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PM2.5 Interpolation and Visualization
based on Wind Fields
ked
2016/05/15
2016/06/13 update
Our system
 PM2.5
 http://www.cmlab.csie.ntu.edu.tw/~ked/pm25.htm
 PM2.5 + radar
 http://www.cmlab.csie.ntu.edu.tw/~ked/pm25_4.ht
m
Why a different PM2.5 vis. system
2016/04/12
The visualization of
 PSI result
2016/04/12
The visualization of
2016/04/20
The visualization of
 PSI result
 Averaged index
2016/04/12
The visualization of
 PSI result
 Averaged index
 Sparse distribution
2016/04/12
Other systems for PM2.5
http://env.g0v.tw/air/
2016/04/15
Other systems for PM2.5
http://env.g0v.tw/air/ http://env.childgrowth.info/air/
2016/04/15
Other systems for PM2.5
http://env.childgrowth.info/air/
天下雜誌 2015.03.17
How to interpolate the sparse data
Wind & PM2.5
天下雜誌 2015.03.17
Wind & PM2.5
東森新聞 2016.01.01
Our system
2016/04/15
Our system
2016/04/22
Outline
 System description
 Data capture
 Interpolation
 Rendering
 Conclusions
Outline
 System description
 Data capture
 Interpolation
 Rendering
 Conclusions
Features
Interpolation based on winds
x 3D interaction
x Auto-renewal
Architecture
Our server
環保署
NCEP
Capture PM 2.5
Interpolate
Capture wind field & height field
Architecture
Web browserOur server
環保署
NCEP
Capture PM 2.5
Interpolate
Get data
Visualize &
update
Capture wind field & height field
Outline
 System description
 Data capture
 Interpolation
 Rendering
 Conclusions
PM2.5 data
Location ?
PM2.5 data + coordinate
Data of NCEP
.grib2 format – records
How to read .grib2 file
 Refer to https://www.google.com.tw/url?
sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjCvc7I2Y3MAhVKGJQKHVC4AxgQFgglMAA&url=http%3A%2F%2Fjou-p3.as.ntu.edu.tw
%2FP3%2Ffile%2Fteaching%2Ff1386923093.pdf&usg=AFQjCNF0Z5dLY08WxC5pl_0BUxfTvxUvuQ&sig2=eLDe7X0wv0Ol1KG_Dnr9YQ
.grib2 format – regular data
 https://www.google.com.tw/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjCvc7I2Y3MAhVKGJQKHVC4AxgQFgglMAA&url=http%3A
%2F%2Fjou-p3.as.ntu.edu.tw%2FP3%2Ffile%2Fteaching%2Ff1386923093.pdf&usg=AFQjCNF0Z5dLY08WxC5pl_0BUxfTvxUvuQ&sig2=eLDe7X0wv0Ol1KG_Dnr9YQ
Outline
 System description
 Data capture
 Interpolation
 Rendering
 Conclusions
Vector field
Streamline
Streamlines
Flow in the field
Flow in the field
Transition
1/3
5/9
1/9
Transition – intensity decay
1/3 x 1/2
5/9 x 1/2
1/9 x 1/2
1/2
Transition matrix
 Refers to the stochastic process in which the
transition probabilities depend only on the
current state
1/3 x 1/2
5/9 x 1/2
1/9 x 1/2
1/2
 Irreducible, aperiodic, recurrent
Stationary Markov chain
Stationary Markov chain
Outline
 System description
 Data capture
 Interpolation
 Rendering
 Conclusions
Implementation
 Javascript + webgl
Rendering
Taiwan
PM2.5
Wind vector
Scientific visualization
 "it’s infovis when the spatial representation is chosen,
and it’s scivis when the spatial representation is given“
-- wikipedia
Volume rendering
Volume rendering
Billboard
http://www.markmark.net/clouds/
Outline
 System description
 Data capture
 Interpolation
 Rendering
 Conclusions
Case – 2016.03.14
 " 在臺灣西南側一條繩索狀的
白雲,那是南海吹過來微弱西
南風和東側高壓迴流的交界面
,因為大氣穩定,所以只在海
面上形成一長條的索狀雲。 "
Case – 2016.04.08
Thx.
站在推廣環保和教育的立場,
我從電腦圖學的角度出發,做了一個互動系統,
希望民眾可以重視 PM2.5 的問題,
更希望的是,
我們的下一代,可以因為我們的覺醒,
而呼吸到更好的空氣。
Update
 Streamlines for wind field
 Weather radar for rainfall
 Anaglyph 3D
Streamline placement
 Refer to
Weather radar data
 Download from
Thx.

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PM2.5 visualization

Editor's Notes

  1. 大家好,我是蘇彥禎, 我要報告的題目是“空污資料搭配風場的內插與視覺化”, 我會介紹我的pm2.5視覺化系統, 這個系統會定時地去擷取pm2.5和風場的資料, 並以風場對pm2.5的取樣結果做內插。
  2. 先說明一下, 中央氣象局也有它的空污視覺化系統, 可是這個系統有幾個問題。
  3. 首先,他視覺化的對象是多項監測數據的綜合指標, 稱之為PSI。
  4. PSI是1989年制訂的, 那時候甚至沒有考慮到PM2.5這個因素, 除此之外,PSI還存在著一些問題, 有不少人呼籲政府應該重新制定一個空氣污染的指標。
  5. 而氣象局提供的數值,也讓人有一些意見, 如果以12個小時的移動平均來呈現數值, 可能會有模糊和淡化問題的疑慮。
  6. 最後一點就是,觀測點的分布相對於整個台灣實在太稀疏了, 如果可以根據物理基礎來做內插, 其實是一件滿有意義的事。
  7. 因為以上提到的諸多問題, 所以民間有開發自己的pm2.5視覺化系統, 舉例來說g0v就做得不錯。
  8. 也有人考慮不同的內插方式, 然後嘗試把風向的資訊加入視覺化呈現。
  9. 關於內插的問題,最明顯的例子是南投, 它的觀測點很少, 但是每年2, 3月份的空氣,卻是出乎意料之外的差。
  10. 所以這是一個滿有趣的問題, 對於空污的量測資料,可以怎麼做內插。
  11. 我們先來看幾篇報導, 首先是林明德教授提到地形還有風速等因素。
  12. 接著是氣象預報中心的鄭明典主任, 也提到風向對空氣品質的影響。
  13. 受到這些的啟發, 我們的系統就是根據預測的風向與大小, 來對pm2.5的觀測資料做內插。
  14. 這是在沒有觀測點的地方內插出資料的例子, 新北山區,如平溪和雙溪等地方,完全沒有觀測點, 但是我們根據風場,可以預估這些地方的數值。
  15. 以下是我的outline, 首先我會簡介整個系統的架構; 接著說明一下資料擷取的重點; 然後解釋資料內插和繪製的方法; 最後則是結論。
  16. 如同前面所提到的, 我們的系統有3個特色, 根據風場做內插; 3D互動的功能; 和它會自動更新資料。
  17. 系統架構是這樣設計的, 系統是運行在一個server上, 這個server會定時地去環保署和NCEP下載pm2.5和風場的資料, server會根據風場對pm2.5做內插,然後把結果存起來。 National center for environmental prediction
  18. 使用者要瀏覽資料則是透過browser把資料抓下來, 視覺化與人機互動是在本機端處理, 本機端的程式並且會定時地去更新資料。
  19. 資料的擷取有些地方要注意。
  20. 首先是pm2.5的觀測資料並沒有提供觀測站的位置。
  21. 因此要另外下載一份測站的基本資料, 然後透過測站名稱去連結觀測站的經緯座標。
  22. 另外就是風場資料,我是從ncep下載的, 台灣沒有提供解析度夠高的風場資料,有它的諷刺性存在, 我之前能找到最高的解析度是13公里一個格點, 雖然解析度不夠高,但是勉強可以接受, 我必須強調,如果可以拿到更高解析度的資料, 內插的結果會更精確,也更有意義。
  23. Ncep提供的預測資料是.grib2的格式, .grib2裡面包含很多氣候相關的record, 我們從.grib2裡面拿到的是風場資料,和地理高度。
  24. 網路上有一些教學文件在說明要怎麼讀取.grib2的資料, 我把連結放在這裡,需要的人再自行參考。
  25. .grib2是regular的儲存方式, 解析後我們可以得到,在固定的座標位置上的相關預測值, 這部分等一下會再詳細說明。
  26. 接著是資料的內插。
  27. 剛剛說.grib2提供的資料是regular data, 以風場為例,解析出來的資料會像這個樣子, 在固定的格點上,會有相對的風向和風力強度。
  28. 要表現一個風場,畫出streamline是一個常見的方式, 這條streamline相切於風場中的各個風向, 也就是說,如果風場是固定的, 那麼在風場中的particle會沿著streamline移動。
  29. 因此,多條streamline可以用來更精確地表示整個風場。
  30. 接著我們試著來分析, 從一個特定區域出發,particle的移動方式。
  31. 如果particle完全是被風吹著走的, 那particle之後會跑到哪裡, 跟它目前所在位置有關係。
  32. 所以我們可以統計出, 在一個特定區域中的particle, 移動到其他區域的機率。
  33. 在推導particle的遞移的時候, 我們會加入一個比例參數,讓particle留在原本的區域中, 也是就是說,它不會無限制地讓風吹著跑。
  34. 收集每個區域到其他區域的傳遞機率, 我們可以得到一個transition matrix, 這個矩陣可以描述,整個風場中particle的傳遞。
  35. 我們用Markov chain來解在這個風場中,pm2.5的分布結果, 要使用Markov chain,transition matrix還必須符合以上所列的3個條件, 所以還要做一些調整,使得要內插的區域形成一個封閉系統, 讓particle一直在這個系統中移動。
  36. 這樣,對transition matrix連乘,就可以得到particle的分布結果。
  37. 接著簡單介紹一下rendering的概念。
  38. 最近幾年javascript在效能還有擴充度上有了長足的進步, 另外又有webgl這項新技術的開發, 完全擴展了電腦圖學的應用層面。
  39. 我們的系統目前繪製了3樣東西, 包括台灣島,pm2.5,和風場。
  40. 我們所做的事情,就是電腦圖學中的一個領域, 叫做scientific visualization, SciVis跟最近很紅的infoVis有甚麼不同呢, 硬要分的話,可以說sciVis跟空間有比較直接的關係。
  41. 要怎麼繪製一個3維空間的資料分布呢, 這個技術稱為volume rendering, 我們可以從畫面的每個像素去回推資料對這個像素的貢獻, 然後計算出綜合的結果。
  42. 也可透過一些運算,直接把資料投射到畫面上, 我們採用的技術屬於後者。
  43. 要投射資料,有一個有趣的方式叫billboarding, 它是根據使用者的視角,去重新計算資料的分布,以及排序方式, 我們pm2.5和風場的繪製,都是使用billboarding。
  44. 接著我們來看一些觀察到的例子。
  45. 系統剛完成的時候, 有一次我在檢查風場的正確性, 卻發現東部海面的風場竟然長這個樣子, 還好後來在鄭明典的臉書看到它的相關說明。
  46. 這個例子則是呈現了pm2.5隨風移動的傳遞狀況。
  47. 站在推廣環保和教育的立場, 我從電腦圖學的角度出發,做了一個互動系統, 希望讓民眾可以重視pm2.5的問題, 更希望的是, 我們的下一代,可以因為我們的覺醒, 而呼吸到更好的空氣。