Eawag: Swiss Federal Institute of Aquatic Science and Technology

Recurrent Neuronal Network tailored forWeather Radar Nowcasting

June 23, 2016

Andreas Scheidegger

Andreas Scheidegger – Eawag

Weather Radar Nowcasting

t0t

-1t-2t

-3t-4

t5t

4t3t

2t1

nowcastmodel

available inputs:every pixel of the previous images

desired outputs:every pixel of the next n images

Andreas Scheidegger – Eawag

Recurrent ANN

Pt

Ht Ht+1

ANN

Pt+1^

last observedimage

Andreas Scheidegger – Eawag

Recurrent ANN

Pt

Ht Ht+1

ANN

Ht+2

ANN

Pt+1^ Pt+2

^

last observedimage

Andreas Scheidegger – Eawag

Recurrent ANN

Pt

Ht Ht+1

ANN

Ht+2 Ht+3

Pt+3^

ANN ANN

Pt+1^ Pt+2

^

last observedimage

Andreas Scheidegger – Eawag

Recurrent ANN

Pt

Ht Ht+1

ANN

Ht+2 Ht+3 Ht+4

Pt+3^ Pt+4

^

ANN ANN ANN

Pt+1^ Pt+2

^

last observedimage

Andreas Scheidegger – Eawag

Model structure

Pt

Ht Ht+1

ANN

Pt+1^

v j=σ(∑k

w jkuk+b j)

Traditional Artificial Neuronal Networks (ANN) are (nested) non-linear regressions:

Traditional Artificial Neuronal Networks (ANN) are (nested) non-linear regressions:

How can we do better?

Andreas Scheidegger – Eawag

Model structure

Pt

Ht Ht+1

ANN

Pt+1^

v j=σ(∑k

w jkuk+b j)

Traditional Artificial Neuronal Networks (ANN) are (nested) non-linear regressions:

Traditional Artificial Neuronal Networks (ANN) are (nested) non-linear regressions:

How can we do better?

tailor model structure to problem

Andreas Scheidegger – Eawag

Model structure

Pt

Ht Ht+1

ANN

Pt+1^

Andreas Scheidegger – Eawag

Model structure

convolution

Pt

Ht

Pt+1

Ht+1

+

fullyconnected

fullyconnected

fullyconnected

spatialtransformer deconvolution

^

Gaussian blur

fullyconnected

Andreas Scheidegger – Eawag

Spatial transformer

convolution

Pt

Ht

Pt+1

Ht+1

+

fullyconnected

fullyconnected

fullyconnected

spatialtransformer deconvolution

^

Gaussian blur

fullyconnected

Jad

erb

erg

, M

., S

imo

nya

n,

K.,

Zis

serm

an

, A.,

an

d K

avu

kcu

oglu

, K

. (2

01

5)

Sp

atia

l Tra

nsf

orm

er

Ne

two

rks.

arX

iv:1

50

6.0

20

25

[cs

].

Andreas Scheidegger – Eawag

Spatial Transformer

input output

Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015) Spatial Transformer Networks. arXiv:1506.02025 [cs].

Andreas Scheidegger – Eawag

Spatial transformer

convolution

Pt

Ht

Pt+1

Ht+1

+

fullyconnected

fullyconnected

fullyconnected

spatialtransformer deconvolution

^

Gaussian blur

fullyconnected

Andreas Scheidegger – Eawag

Local correction

convolution

Pt

Ht

Pt+1

Ht+1

+

fullyconnected

fullyconnected

fullyconnected

spatialtransformer deconvolution

^

Gaussian blur

fullyconnected

Andreas Scheidegger – Eawag

Local correction

Andreas Scheidegger – Eawag

Local correction

Andreas Scheidegger – Eawag

Gaussian blur

convolution

Pt

Ht

Pt+1

Ht+1

+

fullyconnected

fullyconnected

fullyconnected

spatialtransformer deconvolution

^

Gaussian blur

fullyconnected

Andreas Scheidegger – Eawag

Recurrent ANN

Pt

Ht Ht+1

ANN

Ht+2 Ht+3 Ht+4

Pt+3^ Pt+4

^

ANN ANN ANN

Pt+1^ Pt+2

^

last observedimage

Andreas Scheidegger – Eawag

Model structure

convolution

Pt

Ht

Pt+1

Ht+1

+

fullyconnected

fullyconnected

fullyconnected

spatialtransformer deconvolution

^

Gaussian blur

fullyconnected

Andreas Scheidegger – Eawag

PredictionForecast horizon: 2.5 minutes

observed predicted

Andreas Scheidegger – Eawag

Prediction

observed predicted

Forecast horizon: 15 minutes

Andreas Scheidegger – Eawag

Prediction

observed predicted

Forecast horizon: 30 minutes

Andreas Scheidegger – Eawag

Prediction

observed predicted

Forecast horizon: 45 minutes

Andreas Scheidegger – Eawag

Prediction

observed predicted

Forecast horizon: 60 minutes

Andreas Scheidegger – Eawag

Prediction

Andreas Scheidegger – Eawag

Conclusions

Andreas Scheidegger – Eawag

Deep learning may be a hype – but it's a useful tool nevertheless!

Conclusions

Andreas Scheidegger – Eawag

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

Deep learning may be a hype – but it's a useful tool nevertheless!

Conclusions

Andreas Scheidegger – Eawag

and Outlook

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

Deep learning may be a hype – but it's a useful tool nevertheless!

Conclusions

Andreas Scheidegger – Eawag

and Outlook

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

θt+1=θt−λ ∇ L(θt)Online parameter adaptionDeep learning may be a

hype – but it's a useful tool nevertheless!

Conclusions

Andreas Scheidegger – Eawag

and Outlook

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

θt+1=θt−λ ∇ L(θt)Online parameter adaption

L(θ)Other objective function?

Deep learning may be a hype – but it's a useful tool nevertheless!

Conclusions

Andreas Scheidegger – Eawag

and Outlook

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

θt+1=θt−λ ∇ L(θt)Online parameter adaption

L(θ)Other objective function?

Deep learning may be a hype – but it's a useful tool nevertheless!

Include other inputs

Conclusions

Andreas Scheidegger – Eawag

and Outlook

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

θt+1=θt−λ ∇ L(θt)Online parameter adaption

L(θ)Other objective function?

Deep learning may be a hype – but it's a useful tool nevertheless!

Include other inputs

Predict prediction uncertainty

Conclusions

Andreas Scheidegger – Eawag

and Outlook

v j=σ(∑

k

w jku k+b j)

Combine domain knowledge with data driven modeling

θt+1=θt−λ ∇ L(θt)Online parameter adaption

L(θ)Other objective function?

Deep learning may be a hype – but it's a useful tool nevertheless!

Include other inputs

Interested in collaboration? andreas.scheidegger@eawag.ch

Predict prediction uncertainty

Conclusions