Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo(1997) Using the Ensemble Kalman Filter Chen Deng-Shun 16 Apr, 2013, NCU Godinez,

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Presentation transcript:

Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo(1997) Using the Ensemble Kalman Filter Chen Deng-Shun 16 Apr, 2013, NCU Godinez, Humberto C., Jon M. Reisner, Alexandre O. Fierro, Stephen R. Guimond, Jim Kao, 2012: Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo (1997) Using the Ensemble Kalman Filter. J. Atmos. Sci., 69, 3147–3171.

Hurricane Guillermo(1997)

Parameter Estimation Model Predictive Model – Navier-Stokes equation set – Bulk microphysical model – Four parameters used within in model – Discrete model Parameter Estimation Model – Parameter estimation with EnKF – Temporal average

Navier-Stokes Equation Set Reisner and Jeffery (2009) momentum equation energy equation TKE model

Bulk Microphysical Model

Parameters of interest Wind shear Diffusion coefficient for surface momentum Surface vertical transport of water vapor Turbulence length scale(from the surface to the free atmosphere)

Latin Hypercube Sampling : LHS ensures that the ensemble of random numbers is representative of the real variability.

Parameter Estimation with EnKF EnKF analysis equation Model forecast covariance matrixCross-correlation matrix Perturbed observation Parameter correlation matrix

Considerations for parameter estimation with EnKF Time Average and Member Average 1.Though EnKF as a tool to estimate the model parameters using the variable data. 2. The model parameters can affect the dynamics structure, but they do not evolve though model. 3.The object of this paper is to estimate a constant parameter value for the given dataset.

Parameter Estimation Model Setup Derived Doppler Data Fields

Observation error in the retrieved latent heating δw = 1.56 m/s Reasor et al. (2009)

Parameter Estimation Model Setup EnKF and observation selection Sorting from smallest to largest (k x n) (n x 1) (1x l)

Results 1.Leading to large variations in the simulated intensity and structure. 2.Large amounts of derived data required to reasonably estimate. 3.The newly estimated parameters lead to an overall reduction in the model forecast error.

Ensemble averageControl Results Min. SLP vertical motion

Reasor et al. (2009) vertical velocity

Ensemble averageControl Larger values of latent heat are required to compensate for the impact of spurious evaporation (Reisner and Jeffery 2009) legs 5legs 9legs 5legs 9

How many observations should be assimilated ? Tong and Xue (2008) : A smaller number of data leads to a slower convergence rate in some experiments, and a number larger than 50 results in overadjustment to some parameters ensemble mean + white noise Reference runexperiments

It is only when approximately observations are used that all 4 parameters converge to the correct values. Blue : latent heat (DA1) Red : horizontal wind (DA2) Green : latent heat + horizontal wind (DA3) TE9 TE1 Reference run parameter Consistent with Yussouf and Stensrud (2012) Sensitivity test

DA1DA2 legs 5legs 9legs 5legs 9

Summary and Conclusions The estimates obtained using derived latent heat fields produced lower model forecast errors in structure. The results with latent heat produced a hurricane the has a reasonable intensity. Numerical errors in the model, such as numerical diffusion and spurious evaporation. The parameters associated with the primary component of hurricane intensification, condensation of water vapor into cloud water, should also be included in the current parameter estimation procedure.

Summary and Conclusions The influence that a small number of parameters has upon the evolution and formation of a simulated hurricane. Improving both hurricane structure and intensity, by estimating parameter using latent heat observations, will be further explored in future research.

Thanks for your sweet attention !! Question ? “The fear of the LORD is the beginning of wisdom.”

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