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Impact of GPS Radio Occultation Data on Typhoon Prediction

Impact of GPS Radio Occultation Data on Typhoon Prediction. Bill Kuo, UCAR Ching-Yuang Huang, NCU Jing-Shan Hong, CWB. Impact of COSMIC on Hurricane Ernesto (2006) Forecast. With COSMIC. Without COSMIC. Impact of COSMIC on Hurricane Ernesto (2006) Forecast. With COSMIC. GOES Image.

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Impact of GPS Radio Occultation Data on Typhoon Prediction

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  1. Impact of GPS Radio Occultation Data on Typhoon Prediction Bill Kuo, UCAR Ching-Yuang Huang, NCU Jing-Shan Hong, CWB

  2. Impact of COSMIC on HurricaneErnesto (2006) Forecast With COSMIC Without COSMIC

  3. Impact of COSMIC on HurricaneErnesto (2006) Forecast With COSMIC GOES Image GOES Image from Tim Schmitt, SSEC

  4. Verification of WRF/DART analysis by about 100 dropsondes during the Ernesto genesis stage. See Hui Liu’s presentation later this morning, at 11:40 a.m.

  5. Super Typhoon Jangmi (2008)

  6. 4-km WRF forecast experiments for the genesis of Typhoon Jangmi • Start model at T = 3, 2.5, 2, and 1.5 days before the genesis • Use NCEP FNL as well as ECMWF I.C. • Mix FNL and ECMWF initial conditions by swapping moisture fields • FNL u.v. T + ECMWF q • FNL q + ECMWF u, v, T

  7. FNL initial conditions T = -3 d T = -2.5 d T = -2 d T = -1.5 d

  8. ECMWF initial conditions T = -3 d T = -2.5 d T = -2 d T = -1.5 d

  9. 2-day forecast FNL u, v, T, q ECMWF u, v, T, q FNL – u, v, T EC -- q FNL - q EC – u, v, T

  10. Evaluating the impact of COSMIC GPS RO data on the prediction of Typhoon Morakot, using the CWB 45-km operational WRF system (OP2) and the another system with digital filter, from 080318 – 080918 (a week long period). Red: No GPS, Green: With GPS OP2 run DFI

  11. GPS RO is shown to have an impact on typhoon track prediction, particularly, after 36-h. The impact is even more robust with the use of digital filter initialization. OP2 run DFI diff-=CTL-GPS Positive means improvement from assimilating GPSRO

  12. 24h forecast of 24-h accumulated rainfall (Aug 7-8) Ensemble mean Observed GPS NOGPS

  13. Rain Probability Forecast (August 7-8 00Z) Ensemble mean Observed

  14. c. 12/8-15/8 August (model simulation time 60-h~63-h) ---for EN0600 The spatial distribution of the simulated 3-h rainfall rate (colored shade, unit: mm) at 12/8-15/8 August. The associated sea level pressure (blue contours, unit: hPa) at the end of this period is superimposed. ---EN0600

  15. a. The rainfall probability forecast (a) D2 500 mm (b) D2 1000 mm (c) D3 500 mm (d) D3 1000 mm D2 00/7-00/8 D3 00/8-00/9 (e) 2Ds 1000 mm (f) 2Ds 1500 mm (g) 4Ds 1500 mm (h) 4Ds 2500 mm 2Ds 00/7-00/9 4Ds 00/6-00/10 The rainfall probability distribution (%) exceeding the thresholds of (a) 500, (b) 1000 mm for 24-h rainfall ending at 0000 UTC 8 August; (c) 500, (d) 1000 mm for 24-h rainfall ending at 0000 UTC 9 August; (e) 1000, (f) 1500 mm for 48-h rainfall ending at 0000 UTC 9 August; (g) 1500, (h) 2500 mm for 96-h rainfall ending at 0000 UTC 10 August estimated from the 32 members of EN0600. The observed rainfall at the corresponding threshold is superimposed by the blue line.

  16. Taiwan GPS-ARC Team Works on GPS RO Data Impact on Typhoon Prediction Paper Lists: Chen, S.-Y., C.-Y. Huang, Y.-H. Kuo, Y.-R. Guo, and S. Sokolovskiy, 2009: Assimilation of GPS refractivity from FORMOSAT-3/COSMIC using a nonlocal operator with WRF 3DVAR and its impact on the pre diction of a typhoon event. Terr. Atmos. Ocean. Sci., 20, 133154. Kueh, M.-T., C.-Y. Huang, S.-Y. Chen, S.-H. Chen and C.-J. Wang, 2009: Impact of GPS radio occultation soundings on prediction of Typhoon Bilis (2006) landfalling Taiwan. Terr. Atmos. Oceanic Sci., 20, 115-131. Huang, C.-Y., Y.-H. Kuo, S.-Y. Chen, C.-T. Terng, F.-C. Chien, P.-L. Lin, M.-T. Kueh, S.-H. Chen, M.-J. Yang, C.-J. Wang, A. S. K. A. V. Prasad Rao, 2010: Impact of GPS radio occultation data assimilation on regional weather predictions. GPS Solutions, 14, 35-49. Chen, S.-Y., C.-Y. Huang, Y.-H. Kuo, and S. Sokolovskiy, 2010: Observational Error Estimation of FORMOSAT-3/COSMIC GPS Radio Occultation Data. Mon. Wea. Rev., accepted.

  17. 莫拉克颱風(Morakot) - B- CWB Best Track - N- None - 1- GPS1 - 2- GPS2 - 3- GPS3 * 3 days Mean Track Error : including the track error at the initial time

  18. Data Density for COSMIC and COSMIC-II Options: A, B, C, and D COSMIC - 6 x 72o COSMIC-IIA - 8 x 72o + 4 x 24o COSMIC-IIB - 12 x 72o COSMIC-IIC - 6 x 72o + 6 x 24o COSMIC-IIB - 4 x 72o + 8 x 24o

  19. 3 hour 1 hour COSMIC-II SoundingsGeographic Coverage(6@72°, 6@24°) 24 hour 6 hour

  20. Summary • Analysis and prediction of tropical cyclogenesis is highly sensitive to moisture distributions over the tropics. • GPS RO data provide valuable information on moisture that are important to tropical cyclone prediction. • The assimilation methods and strategies have significant influence on the impacts of GPS RO data on tropical cyclogenesis. Ensemble based data assimilation system performs better than 3D-Var. True for both global and regional weather prediction systems (e.g., WRF and NCEP GFS). • Use of cloud-scale ensemble system, coupled with advanced data assimilation system can be very valuable for the prediction of disastrous event such as Morakot. GPS RO data help improve the precipitation forecast for this case.

  21. Outlook • Current COSMIC sounding distribution is NOT optimal for tropical prediction. We have the lowest data density over the tropics. • Future mission (e.g., COSMIC-II) will substantially increase the data density over the tropics (factor of 10). • UCAR, under sponsorship of NSF, will be conducting an OSSE study to examine the impact of COSMIC-II on Typhoon Morakot.

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