d. m. barker, w. huang, y.-r. guo, a. j. bourgeois, and q. n. xiao mon. wea. rev., 132, 897-914
DESCRIPTION
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 : - PowerPoint PPT PresentationTRANSCRIPT
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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
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Introduction
Goals of 3DVAR for MM5 :• Release as a research community data assimil
ation system.• Implementation in the Advanced Operational A
viation 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).
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Introduction
Assimilation system combines all sources of information:
– Observations - yo
– Background field - xb
– Estimate of observation/background errors.– Laws of physics.
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Introduction
Main feature :• Observations are assimilated to provide analysis
increments.• Analysis increments computed on an unstaggere
d 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.
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Introduction
Main feature : • Control variables include streamfunction, velocit
y 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 the National Meteorological Center (NMC) method.
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Implementation
Cold-Start Mode
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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.
)()()(2
1)()(
2
1 11 oTobTb EJ yyFyyxxBxxx
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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
)(1)()(2
112
1 oEToTJ y'y'Fy'y'x'Bx'x'
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Implementation
• The horizontal transform Uh is 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).
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Impact of truncating 3DVAR’s responsible for only 0.1% of error variance.
Variance # psi mode
# chi
mode
# pu
mode
# q mode
n Its Final J
(x104)
CPU (s)
Mem
(Mb)
99.9% 17 17 10 22 438438 25 1.33 251 220
100% 31 31 31 31 823723 24 1.32 420 316
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Correlation between pressure increment and ‘‘balanced’’ pressure
][2 'fpb vkvvvv
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Sinlaku
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• 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.
)/()( 20
220
02 bb
b yyy
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