Recurrent Neuronal Network tailored for Weather Radar...
Transcript of Recurrent Neuronal Network tailored for Weather Radar...
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Eawag: Swiss Federal Institute of Aquatic Science and Technology
Recurrent Neuronal Network tailored forWeather Radar Nowcasting
June 23, 2016
Andreas Scheidegger
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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
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Andreas Scheidegger – Eawag
Recurrent ANN
Pt
Ht Ht+1
ANN
Pt+1^
last observedimage
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Andreas Scheidegger – Eawag
Recurrent ANN
Pt
Ht Ht+1
ANN
Ht+2
ANN
Pt+1^ Pt+2
^
last observedimage
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Andreas Scheidegger – Eawag
Recurrent ANN
Pt
Ht Ht+1
ANN
Ht+2 Ht+3
Pt+3^
ANN ANN
Pt+1^ Pt+2
^
last observedimage
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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
![Page 7: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/7.jpg)
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?
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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
![Page 9: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/9.jpg)
Andreas Scheidegger – Eawag
Model structure
Pt
Ht Ht+1
ANN
Pt+1^
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Andreas Scheidegger – Eawag
Model structure
convolution
Pt
Ht
Pt+1
Ht+1
+
fullyconnected
fullyconnected
fullyconnected
spatialtransformer deconvolution
^
Gaussian blur
fullyconnected
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Andreas Scheidegger – Eawag
Spatial transformer
convolution
Pt
Ht
Pt+1
Ht+1
+
fullyconnected
fullyconnected
fullyconnected
spatialtransformer deconvolution
^
Gaussian blur
fullyconnected
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Andreas Scheidegger – Eawag
Spatial Transformer
input output
Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015) Spatial Transformer Networks. arXiv:1506.02025 [cs].
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Andreas Scheidegger – Eawag
Spatial transformer
convolution
Pt
Ht
Pt+1
Ht+1
+
fullyconnected
fullyconnected
fullyconnected
spatialtransformer deconvolution
^
Gaussian blur
fullyconnected
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Andreas Scheidegger – Eawag
Local correction
convolution
Pt
Ht
Pt+1
Ht+1
+
fullyconnected
fullyconnected
fullyconnected
spatialtransformer deconvolution
^
Gaussian blur
fullyconnected
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Andreas Scheidegger – Eawag
Local correction
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Andreas Scheidegger – Eawag
Local correction
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Andreas Scheidegger – Eawag
Gaussian blur
convolution
Pt
Ht
Pt+1
Ht+1
+
fullyconnected
fullyconnected
fullyconnected
spatialtransformer deconvolution
^
Gaussian blur
fullyconnected
![Page 18: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/18.jpg)
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
![Page 19: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/19.jpg)
Andreas Scheidegger – Eawag
Model structure
convolution
Pt
Ht
Pt+1
Ht+1
+
fullyconnected
fullyconnected
fullyconnected
spatialtransformer deconvolution
^
Gaussian blur
fullyconnected
![Page 20: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/20.jpg)
Andreas Scheidegger – Eawag
PredictionForecast horizon: 2.5 minutes
observed predicted
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Andreas Scheidegger – Eawag
Prediction
observed predicted
Forecast horizon: 15 minutes
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Andreas Scheidegger – Eawag
Prediction
observed predicted
Forecast horizon: 30 minutes
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Andreas Scheidegger – Eawag
Prediction
observed predicted
Forecast horizon: 45 minutes
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Andreas Scheidegger – Eawag
Prediction
observed predicted
Forecast horizon: 60 minutes
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Andreas Scheidegger – Eawag
Prediction
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Andreas Scheidegger – Eawag
Conclusions
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Andreas Scheidegger – Eawag
Deep learning may be a hype – but it's a useful tool nevertheless!
Conclusions
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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
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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
![Page 30: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/30.jpg)
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
![Page 31: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/31.jpg)
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
![Page 32: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/32.jpg)
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
![Page 33: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/33.jpg)
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
![Page 34: Recurrent Neuronal Network tailored for Weather Radar Nowcastinggeomla.grf.bg.ac.rs/site_media/static/presentations/day... · 2017. 1. 19. · Recurrent Neuronal Network tailored](https://reader034.fdocuments.us/reader034/viewer/2022052016/602f130f38e713079d7d98f1/html5/thumbnails/34.jpg)
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