recurrent neuronal network tailored for weather radar...
TRANSCRIPT
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? [email protected]
Predict prediction uncertainty
Conclusions