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On the Predictability of an Advection Fog Event in North China Plain: Sensitivity of the Simulation to Initial Errors Qinghong Zhang Collaborator: Huiqin.

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Presentation on theme: "On the Predictability of an Advection Fog Event in North China Plain: Sensitivity of the Simulation to Initial Errors Qinghong Zhang Collaborator: Huiqin."— Presentation transcript:

1 On the Predictability of an Advection Fog Event in North China Plain: Sensitivity of the Simulation to Initial Errors Qinghong Zhang Collaborator: Huiqin Hu, Baoguo Xie Department of Atmospheric and Oceanic Science, School of Physics, Peking University Kunming, Yunnan Province The 4 th THORPEX Asia workshop

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

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;)Ballard et al. 1991Chibe et al. 2003Muller 2006 Physical process(Bott et al. 1990; Brown 1980; Brown and Roach 1976;Bott et al. 1990Brown 1980Brown and Roach 1976 Fisher and Caplan 1963 Fisher and Caplan 1963; Musson-Genon 1987;Musson-Genon 1987 Rodhe 1962 Rodhe 1962; Zdunkowski and Nielsen 1969)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 UTC hPa surface Contour: GHT Contour: slp Wind barb: u,v Shading: rh H

6 Longwave radiation Rrtm Short wave radiationDudhia scheme Surface layerQNSE surface layer Land surfaceThermal diffusion scheme Boundary layerQusi-Normal Scale Elimination PBL CumulusKain-Fritsch scheme (Only for D1 and D2) LANDUSE data: Beijing_30s (d03) NCEP fnl data: 1deg*1deg time0000 UTC UTC 22, Feb, 2007 domainD01:159*153 D02:232*214 D03:448*343 Horizontal resolution27 km;9 km;3 km e_vert39 levels (13 levels below 850 hPa) p_top50 hPa MicrophysicsWSM 6-classs graupel WRF Experimental design

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

8 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. Experimental design

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

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

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

12 0300 UTC 21 Feb 2007 SPTEXP: sensitivity to the magnitude of initial errors WORST0.2 initial error0.4 initial error 0.6 initial error 0.8 initial errorBEST

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

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

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

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

17 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. Conclusion and discussion

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

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

20 Thanks


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