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.