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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

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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

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
  • Assimilation system combines all sources of information:
    • Observations - yo
    • Background field - xb
    • Estimate of observation/background errors.
    • Laws of physics.
introduction2
Introduction

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

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

Cold-Start Mode

implementation1
Implementation
  • 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
  • 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
  • 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).
slide16

Correlation between pressure increment and ‘‘balanced’’ pressure

slide22
The resulting analysis central pressure is given by
  • Using yb = 991 hPa, y0= 955 hPa, σb=1 hPa (derived from the NMC statistics) and σ0= 1 hPa, leads to y = 973 hPa. Using the PBogus2 value of σ0= 2 hPa gives y = 984 hPa.