1 / 18

Hsin -Hung Lin, Pay-Liam Lin National Central University, Taiwan

Effect of Doppler radial Velocity data assimilation on the simulation of a typhoon approaching Taiwan. Hsin -Hung Lin, Pay-Liam Lin National Central University, Taiwan. Wu and Kuo (1999) pointed out that the major problems of typhoon prediction in Taiwan are caused by:

meg
Download Presentation

Hsin -Hung Lin, Pay-Liam Lin National Central University, Taiwan

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Effect of Doppler radial Velocity data assimilation on the simulation of a typhoon approaching Taiwan Hsin-Hung Lin, Pay-Liam Lin National Central University, Taiwan

  2. Wu and Kuo (1999) pointed out that the major problems of typhoon prediction in Taiwan are caused by: 1) inadequate observations 2) insufficient model resolution 3) the complicated influence of the CMR on airflow Mesoscale precipitation patterns induced by typhoon circulation over complicated terrain are difficult to predict. Radar wind data assimilations are used to study possible improvements to this problem. In this study, the potential improvement of short-term typhoon predictions near Taiwan, particularly the related rainfall forecasts over the mountainous island, using Doppler radial wind observations is explored.

  3. Typhoon Case • 2004/8/24-25 Typhoon Aere • 15-hr maximum accumulated rainfall is 760 mm

  4. Experiment Design • MM5 and 3D-VAR • 3 Nesting Domain • model resolutions are 45, 15 and 5 km • Assimilate Doppler Radial Velocity and GTS data

  5. Radar Data Number Radar Data Number at each Height Radar Site at 766meter height 5 assimilation cycles 4kmradar data resolution Max. 3000-3500data points in each cycles fewer data Below 3 km First 2 cycles Last 3 cycles

  6. Typhoon Track The typhoon movement was deflected toward the south during the second assimilation cycle When Typhoon moved closer to Taiwan, the simulated typhoon positions closer to the observed track. CTL&GTS DRV&GAD CTL&GTS

  7. Typhoon Intensity CTL&GTS the DRV, GAD and RV3 produced relatively better minimum SLP, implying a positive influence of the radar data assimilation on typhoon intensity. In comparison with the CTL, the error of the simulated central SLP was reduced by about 25% DRV , GAD, RV3 25.5 hPa

  8. Typhoon Aere Doppler wind data assimilation made the better simulation results of eye wall and rainband structure 2004/08/25 0300 UTC CTL GTS GAD DRV RV3

  9. Typhoon Aere TS and BIAS for 3-hr Rainfall at the last assimilation cycle Doppler wind data assimilation led the better rainfall simulation and improve the under-prediction above 5 mm threshold.

  10. Sensitivity Test In order to clarify the individual impact of different observation. Follow GAD, and assimilate single kind of observation at the last cycle. Dual-Radar retrieval wind was added in addition.

  11. Sensitivity Test Radar Data Number at each Height Doppler Wind Dual-radar wind coverage is smaller than Doppler radial wind and lack data above 6 km 3km radar coverage Dual-radar wind coverage Dual-Radar wind 2km Dual-Rada wind 4km

  12. Sensitivity Test Increment of Horizontal Wind of Assimilation Analysis at 700 hPa SEN-SFC SEN-SND Red: positive increment Blue: negative increment SEN-DRV and SEN-DUR had stronger cyclonic positive increment SEN-DRV SEN-DUR

  13. Sensitivity Test The cross section of u-wind Increment of Analysis at latitude 24.6 Dual-Radar wind increase the low level wind, but decrease high level wind. SEN-SFC SEN-SND Red: positive increment Blue: negative increment SEN-DRV SEN-DUR A

  14. Sensitivity Test The typhoon center cross section of u-wind Increment SEN-DRV had more symmetric wind structure than SEN-DUR. SEN-SFC SEN-SND Red: positive increment Blue: negative increment SEN-DRV SEN-DUR C

  15. Sensitivity Test Compare with Sounding (46715) at the north Taiwan Low Level Wind Speed SEN-DUR had the best analysis wind speed SEN-DRV is secondary SEN-SND SEN-DRV SEN-DUR

  16. Sensitivity Test Simulation Typhoon intensity Minimum Sea Level Pressure Doppler wind data assimilation descended3 hpa Dual-Radar wind data assimilation descended 2 hPa SEN-SFC SEN-SND SEN-DUR SEN-DRV

  17. Summary • The model predicted fields including sea level pressure, wind and precipitation were improved with the additional observed wind information through Doppler radial velocity assimilation. • When the typhoon center moved closer to Taiwan and the whole circulation of the core region could be observed, the typhoon tracks were predicted more correctly with radar data assimilation • The typhoon intensity also was increased and revised about 25 % errors from non-assimilation simulation. • The effects of dual-radar retrieval wind leads the best low level wind speed of typhoon over the land by the analysis of data assimilation. The radar radial wind has the more symmetrical adjustment of wind structures than the dual-radar wind.

  18. Thank You

More Related