Tropical Cyclone Initialization with Dynamical and Physical constraints derived from Satellite data

Transcription

1 International Workshop on Rapid Change Phenomena in Tropical Cyclones Haikou China, 5 9 November 2012 Tropical Cyclone Initialization with Dynamical and Physical constraints derived from Satellite data Ma Lei Ming 1, Bao Xu Wei 1, Liu Jian Yong 2, Wang Dongliang 1 and Huang Wei 1 1. Shanghai Typhoon Institute, Shanghai, Ningbo Meteorological Observatory, Ningbo, Corresponding Author: Ma Lei Ming, malm@mail.typhoon.gov.cn Acknowledgement: Sponsored by National Typhoon 973 project of China

2 Outline Background Methods Verification on Typhoon Prediction Future work

3 umerical Prediction Error of Tropical Cyclone (TC) Track in Northwest Pacific Numerical Model Error + Initial Error Initial error

4 Early approaches for Vortex initialization Liu et al Relocation Relocation Cycle BOGUS BDA Zou et al Pu et al Zhang X. Y., B. Wang, et al , Operational use of BDA in Shanghai BDA+Cycle Wang D., Xudong Liang et al., Weather and Forecasting, 2008,

5 Uses of Satellite Data in TC initialization

6 Initialization with diabatic heating derived from satellite rainfall (Ma Leiming et al. 2007) OBS RDH1 CTRL RDH2

7 TRMM/TMI Rain Rate TRMM/TMI rain rate assimilation Adjoint of Convective Parameterization Ma et al (a ) TRMM Rain Rate (b) 1020 Typhoon Danas(2001) Intensity NO 4DVAR 990 (c) OBS CTL NB BT BNT TRMM 4DVAR

8 4DVAR of Cloud-drift Winds (Wang Dongliang, et al. 2006; Wea Forecast)

9 The use of AMSU data (Wang et al.,sti) AMSUstatis tics

10 The 3dvar of QuikSCAT data (Zeng et al. 2005)

11 However, Deficiencies in these uses of satellite data

12 No cloud drift wind in the TC core region because of obstruction of cloud

13 The Statistical and Empirical vertical heating derived from satellite might not appropriate for the rapid change of TC

14 TRMM rain rate Evolution of TC intensity TRMM OBS CTL NB BT BNT In TRMM rainfall assimilation, the bogus data is required to be used for better prediction of TC intensity OBS

15 QuikSCAT wind 3dvar The sea wind information is introduced to the model based on geostrophic approximation

16 Any approach to avoid these deficiencies? Instead of using 3dvar and geostrophic approximation based on QuikSCAT sea winds: Suggested dynamical constraint: Gradient wind balance and TC PBL rolls by using PBL model in TC initialization. Instead of using cloud drift wind and empirical heating structure derived from cloud and TBB: Suggested Physical constraint: Moisture and latent heating structure (moisture) physically related to TBB

17 Method 1: TC Vortex Initialization with Retrieved Variables (wind and pressure) from QuikSCAT (VIRV) based on consideration of Gradient wind and PBL rolls The QuikSCAT data can also be replaced by other surface datasets, e.g., ASCAT, WINDSAT, DOPPLER RADAR

18 VIRV Vs SLP Bogus The Fujita Bogus TC The TC structure derived from VIRV QuikSCAT winds SLP VS PBL model Symmetric Bogus Vortex SLP Asymmetric Vortex

19 Retrieve the SLP from sea wind based on UWPBL model and modification on roughness parameterization Sea Surface wind field Geostrophic relation Surface friction Courtesy of R. A. Brown and Patoux, University of Washington References: Patoux (2002, 2003, 2008) PBL rolls Drag coefficient for strong wind Gradient field of Surface pressure Buoy PRES OBS Ship PRES OBS Surface pressure

20 Cd decreases with strong wind speed(powell, 2 Enhances the roughness/cd parameterization

21 Ma and Tan, 2010

22 The popular 3dvar/4dvar scheme Geostrophic approximation in 3dvar Simplified Physics in 4dvar scheme Not appropriate for TC Primitive equations Initialization Prediction Initial Time of Prediction Dynamical unbalanced prediction The VIRV scheme Gradient approximation PBL rolls secondary circulation Drag coefficient Primitive equations More appropriate for TC Initialization Prediction Initial Time of Prediction Dynamical balanced prediction

23 CTRL SCAT TC Morakot (2009) Stream and intensity of wind 0712

24 The impact of VIRV on TC prediction (Max wind MWS; Sea Level Pressure MSLP) (CTRL control; REXP1 no modification of UWPBL; REXP2 modify UWPBL) 24h TC Intensity prediction improved by 20%, track by 15%

25 a b CTRL VIRV c

26 TC Morakot (2009)

27 Accumulated rainfall CTRL VIRV OBS

28 CTRL QuikSCAT wind + Radar DBZ VIRV&DBZ OBS DBZ VIRV

29 Method 2: Physical constraint TC initialization by Moisture nudging with FY2 TBB

30 TBBàMoisture à TC Convection à TC initialization Downdraft of convection TBB Updraft of convection Deep Convection Kain (2004) Environmental humidity is obviously important here, consistent with its operative role in determining downdraft strength (Knupp and Cotton 1985; Tompkins 2001) and precipitation efficiency (Ferrier et al. 1996; Shepherd et al. 2001). Latent heating Moisture adjustment according to the difference of TBB between model and observation

31 The relation between temperature and moisture (water vapor) Warmer air can hold more water vapor at equilibrium than colder air. If air is cooled below the saturation temperature, some of the water vapor condenses into liquid, which releases latent heat and warms the air. Thus, temperature and water vapor interact in a way that cannot be neglected. Tetens formula e s = e 0 b ( T T exp T T 2 1 )

32 where qsat is the saturation mixing ratio with respect to liquid water above 0 C. Below 0 C, a mixed phase cloud is considered and qsat is evaluated using a combination of saturation mixing ratio over liquid water and ice from Tetens formula. ν(k) is a weighting function that identifies the portion of the vertical profile modified by the nudging procedure.

33 TBB observed by FY2C and that retrieved from numerical model FY2C Model 5 hours 7 hours 13 hours

34 The moisture nudging cycle NCEP/GFS Background fields TBB TBB TBB TBB nudging nudging nudging nudging 6h fcst 6h fcst 6h fcst 6h fcst 72h fcst 24h 18h 12h 6h 0h (Initial time) 24h nudging cycle

35 Rankine vortex (left) Vs moisture nudging (right) (a) (b) (c) (d)

36 (a) (b) (c) (d) (e) (radius of forcing: a:96km;b: 120km;c: 150km;d:300km)

37 Minimum sea level pressure (hpa) (a) Obs None WRFBogus Cntl Exp1 Exp2 Maximum 10 m wind speed (m s 1 ) (b) Time (hour) Time (hour) Obs None WRFBogus Cntl Exp1 Exp2

38 Future Work Further verification on the performance of the new initialization schemes with more TC cases. Better understanding of the mechanism of TC initialization in association with the dynamical and physical constraint. Join the dynamic and physical constraint in TC initialization Enhancing TC model physics and dynamics on the basis of TC initialization.

39 Can we Bridge the TC physics and Initialization? ( 3.3.html)

40 Numerical Prediction Tasks for China Typhoon 973 project

41 Thank you very much!