Customer Sharing: Weather Risk - Weather on the Cloud

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What does Weather Risk do? Obstacles Weather Risk face IT Infrastructure Redesign Choosing the right cloud services Development rocess Cloud infrastructure and usage Before vs After Conclusion

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© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Marvin Lee, WeatherRisk
2016.05.20
Weather on the Cloud
雲端上的天氣

大綱
• 氣象公司做什麼？
• 氣象公司的核心
• 發展的困境
• 選擇雲端服務
• 雲端架構開發與使用經驗
• 雲端化的效益
• 心得分享

博士
5%
碩士
36%
學士
59%
32 Avg. Age
53% Meteorologist

1 million USD Capital
3million USD Turnover

Synoptic Scale: 27km~45km
Mesoscale: 1~27km
NCEP
CWB
JWA
EPA
CMA
WRF
MM5
CMAQ
TAQM
HYSPLIT
日常預報
海象預報
劇烈天氣警報
氣候預報
資料模式預報
判斷
校驗
加值
專家
視覺化
客製化
服務

新的資訊架構除了需解決上述困境，仍需：
• 提升開發速度
• 擴大服務範疇
• 隨公司發展擴充各項資源

心得
• 選擇正確且合適的雲端服務並不容易
• 最終影響決定的是彈性、穩定度與服務的完整性
• 各家雲端服務商或有優劣，AWS始終遙遙領先。
• 服務代理商的支援與訓練

Satellite
Subnet 5
Image Manipulation
Subnet 7
Data Insertion
Subnet 8
API
Subnet 4
NoSQL DB x3
Subnet 6
Internal Application
Subnet 3
Web Application
Subnet 2
Official Website
Subnet 1
Region
Availability Zone
LambdasAmazon SQS Data
Cloud Front
Amazon SQS Image
Amazon S3Amazon Glacier

• 每10分鐘透過網路從日本接收超過2GB原始衛星資料，等待處理成衛星雲圖
• 因為網路頻寬與延遲，在短短10分鐘須接收下來並處理完成  不可能
• 每6小時透過網路，從美國接收20GB的全球氣象場資料，等待處理成為預報
• 因為網路延遲與限流，每6小時的資料需花費超過12小時下載  沒路用
• 隨時透過網路，從全球各地、中央氣象局與自行架設的偵測儀器(IOT)接收觀
測資料
• 花費許多機器資源介接資料(爬蟲)，成本高、穩定度差又難以管理  做不到

不可能的任務1
Satellite
Subnet 5
Image
Manipulation
Subnet 7
1
日本JWA
2
3
45
Amazon
Glacier
Amazon S3
Amazon SQS
Image

不可能的任務2
Amazon
S3
Lambda Lambda Lambda Lambda Lambda …
美國NOAA

不可能的任務3
Data
Insertion
Subnet 8
NoSQL DB x3
Subnet 6
Lambdas
Amazon SQS
Data
12
3
Amazon
S3

壓力
• 既有服務仍須持續運作
• 資訊或其他部門
• 開發人員不足
• 時間、成本

成功導入的關鍵
• 資訊蒐集與想像力
• 好的雲端服務，好的代理商支援
• 試算再試算
• 溝通：管理高層願意支持，團隊一心
• 決心與耐心
• 盡早開始，掌握時間

Thank you!
www.weatherrisk.com
marvin@weatherrisk.com

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Customer Sharing: Weather Risk - Weather on the Cloud

2. 大綱 • 氣象公司做什麼？ • 氣象公司的核心 • 發展的困境 • 選擇雲端服務 • 雲端架構開發與使用經驗 • 雲端化的效益 • 心得分享

3. 世界各國都有著許多大大小小的氣象公司，提供各類不同的氣象服務…

4. 交通運輸公關公司婚紗拍攝出遊建築工程縣市政府

11. 博士 5% 碩士 36% 學士 59% 32 Avg. Age 53% Meteorologist

12.

13. 1 million USD Capital 3million USD Turnover

14. 如何走到這一步？

15. Synoptic Scale: 27km~45km Mesoscale: 1~27km NCEP CWB JWA EPA CMA WRF MM5 CMAQ TAQM HYSPLIT 日常預報海象預報劇烈天氣警報氣候預報資料模式預報判斷校驗加值專家視覺化客製化服務

16. 但是Happily Ever After只是童話…

17. 受限的網路龐大的資料稀少的空間滿載的機櫃僵化的架構有限的成本

18. 新的資訊架構除了需解決上述困境，仍需： • 提升開發速度 • 擴大服務範疇 • 隨公司發展擴充各項資源

19. 選擇雲端服務

20. 選擇雲端服務 Mx Lx Gx Rx

21. 心得 • 選擇正確且合適的雲端服務並不容易 • 最終影響決定的是彈性、穩定度與服務的完整性 • 各家雲端服務商或有優劣，AWS始終遙遙領先。 • 服務代理商的支援與訓練

22.

23. Satellite Subnet 5 Image Manipulation Subnet 7 Data Insertion Subnet 8 API Subnet 4 NoSQL DB x3 Subnet 6 Internal Application Subnet 3 Web Application Subnet 2 Official Website Subnet 1 Region Availability Zone LambdasAmazon SQS Data Cloud Front Amazon SQS Image Amazon S3Amazon Glacier

24. • 每10分鐘透過網路從日本接收超過2GB原始衛星資料，等待處理成衛星雲圖 • 因為網路頻寬與延遲，在短短10分鐘須接收下來並處理完成  不可能 • 每6小時透過網路，從美國接收20GB的全球氣象場資料，等待處理成為預報 • 因為網路延遲與限流，每6小時的資料需花費超過12小時下載  沒路用 • 隨時透過網路，從全球各地、中央氣象局與自行架設的偵測儀器(IOT)接收觀測資料 • 花費許多機器資源介接資料(爬蟲)，成本高、穩定度差又難以管理  做不到

25. 不可能的任務1 Satellite Subnet 5 Image Manipulation Subnet 7 1 日本JWA 2 3 45 Amazon Glacier Amazon S3 Amazon SQS Image

26. 不可能的任務2 Amazon S3 Lambda Lambda Lambda Lambda Lambda … 美國NOAA

27. 不可能的任務3 Data Insertion Subnet 8 NoSQL DB x3 Subnet 6 Lambdas Amazon SQS Data 12 3 Amazon S3

28. 壓力 • 既有服務仍須持續運作 • 資訊或其他部門 • 開發人員不足 • 時間、成本

29. 成功導入的關鍵 • 資訊蒐集與想像力 • 好的雲端服務，好的代理商支援 • 試算再試算 • 溝通：管理高層願意支持，團隊一心 • 決心與耐心 • 盡早開始，掌握時間

30. To be continue…

31. Thank you! www.weatherrisk.com marvin@weatherrisk.com