Download - Model error issues: microphysics errors
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10/18/2011
Youngsun Jung and Ming XueCAPS/OU
with help from Tim Supinie
Model error issues: microphysics errors
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Observation error: Non-Gaussianity, inaccurate observations error variance, none-zero observation error correlation, etc.
Observation operator error
Model error
Source of errors
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Example: Observation operator error
http://www.radar.mcgill.ca/science/ex-phenomenon/ex-melting-layers.html
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In imperfect model experiments, it is observed that model error dominates the error growth in data assimilation cycles.
Despite this, the characteristics of model error are little known and its statistical properties are poorly understood (Dee 1995; Houtekamer et al. 2005).
For convective-scale NWP, microphysics scheme represents one of the most important physical processes.
Background
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Various covariance inflation methods (Tim Supinie)
Parameter estimation
Improving microphysical parameterizations
Outline
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Inflation methodsMultiplicative inflation (Anderson and
Anderson, 1999)
Relaxation (Zhang et al., 2004)
Adaptive inflation (Whitaker and Hamill, 2010)
Additive noise (Mitchell and Houtekamer, 2000)a
Sensitive to the inflation
factor/size of noise
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Inflation factor
By Tim Supinie
Perfect model scenario– Multiplicative: 1.09– Relaxation: 0.44– Adaptive: 0.43
Imperfect model scenario– Multiplicative: 1.12 -> filter divergence– Relaxation: 0.5 -> filter divergence– Adaptive: 0.8
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Change in ensemble spread
By Tim Supinie
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Change in ensemble spread
By Tim Supinie
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Additive vs. Adaptivet = 1500 sec
Additive noise Adaptive
MAX: 30.88Min: -34.56
MAX: 31.17Min: -27.37
Wz=7km
corr(Z, qr)z=2km
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Additive vs. Adaptivet = 3600 sec
Additive noise Adaptive
MAX: 37.12Min: -20.20
MAX: 25.68Min: -27.68
efmean
enmean
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Additive (0.5 to u, v, T) vs. Adaptive (0.85)
Sky: Additive + multiplicativeOrange: Adaptive
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Certain DSD parameters such as the bulk densities and the intercept parameters of hydrometeors greatly influence the evolution of storm through microphysical processes.
Significant uncertainties exist in those parameters.
Several studies have shown that the EnKF method is capable of successfully identifying parameter values during assimilation process and, therefore, may help improve forecast (Annan et al. 2005a,b; Annan and Hargreaves 2004; Hacker and Snyder 2005; Aksoy et al. 2006a,b; Tong and Xue 2008a,b).
Parameter estimation
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Parameter estimation (single-parameter)
Perfect observation operator Imperfect observation operator
Tong and Xue (2008)Jung et al. (2010)
√√
√
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Parameter estimation (three-parameter)
Perfect observation operator Imperfect observation operator
Tong and Xue (2008)Jung et al. (2010)
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Shade: log10(N0r) for the ensemble mean of EXP_DM at z = 100 m AGLContour: ZDR log10(8x105) ≈ 5.9
Parameter estimation
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Example of high hail bias29-30 May 2004 supercellMilbrandt and Yau SM scheme
Ensemble mean analysis at z = 100 m and t = 60 min
0.10.1
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Example of high hail bias29-30 May 2004 supercellLFO scheme
Ensemble mean analysis at z = 2 km and t = 60 min
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Error in the microphysics scheme
By Tim Supinie
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Analyzed polarimetric variables vs. observed(MY)(LIN)
excessive size sorting ?
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Assimilating ZDR using a SM scheme
z = 2 km
No ZDR With ZDR
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Model error becomes a huge issue for real-data cases.
Various covariance inflation methods are found to be helpful but each method has its own limitations. Understanding strength and weaknesses of each method can help make better use of them.
Additional observations can help only if the observations carries information that the model can handle.
Summary
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Certain microphysics bias is very hard to treat and can be further deteriorated during data assimilation when the problem is seriously under-constrained by observations.
Observation operator errors can significantly influence the quality of analysis for storm scale DA.
Therefore, there should be continuous efforts to improve the model and the observation operator.
Summary