RegionalScale En KF in Comparison with WRF3 Dvar

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Regional-Scale En. KF in Comparison with WRF/3 Dvar Fuqing Zhang and Ellie Meng Texas

Regional-Scale En. KF in Comparison with WRF/3 Dvar Fuqing Zhang and Ellie Meng Texas A&M University

WRF En. KF vs. 3 DVar over the Month of June 2003 (90 km)

WRF En. KF vs. 3 DVar over the Month of June 2003 (90 km) (30 km) Verification area Observations: standard soundings every 12 h QC’d by WRF/3 Dvar in D 2 Verifications: against soundings both before and after assimilation Boundary conditions: D 1 updated by 12 hourly FNL analysis Reference forecast: a single forecast for the period w/o assimilation

Month-long Performance of En. KF vs. 3 Dvar with WRF Reference forecast En. KF

Month-long Performance of En. KF vs. 3 Dvar with WRF Reference forecast En. KF (day of the month) 3 DVar (prior, solid; posterior, dotted) (day of the month) • All in terms of domain-averaged root-mean square error verifying against soundings • Both the En. KF and 3 DVar have smaller error than reference deterministic forecast • En. KF is consistently better than 3 Dvar in terms of 12 -h forecast and posterior analysis

Month-long Performance of En. KF vs. 3 Dvar with WRF ---- Reference forecast En.

Month-long Performance of En. KF vs. 3 Dvar with WRF ---- Reference forecast En. KF 3 DVar (prior, solid; posterior, dotted) posterior Prior Better performance of En. KF than 3 DVar also seen in both 12 -h forecast and posterior analysis in terms of root-mean square difference averaged over the entire month

12 h Fcst from En. KF vs. WRF/3 Dvar vs. NCEP/FNL ICs DF_En. KF

12 h Fcst from En. KF vs. WRF/3 Dvar vs. NCEP/FNL ICs DF_En. KF : 12 h deterministic forecast initiated from En. KF analysis DF_NCEP: 12 h deterministic forecast initiated from FNL analysis • DF_En. KF has smaller (larger) forecast error than 3 DVar (En. KF), suggesting En. KF does benefit from both using the ensemble mean for state estimation and using flow-dependent background error covariance. • The forecast started from the NCEP FNL analysis has error larger than that of En. KF but smaller than that of 3 DVar

En. KF 4 DVar: Coupling En. KF with 4 DVar in Lorenz’ 95 Fuqing

En. KF 4 DVar: Coupling En. KF with 4 DVar in Lorenz’ 95 Fuqing Zhang, Meng Zhang and Jim Hansen

Comparison in Perfect model Scenario (F=8. 0) 80 -variable Lorenz 95 model with 20,

Comparison in Perfect model Scenario (F=8. 0) 80 -variable Lorenz 95 model with 20, 40 (above) vs. 10 (below) members

Comparison in Imperfect model Scenario (F=8. 5 but truth is 8. 0) 80 -variable

Comparison in Imperfect model Scenario (F=8. 5 but truth is 8. 0) 80 -variable Lorenz 95 model with 20, 40 (above) vs. 10 (below) members

Imperfect model with moderate model error: F=8. 5 but truth is 8. 0 80

Imperfect model with moderate model error: F=8. 5 but truth is 8. 0 80 -variable Lorenz 95 model with 20, 40 (above) vs. 10 (below) members

Imperfect model with strong model error: F=9. 0 but truth is 8. 0 80

Imperfect model with strong model error: F=9. 0 but truth is 8. 0 80 -variable Lorenz 95 model with 20, 40 (above) vs. 10 (below) members