Qinghong Zhang 张庆红 Collaborator: Huiqin Hu, Baoguo Xie

1. The 4th THORPEX Asia workshop On the Predictability of an Advection Fog Event in North China Plain: Sensitivity of the Simulation to Initial Errors Qinghong Zhang 张庆红 Collaborator: Huiqin Hu, Baoguo Xie Department of Atmospheric and Oceanic Science, School of Physics, Peking University Kunming, Yunnan Province 10-31-2012 Email: qzhang@pku.edu.cn

2. Fog is high impact weather on aviation, marine and land transportation (Gultepe et al. 2007) Fog coverage from satellite NOAA-17 0228 UTC 21 Feb 2007 “Dense fog raided Beijing !“

3. predictability of Fog • Due to the complexity, diversity and the fine scale of process • Fog predictability is limited • Fog simulation is sensitive to • Grid resolution (Ballard et al. 1991; Chibe et al. 2003; Muller 2006;) • Physical process(Bott et al. 1990; Brown 1980; Brown and Roach 1976; • Fisher and Caplan 1963; Musson-Genon 1987; • Rodhe 1962; Zdunkowski and Nielsen 1969) • Initial condition (Musson- Genon 1987; Bergot and Guedalia 1993 • Fitzjarrald and Lala 1990; Ballard et al. 1991; etc) there are few systematic studies focusing on fog predictability associated with the characteristics of initial errors

4. Objective: sensitive of fog simulation to initial errors • Sensitivity of fog simulation to different kinds of initial errors • with aspects to different magnitudes, • vertical distributions • and variables.

5. Synoptic overview 1200 UTC 20 0000 UTC 21 500 hPa Contour: GHT surface Contour: slp Wind barb: u,v Shading: rh H

6. WRF Experimental design • LANDUSE data: Beijing_30s • (d03) • NCEP fnl data: 1deg*1deg

7. Deterministic simulation Fog coverage (visibility less than 1 km for d03) at 27h 0300 UTC 21, Feb 2007 (at the second vertical model layer, ~ 94 m)

8. Experimental design An ensemble of 40 members with initial conditions generated by randomly drawing the background error covariance from a fixed covariance model WRF VAR The initial perturbations were roughly 0.3 g/kg for mixing ratio, 3 m/s for winds and 1.2 K for temperature.

9. Ensemble simulation of fog coverage at 27h Best: M16 M39 Worst: M38

10. Experimental design Ensemble Forecast BSTM WSEM Initial Difference Initial Errors SPTEXP (0.2, 0.4, 0.6, 0.8) magnitude REPEXP (at 10, 20, 30 bottom vertical model layers) Vertical distributions RMVEXP (Qv, , U and V) Most sensitive variables

11. Comparison of BEST & WORST Ensemble simulation 0300 UTC 21 Feb 2007

12. SPTEXP: sensitivity to the magnitude of initial errors WORST 0.2 initial error 0.4 initial error 0.8 initial error BEST 0.6 initial error 0300 UTC 21 Feb 2007

13. REPEXP: sensitivity of vertical distribution of initial error (~ 1.7 km) (~ 7 km) 20 bottom levels 10 bottom levels WORST (~ 13 km) 30 bottom levels 20 top level BEST 0300 UTC 21 Feb 2007

14. Initial error for d02 (error = BSTM –WSEM) T Qv U, V of BSTM U,V

15. RMVEXP: sensitivity of different variable of initial errors no Qv no T no u, v WORST no Qv, T no Qv,u,v no Qv, T ,u,v BEST 0300 UTC 21 Feb 2007

16. Evolution of diff (BSTM-N_UV) 0h 6h 12h 18h 24h U,V T Qv 0000 UTC 20—0000 UTC 21

17. Conclusion and discussion • Although fog simulation was highly sensitive to initial errors, the improvement of simulation due to the linearly decreasing of initial errors is nearly linear. • The initial errors at 20 bottom vertical model layers (~7 km) are sufficient to cause the failure of fog simulation in this case. • Fog simulation is much more sensitive to initial errors of horizontal wind than that of water vapor and temperature in this case. • Results from a series of sensitivity experiments in this study should be verified by data assimilation of real data.

18. Observation on Offshore Oil Platform over Bohai Sea U,V initial error T, Td, P, VIS, RH, Wind speed and direction *

19. Observation network for FOG project GPS 43 Surface observation 2137 wind profiler 3 Highway observation 47 offshore oil platform 3 Visibility * * *

20. Thanks