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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 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: [email protected]
The 4th THORPEX Asia workshop
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“Dense fog raided Beijing !“
Fog coverage from satellite NOAA-17
0228 UTC 21 Feb 2007
Fog is high impact weather on aviation, marine and land transportation (Gultepe et al. 2007)
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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
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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.
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Synoptic overview
1200 UTC 20 0000 UTC 21
500 hPa
surface
Contour: GHT
Contour: slpWind barb: u,vShading: rh
H
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Longwave radiation Rrtm
Short wave radiation Dudhia scheme
Surface layer QNSE surface layer
Land surface Thermal diffusion scheme
Boundary layer Qusi-Normal Scale Elimination PBL
Cumulus Kain-Fritsch scheme (Only for D1 and D2)
LANDUSE data: Beijing_30s (d03)
NCEP fnl data: 1deg*1deg
time 0000 UTC 20 — 0000 UTC 22, Feb, 2007
domain D01:159*153D02:232*214D03:448*343
Horizontal resolution 27 km;9 km;3 km
e_vert 39 levels(13 levels below 850 hPa)
p_top 50 hPa
Microphysics WSM 6-classs graupel
WRF Experimental design
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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
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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
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Best: M16 M39
Worst: M38
Ensemble simulation of fog coverage at 27h
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Ensemble Forecast
BSTM WSEM
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
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0300 UTC 21 Feb 2007
Comparison of BEST & WORST Ensemble simulation
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0300 UTC 21 Feb 2007
SPTEXP: sensitivity to the magnitude of initial errors
WORST 0.2 initial error 0.4 initial error
0.6 initial error 0.8 initial error BEST
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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
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Qv T
U,V U, V ofBSTM
(error = BSTM –WSEM)Initial error for d02
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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
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Evolution of diff (BSTM-N_UV)
0000 UTC 20—0000 UTC 21
0h 6h 12h 18h 24h
T
U,V
Qv
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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
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U,V initial error
*
Observation on Offshore Oil Platform over Bohai Sea
T, Td, P, VIS, RH, Wind speed and direction
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Highway observation 47 offshore oil platform 3
GPS 43
***
Observation network for FOG project
Surface observation 2137wind profiler 3
Visibility
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Thanks