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Methods for Initial Ensembles. Assimilated observation. Sea fog case. Observed sea fog (brown shades) evolution detected from MTSAT data from 6 to 7 March 2006. The light-blue areas indicate high clouds overcast. Model configuration. Experiments design. - PowerPoint PPT Presentation
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Methods for Initial Ensembles
Assimilated observation
Sea fog case
Observed sea fog (brown shades) evolution detected from MTSAT data from 6 to 7 March 2006. The light-blue areas indicate high clouds overcast.
Model configuration
Back-ground
FNL Data (1.0°x 1.0°)
NEAR-GOOS Daily SST
(0.25°x 0.25°)
Resolution 30km, 10km; 44 levels
PBL YSU
Cumulus Kain-Fritsch
Moisture Lin et al.
RadiationLW: RRTMG
SW: RRTMG
Surface Noah land-surface model
Experiments design
Expt Specification
Y-D Single forecast using YSU scheme
M-D Single forecast using MYNN scheme
YC Ensemble forecast using YSU scheme
YS As in YC, but with SST perturbation
MC As in YC but using MYNN scheme
MS As in MC, but with SST perturbation
MY Choose 20 members from YS and MS respectively
There are 40 members in each ensemble experiment.
SST perturbation
Each ensemble member has the fixed perturbations throughout the experimental period, in order to avoid abrupt changes in the SST fields at every 6 h.
Evaluation methodStatistical scores POD, FAR, bias, ETS
Fog observation model
Derived from MTSAT Visibility LWC (g/kg)
1000 0.015
500 0.029
200 0.081
50 0.39
Flowchart of the Yellow Sea fog detection by using MTSAT data. (Wang et al. 2014)
Results -- fog area statistical scores The probability
threshold should be selected around 30%-50%,with bias around 1.0.
YS and MS are better than others, especially than 2 single forecasts.
BSS(Brier skill score)
rBSBS
BSS 1
A good probabilistic forecast should have a positive and larger BSS.
YSU and MYNN mean the five ensembles use the Y-D and M-D single forecasts as the references (BSr), respectively
BSS results show that SST perturbation is important for sea fog ensembles.
Fog visibility forecasts
200 m 500 m
SST perturbation improves the visibility forecast, and YSU scheme is more skillful than MYNN scheme for that in this case.