAIRS is under developmentFeedback is highly welcome!https://github.com/scheidan/CAIRS.jl
Transformation: non-normal priors
?
Integration matters!
Prior formulation: add advection, diffusion?
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Continuous Assimilation of Integrating Rain Sensors
Interested in collaborating? [email protected]