A three dimensional variational data assimilation system for mm5 implementation and initial results
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D. M. Barker, W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao Mon. Wea. Rev., 132, 897-914 - PowerPoint PPT Presentation


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A Three-Dimensional Variational Data Assimilation System for MM5 : Implementation and Initial Results. D. M. Barker, W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao Mon. Wea. Rev., 132, 897-914. Introduction. Goals of 3DVAR for MM5 :

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A three dimensional variational data assimilation system for mm5 implementation and initial results

A Three-Dimensional Variational Data Assimilation System for MM5 : Implementation and Initial Results

D. M. Barker, W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao

Mon. Wea. Rev., 132, 897-914


Introduction
Introduction MM5 : Implementation and Initial Results

Goals of 3DVAR for MM5 :

  • Release as a research community data assimilation system.

  • Implementation in the Advanced Operational Aviation Weather System (AOAWS) of the Taiwan Civil Aeronautics Administration (CAA).

  • Replacement of the multivariate optimum interpolation (MVOI) system in the operational, multitheater MM5-based system run by the U.S. Air Force Weather Agency (AFWA).


Introduction1
Introduction MM5 : Implementation and Initial Results

  • Assimilation system combines all sources of information:

    • Observations - yo

    • Background field - xb

    • Estimate of observation/background errors.

    • Laws of physics.


Introduction2
Introduction MM5 : Implementation and Initial Results

Main feature :

  • Observations are assimilated to provide analysis increments.

  • Analysis increments computed on an unstaggered grid. The unstaggered wind analysis increments are interpolated to the staggered grid of MM5/WRF, combined with the background field and output.

  • Analysis vertical levels are those of the input background forecast.


Introduction3
Introduction MM5 : Implementation and Initial Results

Main feature :

  • Control variables include streamfunction, velocity potential,‘‘unbalanced’’ pressure, and a humidity variable.

  • the horizontal component of background error is via horizontally isotropic and homogeneous recursive filters.

  • The vertical component of background error is climatologically averaged eigenvectors of vertical error estimated via theNational Meteorological Center (NMC) method.


Implementation
Implementation MM5 : Implementation and Initial Results

Cold-Start Mode


Implementation1
Implementation MM5 : Implementation and Initial Results

  • analysis xa is minimum x of cost-function

  • y = H(x). H is the nonlinear “observation operator”.

  • Error covariances:

    B = Background (previous forecast) errors.

    E = Observation (instrumental) errors.

    F = Representivity (observation operator) errors.


Implementation2
Implementation MM5 : Implementation and Initial Results

  • Define analysis increments: x’ = x-xb=UpUvUhv

    where y’ = Hx’, yo’ = yo - y.

    Up: physical variable transformation

    Uv: vertical transform

    Uh: horizontal transform

    v : control variable


Implementation3
Implementation MM5 : Implementation and Initial Results

  • The horizontal transform Uhis performed using recursive filters. The background error length scales is estimated using the NMC method’s accumulated forecast difference data.

  • The vertical transform Uv is applied via an empirical orthogonal function (EOF) decomposition of background error Bv (via the NMC method).



Correlation between pressure increment and ‘‘balanced’’ pressure


Sinlaku ‘‘balanced’’ pressure



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