Download presentation

Presentation is loading. Please wait.

Published byRiya Huckins Modified over 2 years ago

1
CV3 background error statistics and tuning experiments with WRFDA Yong-Run Guo NCAR/MMM Presented in Central Weather Bureau, Taiwan 8 April 2011

2
CWB/UCAR 2011 project ---- TASK#1 Yong-Run Guo 1 April 2011 1.2 Improve the performance of WRFVar 1.2.1 Conduct additional tests on multiple outer-loop with the variable CV3 BE tuning factors To conduct the TWRF run and CV3 BE outer-loop experiments Developed the running shell script to include the relocation module, DFI, new KF. Testing runs starting from 2009080312Z to 2009080718Z with 6h cycling Experiments conducted: 1, TWRF : CV5 BE with 3 outer-loops (TWRF) Benchmark 2, CV3LP1: CV3 BE with 1 outer-loop OP211 3, CV3LP2: CV3 BE with 2 outer-loops, tuning approach-I 4, CV3LP2II: CV3 BE with 2 outer-loops, tuning approach-II With and without GPSRO data experiments

3
References Wu, Wan-Shu, R. James Purser, and David F Parrish, 2002: Three-Demesional Analysis with Spatially Inhomogeneous Covariances. Mon. Wea. Rev., 130, 2905-2916. Purser, R. James, Wan-Shu Wu, David F. Parrish, and Nigel M. Robert, 2003:Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis, Part I: Saptial Homogeneous and Isotropic Gaussian Covariances. Mon. Wea. Rev., 131, 1524-1535. Purser, R. James, Wan-Shu Wu, David F. Parrish, and Nigel M. Robert, 2003:Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis, Part II: Spatially Inhomogeneous and Anisotropic General Covariances. Mon. Wea. Rev., 131, 1536-1548. Bannister, R.N., 2008: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. Q. J. R. Meteorol. Sco. 134, 1951-1970. Bannister, R.N., 2008: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modeling the forecast error covariance statistics. Q. J. R. Meteorol. Sco., 134, 1971-1996.

4
Introduction of CV3 BE

5
The number 1 question from WRFDA users is What background error covariances are best for my application?. Procedure: Use default statistics files supplied with code (CV5 BE, CV3 BE, etc.). Create you own, once you have run your system for ~a few weeks (gen_be utility provided with WRFDA). Implement, tune, and iterate. Background Error (BE) Estimation in WRF-Var

6
Define analysis increments: x a = x b + I x Solve incremental cost function: where y = Hx, Define preconditioned control variable v space transform x=Uv (CVT) where U transform CAREFULLY chosen to satisfy B = UU T. Bannister (2008): Part II, Table 1, p.1975. Choose (at least assume) control variable components with uncorrelated errors: Incremental WRF-Var and Preconditioning

7
Assume background error covariance estimated by model perturbations x : Two ways of defining x in utility gen_be: The NMC-method (Parrish and Derber 1992): where e.g. t2=24hr, t1=12hr forecasts… …or ensemble perturbations (Fisher 2003): Tuning via innovation vector statistics and/or variational methods (??) Background Error Estimation for WRF-Var

8
Mathematical properties of the B-matrix Correlation matrix C and variance Σ 2, the covariance matrix is formed by multiplying respective columns and rows of C by the square roots of the variance, U is a orthogonal transform, i.e. U T U=I, to transform a vector between space and an alternative space. Given that the background error covariance matrix B in, we have. If the eigenvectors are the columns of U, and eigenvalues are the elements of, the -matrix in its eigenrepresentation is diagonal. B-matrix is square and symmetric,. The eigenvalues are real-valued and eigenvectors are mutually orthogonal. Covariance matrices are positive semi-definite. It means that the background term in the cost function, is convex or flat in all directions in state space, so the minimum of the whole cost function exists.

9
WRF-Var Control Variable Transform Define control variables: V (i,j,k)

10
Balance Via Statistical Regression Regression Coefficients after Wu et al (2002): Variances for Ψ, unbalnced χ_u, T_u, Ps_u, and rh Use corresponding regression coefficients to compute unbalanced field. Variances vary with latitude and level. Vertical length scale and horizontal length scale Wu et al. (2002): AppendixDaley (1993), Eq.(4.3.10) P110.

11
Regression coefficients

13
Standard deviation

14
Horizontal length-scale

15
Vertical length-scale The vertical length-scale in sigma unit {log(sigma)?} Only four 3-D control variables, ψ, χ u, t u, and rh, have the vertical length-scale.

16
Size of B-matrix CWB doamin: (221x127x44)=1,234,948 4 3-D variables (u, v, t, q) and 1 2-D variable (Ps). The size of vector η=(x b -x t ) is 4x1,234,948+28,067 = 4,967,859. The size of B-matrix : (4,967,859) 2 CV3 BE (NCEP Global): 192 latitudes, 42 sigma levels: Regression coefficients: 192x42 = 8,064, 192x42x42 = 338,688, 192x42 = 8,064. Total = 354,816 cross-covariance between variables (balance) Variances: (4x42 + 1) x 192 = 32,448 standard deviation Length-scales: (2x4x42 + 1) x 192 = 64,704 auto-covariance of variables Total = 451,968

17
Approaches to tune CV3 BE

18
The formulation for normalization of standard deviation is where σ norm_k is the normalized standard deviation and will be used in the transformation of the control variables. σ k is the input standard deviation, as 1 is the tuning factor for standard deviation, hl k is the tuned horizontal scale- length, v k, k depends on the tuned vertical scale-length, M fac is the map factor, and S amp is a factor by which the amplitude at the second sweep is normalized. S amp is determined by the filter characteristics. The subscript k denotes the model level k. So the normalized standard deviation σ norm_k will be obtained by the combined effect from all tuning factors (as 1 ) for variance, (as 2 ) for horizontal and (as 3 ) vertical scale-lengths. The caution must be taken to set the proper tuning factors to get the reasonable increments from each of the outer-loops. Approach I for CV3 BE tuning be%corp, be%corz in WRFDA

19
FactorLoop 1Loop 2Loop3 Var_scaling11.501.000.50 Var_scaling21.501.000.50 Var_scaling31.501.000.50 Var_scaling41.00 0.50 Var_scaling51.501.000.50 Len_scaling11.000.500.25 Len_scaling21.000.500.25 Len_scaling31.000.500.25 Len_scaling41.000.50 Len_scaling51.000.500.20 FactorLoop 1Loop 2 Var 2 HLVLVar 2 HLVL as1(3)0.0630.751.50.0160.501.5 as2(3)0.0630.751.50.0160.501.5 as3(3)0.2201.001.50.0630.751.5 as4(3)0.2302.001.50.0631.501.5 as5(3)0.2700.501.50.0680.401.5 Based on the formulation of variance: Considering that the (σ norm_k ) 2 should be smaller than (σ norm_k ) 1, the as1 2 should be further reduced from {0.167} 2 to {0.167x0.75} 2 = {0.125} 2 = 0.016 CV5 Tuning factors in TWRFCV3 Tuning factors in 2 loops Exp

20
00061218243036424854606672Mean TWRF10.236.252.065.3103.4127.0143.9151.3158.9177.2185.1211.7231.2127.2 CV3LP118.953.688.0118.1151.1168.1179.1231.0281.4303.7325.5357.2413.2206.8 CV3LP222.147.671.188.4106.0130.3153.0187.5238.2257.5314.8315.9341.0174.9

21
Approach II for CV3 BE tuning o Use TWRF CV5 BE as the target: standard deviation and length-scale o Two outer-loops for CV3 BE with the 1 st tuning factors same as before (OP211) o Single ob (near the typhoon center i=148, j=55) tests for u, t, q at level 20 and p at level 1. (since CV3 BE is latitude-dependent, we may need to try other the single ob at points o Analysis time is 2009080400Z Background error variance for the standard (one outer-loop) single ob test: However, if BE is kept same, the above equation is meaningless after the 1 st outer loop because the ( a-b ) equals to zero starting the 2 nd outer loop. But if BE is changed in the different outer loops, we can still use the above equation to diagnose a presumable by completing all the outer loops with the final analysis as a.

22
Scale-length determination in the single ob test: r B(r) B(0) S 0.882xσ 2

23
obParams.TWRF(CV5)CV3 BE ICV3 BE II u a-b (m/s) 0.8180.7290.839 S.L (km)71.07124.7877.23 t a-b ( o )0.4450.1290.441 S.L. (km)21.0698.9821.61 q a-b (g/kg)0.6230.3710.678 S.L. (km)13.3376.7617.45 p a-b (Pa)41.920.740.4 S.L. (km)98.48109.1197.64 CV3 BE ICV3 BE II FactorLoop 1Loop 2Loop1Loop2 Var 2 HLVLVar 2 HLVLVar 2 HLVLVar 2 HLVL as10.0630.751.50.0160.501.50.0630.751.50.0030.201.5 as20.0630.751.50.0160.501.50.0630.751.50.0030.201.5 as30.2201.001.50.0630.751.50.2201.001.50.9500.301.5 as40.2302.001.50.0631.501.50.2302.001.50.2300.201.5 as50.2700.501.50.0680.401.50.2700.501.51.3000.5751.5 Increments ( a-b ) and scale-lengths for single ob tests wit different BEs Tuning factors for CV3 BE with approach I and II

24
Reults Single ob tests Morakot forecasts

25
South-North Cross-section of increments of u, p, θ, and q from single u(148,55,20) tests: o - b = 1 m/s, TWRF: CV5 3-outer-loops U(a – b) = 0.818 m/s S.L. = 71.07 km CV3 BE I 2-outer-loops U(a – b) = 0.729 m/s S.L. = 124.78 km CV3 BE II 2-outer-loops U(a – b) = 0.839 m/s S.L. = 77.23 km

26
South-North Cross-section of increments of u, p, θ, and q from single t(148,55,20) tests: o - b = 1 o K, TWRF: CV5 3-outer-loops t (a – b) = 0.445 o S.L. = 21.06 km CV3 BE I 2-outer-loops t (a – b) = 0.129 o S.L. = 98.98 km CV3 BE II 2-outer-loops t (a – b) = 0.441 o S.L. = 21.61 km

27
South-North Cross-section of increments of u, p, θ, and q from single q(148,55,20) tests: o - b = 1 g/kg, TWRF: CV5 3-outer-loops q (a – b) = 0.623 g/kg S.L. = 13.33 km CV3 BE I 2-outer-loops q (a – b) = 0.371 g/kg S.L. = 76.76 km CV3 BE II 2-outer-loops q (a – b) = 0.678 g/kg S.L. = 17.45 km

28
South-North Cross-section of increments of u, p, θ, and q from single p(148,55,1) tests: o - b = 100 Pa, TWRF: CV5 3-outer-loops p (a – b) = 41.9 Pa S.L. = 98.48 km CV3 BE I 2-outer-loops p (a – b) = 20.7 Pa S.L. = 109.11 km CV3 BE II 2-outer-loops p (a – b) = 40.4 Pa S.L. = 97.64 km

29
CWB/UCAR 2011 project ---- TASK#1 Yong-Run Guo 10 February 2011 1.2 Improve the performance of WRFVar 1.2.1 Conduct additional tests on multiple outer-loop with the variable CV3 BE tuning factors To conduct the TWRF run and CV3 BE outer-loop experiments Developed the running shell script to include the relocation module and DFI Testing runs starting from 2009080312Z to 2009080718Z with 6h cycling Experiments conducted: 1, TWRF : CV5 BE with 3 outer-loops (TWRF) 2, CV3LP1: CV3 BE with 1 outer-loop 3, CV3LP2: CV3 BE with 2 outer-loops, tuning approach-I 4, CV3LP2II: CV3 BE with 2 outer-loops, tuning approach-II With and without GPSRO data experiments

30
00061218243036424854606672Mean TWRF10.236.252.065.3103.4127.0143.9151.3158.9177.2185.1211.7231.2127.2 CV3LP118.953.688.0118.1151.1168.1179.1231.0281.4303.7325.5357.2413.2206.8 CV3LP222.147.671.188.4106.0130.3153.0187.5238.2257.5314.8315.9341.0174.9

31
00061218243036424854606672Mean TWRF10.236.252.065.3103.4127.0143.9151.3158.9177.2185.1211.7231.2127.2 CV3LP118.953.688.0118.1151.1168.1179.1231.0281.4303.7325.5357.2413.2206.8 CV3LP222.147.671.188.4106.0130.3153.0187.5238.2257.5314.8315.9341.0174.9 CV3LP2i i 9.038.856.5101.0132.5150.4167.8183.7221.2261.6282.8335.4369.6177.7

32
There are no significant differences between approach I and II. The reasons might be: The same tuning factors were used in the 1 st loop in order to be consistent with the OP211 Only variances and scale-lengths could be tuned with the tuning factors. The statistic balance part cannot be tuned, which are different between the CV5 and CV3 BE. We did not touch the vertical scale tuning. The vertical covariance modeling between the CV5 and CV3 are totally different. We just tuned the CV3 BE based on the single ob tests at ONE points. It is worth to try single ob tests at other grid-points. Here we just proposed two approaches for CV3 BE tuning only for typhoon Morakot case, which may be worth for more experiments. Regional GSI BE (code is ready) is also worth to be tested. Remarks

33
GSPRO Impact on Morakot track forecast

34
Impact of GPSRO data on Typhoon MORAKOT track forecast From 18 UTC 3 to 12 UTC 7 August 2009 with Sixteen 6-h WRFDA/WRF (TWRF) full cycles Domain: CWB 45-km operational domain (222x128x45) TWRF: Relocation => 3DVAR with 3 CV5-outer-loops => Update_BC => DFI => WRF with new-KF Totally there are 506 GPSRO profiles available during the experimental period. With GPSRO data assimilated, mean error is reduced from 131.4 km to 114.9 km.

35
END THANK YOU

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google