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Toshiro Inoue 1) 、 Tomoo Ushio 2) , Daisuke Katagami 3) 1) Meteorological Research Institute

Applications of Split Window Data for Rainfall Estimation. Toshiro Inoue 1) 、 Tomoo Ushio 2) , Daisuke Katagami 3) 1) Meteorological Research Institute 2) Osaka University 3) Osaka Prefecture University. Cloud motion vector To construct high temporal rainfall map,

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Toshiro Inoue 1) 、 Tomoo Ushio 2) , Daisuke Katagami 3) 1) Meteorological Research Institute

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  1. Applications of Split Window Data for Rainfall Estimation Toshiro Inoue 1)、Tomoo Ushio 2), Daisuke Katagami 3) 1) Meteorological Research Institute 2) Osaka University 3) Osaka Prefecture University

  2. Cloud motion vector • To construct high temporal rainfall map, • we have to fill the observation gap from • MW onboard LEO. • 2. Convective/Stratiform rainfall type • Dominant rainfall type depends on life stage • of deep convection.

  3. Cloud Motion Vector • Filling the observation gap from the passive microwave onboard LEO becomes important issue to construct a higher temporal rainfall map. • A method to estimate half-hourly rainfall map using cloud motion vector( Joyce et al., 2004) . • Use of cloud motion vector is better than simple interpolation (Ushio et al., 2005) to fill the gap.

  4. Cloud motion vector by Split Window Better rainfall area delineation by split window Optically thicker cloud defined by the split window corresponds well to the rainfall area compared to the cloud area defined by single IR (Inoue and Aonashi, 2000; Inoue,1987) Computing the cloud motion vector from split window is expected to be better than single IR.

  5. Data and Method GOES-9 (over Japan) September, 2003 Split Window (11mm,12mm) Vector: 2D cross-correlation Template 6° lat/lon Every 3° lat/lon (Interpolated to 0.1 lat/lon) Radar AMeDAS

  6. GSMaP (Global Satellite Mapping of Precipitation) is constructing hourly rainfall map over the globe with 0.1° lat/lon. Considering current MW observation, we studied the time interpolation up to 5 hours.

  7. Radar AMeDAS image at 7 JST on 8th September, 2003 overlaid the cloud area defined as 273K or colder. Radar AMeDAS image at 7 JST on 8th September, 2003 superimposed on the cloud area defined as 273K or colder with brightness temperature difference between the split window less than 2K.

  8. Same as former slides except for the 17 JST on 5th September, 2003.

  9. Cloud Motion Vector and Rainfall Motion Vector 18UTC 5th September, 2003 u v IR Split Window Radar AMeDAS

  10. Correlation coefficients between cloud tracked rainfall rate and Radar AMeDAS on 5th September, 2003

  11. Relative correlation coefficients (Split Window/ Single IR) We studied 30 cases during September, 2003. 11 cases indicate better correlation coefficients.

  12. The use of Split Windowfor computing Cloud Motion Vector • We studied 30 rainfall cases during September 2003, and split window data indicated better score in 11 cases out of 30. • The correlation coefficients are improved by more than 50% for the 11 cases.

  13. Convective/Stratiform rainfall type Split Window can classify Ci and Cb The % of Ci within the deep convection is a good indicator to tell the life stage. Depending on the life stage, dominance of rainfall type (conv/stra) is different..

  14. Life Cycle of Deep Convection Defined by Split Window Data: Hourly data of GOES-W Period: Jan., 2001-Dec., 2001 (except Sep., 2001) Deep Convection: Cloud area colder than 253K Life Cycle: Birth: First time of DC detected End: Last time of DC detected Case: No merge No split % of Ci defined by Split Window within DC

  15. BTD Image by MSG (Ci is whiter, Cb/Cu black) IR Image by MSG

  16. 1° Cloud Type Map Classified by the Split Window GOES-W 2 hourly from 12UTC May 09, 2001

  17. % of Ci within DC for each life time category

  18. decaying Ci % within DC mature developing Life Stage of Deep Convection

  19. Comparison between % of Ci within 253K cloud area and PR/TRMM rainfall data (3G68) Rainfall Rate and % of Ci within DC % of convective rain and % of Ci within DC

  20. % of Ci within DC Good indicator the life stage of deep convection Good indicator for dominance of Convective /Stratiform rainfall type classification as a system Possible to apply the passive microwave estimation which has a tendency of overestimation at decaying stage comparing to PR estimation

  21. Thank you for your patience.

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