Dynamic Initialization to Improve Tropical Cyclone Intensity and Structure Forecasts Chi-Sann Liou Naval Research Laboratory, Monterey, CA (JHT Project,

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Presentation transcript:

Dynamic Initialization to Improve Tropical Cyclone Intensity and Structure Forecasts Chi-Sann Liou Naval Research Laboratory, Monterey, CA (JHT Project, Progress Report)

Unbalanced Initial Conditions of a TC Forecast Improper balance conditions included in analyzing TC circulation Diabatic Forcing: an important component in TC circulation balance –Forecast model must be involved in getting balanced initial conditions Method of getting balanced initial conditions –4D Var, or –3D Var with a proper initialization procedure

SLP850 W Unbalanced Initial Conditions

Dynamic Initialization Must consider diabatic forcing in TC balance –Dynamic initialization (assuming unbalanced components are high frequency) t=0 -NN (forecast) Digital Filtering t=0 N (forecast) Extra Damping –Traditional method versus Digital Filter

Digital Filter A very selective low pass filter Using truncated inverse Fourier transform to remove high frequency components from input signals In Frequency Domain: In Physical Time Domain:

Gibbs’ Phenomenon Unfortunately: converges very slowly !! H =>

Filter Response Function  c = 2  /(6*3600)  t = 240s Pass band:  <  p =-3dB Stop band:  >  s = -20dB Transition band:  p >  >  s Stop band ripple:  s = -20log(  )  = largest ripple Size  pp ss cutoff =>

Window Functions Apply a window function to improve convergence: i.e., Windows Tested: – Lanczos – Hamming – Riesz – Kaiser – Dolph-Chebyshev (Fixed Windows) (Adjustable Windows)

Windows Tested Lanczos: Hamming: Riesz: Kaiser: Dolph-Chebyshev: T 2N : Chebyshev polynomial,

Hamming Lanczos Kaiser Dolph-Chebyshev Riesz Response Functions with Windows

Dynamic Initialization with Digital Filtering Adiabatic: Diabatic: t=0 -NN (forecast) Digital Filtering ADIA t=0 -NN (forecast) Digital Filtering DIAB1 t=0 -NN (forecast) Digital Filtering DIAB2 Digital Filtering

Cost of Digital Filtering Initialization Compute filter weights Apply filtering Perform backward and forward initialization integration ===> 95% cost Initialization integration  cutoff frequency (period=  c ): For tropical cyclone forecast:  c = 2 hours (For NWP forecast with Lanczos window :  c = 6 hours)

Initialization with Digital Filtering

Tropical Cyclone Isabel ( ) AnalyzedInitialized

Initialization with Digital Filtering

Summary With the Dolph-Chebyshev window and 2-h cutoff period, diabatic digital filtering can effectively provide much better balanced initial conditions of a tropical cyclone Adiabatic digital filtering only marginally improves initial conditions for tropical cyclone forecast The type-2 diabatic digital filtering integration strategy makes very little difference in the results, but cost ¼ more in time integration Diabatic digital filtering initialization improves track forecast of COAMPS ®