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Objective method to determine the Typhoon intensity using IR1 images

Objective method to determine the Typhoon intensity using IR1 images. 20/03/2007 NESDIS Japan Meteorological Agency Koji Kato. IR1. 20.5 [N] 135.0[E]. Today’s Topic. Preliminary objective approach to determine the typhoon intensity.

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Objective method to determine the Typhoon intensity using IR1 images

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  1. Objective method to determine the Typhoon intensityusing IR1 images 20/03/2007 NESDIS Japan Meteorological Agency Koji Kato

  2. IR1 20.5 [N] 135.0[E] Today’s Topic • Preliminary objective approach to determine the typhoon intensity. • Input the position of typhoons and IR1 image of typhoon then get the typhoon intensity automatically! 960[hPa] MAX103[kt] Analysis Program

  3. Example Comparison between and manual result 2004 TC0024(Japan)

  4. Procedure • Brief introduction of conventional method • Introduction of objective hurricane analysis method.

  5. What is Dvorak Technique? • Tropical storm analysis method using IR (11μm) image of meteorological satellites. • (*Especially minimum pressure and maximum wind velocity) • At the JMA, we have no operational objective method for Typhoon analysis.

  6. 階調コード（ＢＤコード） >.20 Spiral .40 to .55 .60 to .75 .80 to 1.0 < 1¼’ from <3/4’ from <1/2’ from <1/3’ from “DG” “DG” “DG” “DG” 開始 “CSCの決定” 全ての湾曲した雲列または雲バンドにより ”CSC”を決定する。初期の発達（TI）に対しては、Step1A（本文）を参照 【眼の明瞭さによる調整】 １）大きな眼あるいは細長い眼に対しては、対角線の右側の値のみを使う。 ２）E数が4.5以上である細長い眼に対しては、他に惹かれていなければ0.5を引く 1 雲パターンによる解析 （不可能ならStep3も考慮する。） 雲パターンが2A～2Eに適合しない場合、 Step3～6を先に実施し、指示」された場合Step2に戻る 2 測定階調「W」のときはDT数に0.5を加える。スパイラル長が1.0以上のときは「2C（バンド状眼）」で解析する 　　　　　　　　　　　　　　　　　　　眼階調　　　　　　　　　　　　　⇒　冷 眼を一周する最も冷たいリングの階調 BANDパターン (10°logスパイラルを適合して 弧の長さを測定) 2A SHEARパターン (CSC位置と濃密な雲域まで の距離を測定) 2B DT1.5±0.5 DT2.5 DT3.0 DT3.5 Step4へ 眼調整(Eadj) YES 24時間前のＴ数 ≧2.0 EYEパターン (バンド状眼パターンのときは 平均バンド幅を測定) 2C CF数決定 CF=E+Eadj E6.5 E6.0 E5.5 E5.0 E4.5 E4.5 E4.0 NO Step2A or 4へ BF調整（BF） 12時間前のＴ数 ≧3.5 EMBEDパターン (CSCの埋没距離が0.4°以上) YES 2D DT数決定 DT=CF+BF CF5.0 CF5.0CF4.5CF4.0CF4.0CF3.5 NO Step2A or 4へ Step4へ

  7. EIR How to do? Part1 IR Special Coloring Determine the pattern TY 05 2005/07/15日12UTC TY 05 2005/07/15 12 UTC

  8. >.20 Spiral .40 to .55 .60 to .75 .80 to 1.0 How to do? Part2 Tropical Number = 3.5

  9. How to do? Part3

  10. Weak point of Dvorak Technique 1. Depend on forecaster’s skill. 2. Improvement of satellite sensor doesn’t contribute the accuracy. 3. Get intensities in 6 hours interval.

  11. Pattern determination EYE Patter or Band Pattern ?

  12. Weak point of Dvorak Technique 1. Depend on forecaster’s skill. 2. Improvement of satellite sensor doesn’t contribute the accuracy. 3. Get intensities in 6 hours interval. Why?

  13. Can we see the difference?

  14. How to overcome the problems?

  15. Objective Hurricane Analysis method • ODT (Objective Dvorak Technique) (Velden, Zehr 1998) • AODT (Advanced ODT) (Olander, Velden 2002-present)

  16. What is AODT? • National Hurricane Center and Wisconsin university group developed AODT. Only one objective method to determine the hurricane intensities. • Input the center position of the hurricane and the IR(10 μm) image, then output the hurricane intensity. • Algorithm is not open! • Output the intensity every hour!

  17. AODT The Advanced Objective Dvorak Technique

  18. I want an objective method!I am envious of them.But I can’t get the AODT……..….!!Make by myself!

  19. My method 1．Use IR picture and center position of typhoon. 2. Divide typhoon area into concentric circles. 3. Classify the typhoon using maximum temperature, minimum ring temperature and variance. Tropical Number

  20. Procudure1 Divide into rings (0.05°width) to 1.5°radius. And find the minimum temperature ring and calculate the variance of temperatures. TCenter TRing

  21. Procedure2 • From the temperature distribution classify the typhoon automatically

  22. Procedure3

  23. Procedure4 Ultra EYE Active Cb Low Cloud Weak EYE Tropical Number Flat EYE Determine the Tropical number using tendency and previous Tropical number. unknown

  24. Example Ultra EYE Low Cloud Active Cb

  25. Ultra EYE Warm area in the center area Smooth ring (low variance) Very low temperature Ultra EYE

  26. Active Cb Very low temperature around the center Temperature distribution is rough spatially-temporally Totally low temperature Active Cb

  27. Low cloud High temperature around the center Asymmetric shape High temperature area Low Cloud

  28. Condition 1 Ultra EYE • TBBMAX-TBBMIN>=30[K] • σTBBMIN<=8[K] • TBBMAX>=240[K] • TBBMIN<=220[K]

  29. Condition 2 Active Cb -α • TBBMIN<230[K] • TBBMAX<=8[K] • σTBBMIN>20[K] Or Average6(TBBMIN)>16[K]

  30. Condition 2.1 Active Cb -β • TBBMAX-TBBMIN< 7[K] • σTBB1.5Ring≧20[K]

  31. Condition 2.3 Active Cb -γ • TBBMIN<230[K] • Ave3(σTBB0.25)-AvePre3(σTBB0.25)

  32. Condition 3.1 Low Cloud - α • TBBMAX≧260[K] • TBBMIN≧240[K]

  33. Condition 3.2 Low Cloud – β • TBB0.25≧230[K] • TBBMAX≧260[K] • σTBB0.25≧15[K]

  34. Condition 4 Weak EYE • TBBMAX-TBBMIN>=15[K] • σTBBMIN<=10[K] • TBBMIN<=230[K]

  35. Condition 5 Flat EYE • TBBMAX-TBBMIN≦5[K] • Ave3(σTBBMIN)<=8[K] • TBBMIN<=210[K]

  36. Data • Period 2004 TC0004-TC0040 • Satellite IR1(GOES-9) • Using regression analysis to determine the T-number Data Resolution 0.05° 10bit

  37. Is this method is valid? • Introduce 5 examples. TC0007 TC0009 TC0013 TC0021 TC0024

  38. TC0007

  39. TC0009

  40. TC0013 Expect the typhoon to become more stronger!

  41. TC0021 Human error

  42. TC0024

  43. Features • In T≧4.5 (under 979[hPa]), comparable to human • In T<4.0 (over 987[hPa]), Auto classification doesn’t work well.

  44. Further Study • Improve the recognition ability especially in the developing and decaying stage • Reduce biases between satellites

  45. TC0517 Different Satellite! (MTSAT-1R) Bad Result!

  46. Thank you for your attention! Sorry, I stopped developing these tools. But I’d like to try again someday! Thank you for your attention!

  47. Fin

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