how deep learning could predict weather events

NVIDIA GPU Technology Conference 2018Copyrightcopy 2018 Sa-Kwang Song KISTI

How Deep Learning Could Predict Weather Events

Seongchan Kim PhD Seunkyun Hong

On behalf of Sa-Kwang Song PhD

Research Data Platform Center

GTC 2018

NVIDIA GPU Technology Conference 2018Copyrightcopy 2018 Sa-Kwang Song KISTI 2

Outline

bull Introduction Vision vs Meteorology tasks

bull Deep Learning for Weather Predictionbull DeepRain Precipitation Prediction of amp Next Step of Images

bull DeepTC Tropical Cyclone Trajectory Prediction usingNumerical Model Data

bull GlobeNet Tropical Cyclone Trajectory Prediction usingSatellite Images amp AutoEncoder

bull Conclusion


Outline





bull Conclusion


Vision vs Meteorology Tasks

Typhoon

Classification

Typhoon

Location WeatherClimate Event Detection

Or Segmentation

httpschaosmailgithubiodeeplearning20161022intro-to-deep-learning-for-computer-vision


Similarities vs Differences

Phrabat 2017(CI 2017)

bull Similaritiesbull Tasks are similar

bull Classification Localization Detection Segmentationbull Clustering Regressionbull Representation Learning

bull Differencesbull Unique attributes of Weather Data

bull Multi-channelMulti-variatebull Different Spatio-temporal scalesbull Double precision floating pointbull Underlying statistics are likely differentbull Large amount of data compared to general Vision tasks


Challenge Multi-Variate Data

COMS Satellite Himawari 8 satellite

WRF Result


Outline


bull Deep Learning for Weather Predictionbull DeepRain Prediction of Precipitation amp Next Step of Images

bull DeepTC GPU-Accelerated Trajectory Prediction for TropicalCyclone using Traditional Numerical Model Data

bull GlobeNet Typhoon Track Prediction amp Autoencoder

bull Conclusion


Numerical Model Results

Satellite

Radar

BAIPAS

ImageSensor Data

Prediction Model based on DL

Convolutional Neural Networks

Recurrent Neural Networks

Sensors

Typhoon Track

Surge LevelFlood Level

8

Prediction of Weather Events

Bigdata amp AI based Prediction and Analysis Platform

Disaster Prediction


DeepRain Precipitation Prediction

Weather Radar CenterhttpradarkmagokrlectureradardataflowdoMethods used to measure precipitation

bull Weather Radar Databull refers to data represented by a radar image that is composed using the moving

speed direction and strength of a signal transmitted by a radar transmitter into the atmosphere and received after it has collided with water vapor or the like



6 mins 15 = 90 mins


Shenzhen Meteorological Bureau-Alibab ldquoShort-Term Quantitative Precipitation Forecastingrdquo httpstianchialiyuncomcompetitioninformationhtmspm=517610006756782jsxLyXampraceId=231596

11

DeepRain Radar Data

bull Research-use data by Shenzhen Meteorological Agencybull Modeling specific areas in Shenzhen as a grid pattern(101101km2)

bull Radar reflection values 101101 numerical values (dBZ) of representing each cell

bull Precipitation amount Total amount of rainfall in target site (5050 area from center)

bull Normalization Anonymization

101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12


httpradarkmagokrlectureradardataflowdo

Height 1

Height 2

Height 3

Height 4


DeepRain Experimental Results

1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)

FC-LSTM(GDO0001) FC-LSTM(Adam0001)convLSTM(Adam 0001) convLSTM(Adam00012-stacked)

bull convLSTM shows better learning performance than FC-LSTM

bull Test result with Testsetbull Epoch 5

bull With two-stacked we achieved 230 performance increase than LR

bull Furthermore it is lower 217 than FC-LSTM

bull Because FC-LSTM lost spatial information

Model RMSE Drop Ratio

Linear Regression 1469 -DeepRain FC-LSTM 1446 16

DeepRain convLSTM 1151 216DeepRain convLSTM(2-stacked) 1131 230


Seq-to-Seq DeepRain Prediction of Next Seq Image

The last N input images as input sequence Prediction output sequence

tt-1t-2t-3t-4t-5t-6t-7t-8t-9 t+1 t+2 t+3 t+4 t+5


Seq-to-Seq DeepRain Conv2Deconv RNNLSTM

bull Model Sequence-to-Sequence Modelbull Input rarr encoderbull decoder Output


DeepRain Deep Learning Models

bull Input and state transformation models1) Vanilla fully-connected models

bull Vanilla FullyConnected-RNNbull Vanilla FullyConnected-LSTM

2) Convolution to Deconvolution (Conv2Deconv) modelsbull Convoluted RNNrsquos states (Conv2Deconv-ConvRNN)bull Convoluted LSTMrsquos states and memory cells(Conv2Deconv-ConvLSTM)

3) Full-size convolution(Input images are fed directly to the convolutional networks)bull Convoluted RNNrsquos states (Fullsize-ConvRNN)bull Convoluted LSTMrsquos states and memory cells (Fullsize-ConvLSTM)


DeepRain Results


DeepRain Conv2Deconv-ConvLSTM

rarr Note predicted images are blurred- Testing mean-cost=1316 mean-rmse=2242- Testing best-cost=671 best-rmse=1073

The last 10 input images

Model Predicted images

Ground truth images


Outline



bull DeepTC Tropical Cyclone Trajectory Prediction usingTraditional Numerical Model Data


bull Conclusion


bull Today meteorologists rely on numerical models to predict wind speed precipitation air pressure and other factors that indicate the path and intensity of a hurricane over its lifetimebull WRF (Weather Research and Forecasting model) MPAS (Model for Prediction

Across Scales) UM (Unified Model) and CAM5 (Community Atmosphere Model ver 50)

20

Motivation

Bolaben Wind (UVW) Bolaben DBz Bolaben Flow

Visualization (Vapor)


bull Predicting typhoon track predict using data from a numerical model (WRF) simulation results with Deep Neural Networkbull ConvLSTM Convolutional LSTM (shi et al 2015) that learn spatial features of

input data

bull Ensemble-like techniques learning from five differently conditioned WRF results

Ensemble Forecasting

21

Purpose


bull 10 typhoons drifting in close proximity to the Korea peninsula

No

ID(YYNN)

Name Period DurationSimulation Durations

Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500

9 days 3 hours 4 days 6 hours 25

2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900

7 days 4 days 18 hours 19

9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data


Name Description Dimension Data Dim

T perturbation potential temperature

(5 29 192 192) 4 dimension(time height width length)

P perturbation pressure

QVAPOR Water vapor mixing ratio

SST Skin sea surface temperature(5 1 192 192)

OLR TOA outgoing long wave

bull Selected variables bull 5 variables deemed to be the most significant for cyclone

tracking

bull four-dimensional bull Time height width and length

bull represented in 3-D spatial grids

23

Data


bull Simulation Settingbull 10 typhoons 1060 predictions

bull For example Rusa 4 days 6 hours

bull Simulation every 6 hour cyclically

bull Five different initial conditions (models)

bull 212 5 = 1060bull Whole WRF file size 22 TB

bull Data split

of instances TF Record Size

Training set 600 150 Gb

Validation set 200 50 Gb

Testing set 200 50 Gb

24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4

Prediction every 6 hours for next 24 hours

0000 0600 2400

Best track(Ground Truth)

(lat0 long0)

_O0 _O1 _O4

(lat1 long2) (lat4 long4)

Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model

Prediction(lat0 long0) (lat1 long2) (lat4 long4)


bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE

FC-LSTM 14724DeepTC ConvLSTM 1388

Result

- K40m(x2) reduced 54 of training time against CPU - While P100(x2) reduced 56 of training time against

K40m for 100 epoch

bull Test Result


Outline



bull DeepTC Tropical Cyclone Prediction using Numerical ModelData

bull GlobeNet Typhoon Track Prediction using Satellite Images ampAutoEncoder

bull Conclusion


GlobeNet Typhoon Track Prediction

Observation Data

COMS-1MI (KMA)

Himawari-8 images (16ch)

Prediction of Typhoon Track

Deep Learning Prediction Models

HIMAWARI-8AHI (JMA)

Convolutional Neural Nets (CNNs)

Long shortTerm Memory (LSTM)

Index Conf Lat Long hPa NSWE

1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data

bull Input Satellite Data

bull COMS-1 Satellite 4 channel(IR1 IR2 SWIR WV)

bull Period 201104~201612

bull Label Best Track Data

bull RSMC-Tokyo Best Track

bull 6 years 2011~2016 (152 typhoon cases)

Typhoon TrackCOMS-1


GlobeNet Typhoon Location Detection

Input Satellite Images

Typhoon Location Tracking

Conv2D based Model

Inception based Model


GlobeNet Experiments


GlobeNet Test Error

Distance(km)

- Average Error(distance in km) 7453 km- Eye of typhoon 30-65km in diameter

30-65km


GlobeNet Autoencoder

bull To build a pre-trained model

bull COMS-1

bull 4 channelIR1 IR2 SWIR WV

bull Size 1655 TB (201104~2017 187083 scenes)

bull HIMAWARI-8 AHIbull On-going

bull Size hundreds of TBs

bull Conv-Deconv Neural Network

Encoder(Convolution Step)

Decoder(Deconvolution

Step)

LatentVector


GlobeNet Encoding Step

Convolution Step

Layer 1 Layer2 Layer3 Layer4 Layer5Input


GlobeNet Decoding Step

Deconvolution Step

Layer4 Layer3 Layer2 Layer1Layer5

Prediction



Input Images

Output Images

Conv-Deconv Model

Skip-connection Model


GlobeNet with Skip-connection Decoder

Output Images


Recent Convolutional AE without Skip-connection

Output Images

Input Images


Application of Skip-connection Conv AE PSIque

bull Complex Deep Seq2Seq Autoencoder model based on Memory Network structure

bull Unified model structure compatible with various RNNCells (eg LSTM GRU ConvLSTM)

bull Encoder(Conv)-gtEncoder(LSTM)-gtDecoder(LSTM)-gtDecoder(DeConv) oplus SkipConx

Encoder (Conv)5-layers ConvNet for Feature Extraction

Encoder (LSTM)Encoding SpatiotemporalChanges Through Time

Decoder (LSTM)Decoding SpatiotemporalChanges Through Time

Decoder (DeConv)5-layers DeConvNet for Image Restoration

SkipConxSelective Symmetricbypassing skip connection from Encoder to Decoder

Latent Info (OutputState)Condensed Tensor representing Spatiotemporal Transition


Outline


bull Deep Learning for Weather Predictionbull GlobeNet Typhoon Track Prediction amp Autoencoder

bull DeepRain Prediction of Precipitation amp Next Step of Images


bull Conclusion


Conclusion

bull Deep Learning can be applied effectively to understand meteorological phenomenonbull DeepRain does using radar reflectivity data

bull DeepTC does using numerical model data

bull GlobeNet predicts meteorological phenomenon by analyzing satellite images

bull Distributed Deep Learning Platform is necessary

bull Number of associated challenges

bull Open to collaboration



Outline





bull Conclusion


Outline





bull Conclusion



Typhoon

Classification

Typhoon


Or Segmentation












WRF Result


Outline





bull Conclusion



Satellite

Radar

BAIPAS

ImageSensor Data




Sensors

Typhoon Track


8



Disaster Prediction








6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









Outline





bull Conclusion



Typhoon

Classification

Typhoon


Or Segmentation












WRF Result


Outline





bull Conclusion



Satellite

Radar

BAIPAS

ImageSensor Data




Sensors

Typhoon Track


8



Disaster Prediction








6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Typhoon

Classification

Typhoon


Or Segmentation












WRF Result


Outline





bull Conclusion



Satellite

Radar

BAIPAS

ImageSensor Data




Sensors

Typhoon Track


8



Disaster Prediction








6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion


















WRF Result


Outline





bull Conclusion



Satellite

Radar

BAIPAS

ImageSensor Data




Sensors

Typhoon Track


8



Disaster Prediction








6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









Outline





bull Conclusion



Satellite

Radar

BAIPAS

ImageSensor Data




Sensors

Typhoon Track


8



Disaster Prediction








6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Satellite

Radar

BAIPAS

ImageSensor Data




Sensors

Typhoon Track


8



Disaster Prediction








6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion















6 mins 15 = 90 mins



11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










11

DeepRain Radar Data





101101km2


(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









(101 101 4)

ConvLSTM

h0

ConvLSTM

ConvLSTM

helliphellip

119865119894119899119886119897 119875119903119890119889119894119888119905119894119900119899

_X _X_119894119904119905119886119905119890

_119871119878119879119872_119874

1 2

_O _O _O

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_X15

119868119899119901119906119905 119868119899119901119906119905 119868119899119901119906119905(101 101 4) (101 101 4)

12



Height 1

Height 2

Height 3

Height 4



1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










1 3 5 7 9 11 13 15 17 19 21 23 25 27

0

5

10

15

20

25

30

Epoch

Loss

(RM

SE)
























DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion






















DeepRain Results






Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion













Ground truth images


Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









Outline





bull Conclusion




20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion











20

Motivation





input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










input data



21

Purpose



No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










No

ID(YYNN)


Num of Simulation

1 0215 루사(RUSA)20020823 0900 ~ 20020901 1500


2 0314 매미(MAEMI)20030906 1500 ~ 20030914 0600


3 0415 메기(MEGI)20040816 1500 ~ 20040820 1800


4 0603 에위니아(EWINIAR)20060701 0300 ~ 20060710 2200


5 1004 뎬무(DIANMU)20100808 2100 ~ 20100812 1500


6 1214 덴빈(TEMBIN)20120819 0900 ~ 20120831 0000


7 1215 볼라벤(BOLAVEN)20120820 1500 ~ 20120829 0600


8 1216 산바(SANBA)20120911 0900 ~ 20120918 0900


9 1509 찬홈(CHAN-HOM)20150630 2100 ~ 20150713 0600


10 1618 차바(CHABA)20160928 0300 ~ 20161002 1500


Sum 212

22

Data










tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion

















tracking



23

Data







bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion














bull Data split





24

Data


WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









WRF data

Conv2DLSTM

h0 Conv2DLSTM

Conv2D LSTM

helliphellip

_X _X

_119867

_119894119904119905119886119905119890

_119871119878119879119872_119874

0 1

_119871119878119879119872_119874 _119871119878119879119872_119874

_119871119878119879119872_119878 _119871119878119879119872_119878 _119871119878119879119872_119878

_119867 _119867

_X4


0000 0600 2400


(lat0 long0)

_O0 _O1 _O4


Time

119896119890119903119899119890119897 = 33119891119894119897119905119890119903119904 12

119904ℎ119886119901119890 = (239279)119888ℎ119886119899119899119890119897 = 89

25

Model



bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









bull Learning

0

5

10

15

20

25

30

35

40

45

50

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91

RM

SE

Epoch

26

Model RMSE


Result


K40m for 100 epoch

bull Test Result


Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









Outline





bull Conclusion



Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Observation Data

COMS-1MI (KMA)




HIMAWARI-8AHI (JMA)




1 09999 279748 1259390 972 N

2 09361 133122 837975 1013 NS

3 0 0 0 0 X

4 0 0 0 0 X

5 0 0 0 0 X

6 0 0 0 0 X

Prediction Results


GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion









GlobeNet Data







Typhoon TrackCOMS-1





Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion












Conv2D based Model





GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion











GlobeNet Test Error

Distance(km)


30-65km




bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion











bull COMS-1








Step)

LatentVector



Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Convolution Step




Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Deconvolution Step


Prediction



Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Input Images

Output Images

Conv-Deconv Model




Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Output Images



Output Images

Input Images













Outline





bull Conclusion


Conclusion










Output Images

Input Images













Outline





bull Conclusion


Conclusion




















Outline





bull Conclusion


Conclusion









Conclusion








how deep learning could predict weather events

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