Key Laboratory of Mesoscale Severe Weather, Ministry of Education, School of Atmospheric Sciences, Nanjing University

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1 Modeling Rapid Intensification of Typhoon Saomai (2006) with the Weather Research and Forecasting Model and Sensitivity to Cloud Microphysical Parameterizations Jie Ming and Yuan Wang Key Laboratory of Mesoscale Severe Weather, Ministry of Education, School of Atmospheric Sciences, Nanjing University

2 Outlines Objectives Overview of Typhoon Saomai(2006) Experimental configuration of simulation Verify the simulation using observations. Inner core evolution and structures Summary and future work

3 Objectives Evaluated the performance of different cloud microphysical parameterization schemes To better understand the microphysical processes of different parameterization schemes and its impact on the intensification of Typhoon Saomai

4 Overview of Typhoon Saomai (2006) Track and Intensity

5 Saomai made a landfall at Cangnan of Zhejiang Province at 11 UTC 10 August. Saomai affected around 6 million people and displaced 1.7 million residents. At least 441 people were killed by the storm in China. Because of the huge losses, the name Saomai was retired from western North Pacific typhoon name list and became the specified name of the No. 8 typhoon of 2006.

6 ARW configurations D01: ; 4.5 km D02: ; 1.5km Vertical levels: 47 Top of model: 50 hpa Integration time: 1200 UTC 07 Aug to 1200 UTC 10 Aug D2 is an automatic vortexfollowing moving nest grid and starts at 0000 UTC 8 Aug. Initial condition: 20 km 20 km JMA 6 hourly gridded regional analyses

7 Experiments LIN WSM6 GODDARD THOMPSON MORRISON Microphysical parameterization schemes Lin scheme WSM 6 scheme Goddard scheme Thompson scheme Morrison scheme Features hydrometeors :Qc,Qr,Qi,Qs,Q g Exponential size distributions of rain, snow and graupel and Including ice sedimentation New method for mixed phase particle (snow and graupel) fall speed Option of choosing either graupel or hail and new saturation techniques Assume snow size distribution depend on ice and temperature and non spherical shape with a bulk density Allow more robust treatment of the particle size distribution and mixedphase process

8 Physical parameterization schemes Domain Land Longwave Shortwave PBL D01 Noah RRTM Dudhia MYJ D02 Noah RRTM Dudhia MYJ

9 Verification Simulated and best tracks

10 MSLP MWSP Minimum sea level pressure (hpa) and maximum surface wind speed (kts)

11 OBS LIN WSM6 Goddard Thompson Morrison 1 km height observed radar reflectivity (dbz) at 1602 UTC 09 August, and simulated radar reflectivity (dbz) at 1600 UTC 09 August 2006 from all five experiments

12 LIN WSM6 Goddard Thompson Morrison OBS Mean normalized vertical profiles of various hydrometeors at 2100 UTC 08 August 2006, for five experiments and observation from TRMM 2A12 product at 2244 UTC 08 August 2006.

13 LIN WSM6 Thompson Time height diagrams of various mean mass contents (first line cloud water, second line ice, g/m 3 ) from three experiments

14 LIN WSM6 Thompson Time height diagrams of various mean mass contents (first line rain water and second line precipitated ice, g/m 3 ) from three experiments

15 LIN WSM6 Thompson Time height diagrams of mean diabatic heating rate (first line, K/h) and vertical velocity (second line, m/s) from three experiments

16 LIN WSM6 Thompson Time radius Hovmöller plots of azimuthally averaged tangential wind (m/s) at 2 km altitude from three experiments

17 LIN WSM6 Thompson Time radius Hovmöller plots of azimuthally averaged radial wind (m/s) at 0.25 km altitude from three experiments

18 LIN WSM6 Thompson Mean diabatic heating profiles of various different processes at 0600 UTC 08 August 2006 for three experiments

19 Flowcharts of microphysical processes producing diabatic heating

20 graupel water Mean diabatic heating profiles from generation of (a) graupel and (b) water in experiment with LIN scheme at 0600 UTC 08 August 2006

21 ice snow graupel water Mean diabatic heating profiles from generation of (a) ice, (b) snow, (c) graupel and (d) water in experiment with WSM6 scheme at 0600 UTC 08 August 2006.

22 snow water Mean diabatic heating profiles from generation of (a) snow and (b) water in experiment with THOMPSON scheme at 0600 UTC 08 August 2006.

23 The positive feedback provides intensification of simulated storms with efficient microphysical parameterization scheme condensation (deposition) in a saturated situation stronger diabatic heating stronger upward motion plenty of supplied water vapor intensify the storm

24 SUMMARY AND FUTURE WORK The Lin and Thompson schemes have the ability to reproduce the rapid intensification and similar deepening trends with the observation. The storm with Lin scheme produces more rain water in the inner core compared to other experiments. The storm with Thompson scheme produces largest amounts of precipitated ice, but the least ice in the upper levels. The positive feedback causes the intensification of simulated storms using the efficient cloud microphysical scheme of Lin and Thompson. It results in establishing a well defined secondary circulation and induces rapid intensification of storms. Focus on the microphysical processes in the formation of convective cells in the inner core during the rapid intensification and the improvement of current cloud microphysical parameterization schemes

25 Thank you very much!

26 Symbol Cond Evap Dep Sub iacr sacw and aacw sfw sacr racs smlt and seml gacw and aacw gfr gacr gmlt and geml sdep ssub gdep gsub ihom imlt idep inu ifw Meaning Condensation Evaporation Deposition Sublimation Accretion of rain by cloud ice Accretion of cloud water by snow Bergeron processes: transfer of cloud water to snow Accretion of rain by snow Accretion of snow by rain Melting of snow to rain Accretion of cloud water by graupel Freezing of rain to form graupel Accretion of rain by graupel Melting of snow to rain Depositional growth of snow Sublimation of snow Depositional growth of graupel Sublimation of graupel Homogeneous freezing of cloud water to form could ice Melting of cloud ice to form cloud water Depositional growth and sublimation of cloud ice Nucleate ice from deposition and condensation freezing Bergeron processes: transfer of cloud water to cloud ice