data assimilation for sea fog over the yellow sea
DESCRIPTION
Shanhong Gao. Data assimilation for sea fog over the Yellow Sea. 中国海洋大学 海洋气象学系. MODIS, MTSAT, FY images. Three aspects are important. model. ● initial conditions ● micro-physics ● PBL scheme. Obs. fog area. inversion. Observations. sound. synop. ships. QuikSCAT. airs. gps. - PowerPoint PPT PresentationTRANSCRIPT
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Data assimilation for sea fog over the Yellow Sea
Shanhong Gao
MODIS, MTSAT, FY images
中国海洋大学 海洋气象学系
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Three aspects are important
Obs
model
● initial conditions
● micro-physics
● PBL scheme
fogarea
inversion
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sound synop ships
airs gps
QuikSCAT
others
Observations
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Data assimilation methods
Obs
model resultDA
first guess(bg)
analysis
DA methods : OA, 3DVAR, 4DVAR, Kalman Filters
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(a) 3DVAR (3 dimensional varational )
analysis first guessobs
Background error
Observation error
Thhhh XXXXB ))(( 12241224
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(b) Hybrid-3DVAR ( ETKF + 3DVAR )
yo
3DVARxb xbxa
time
xb
xbxa
xa xb
xb
EnKF3DVAR + ETKF
Xb: bg yo: obs Xa: analysis
3DVAR: 3-dimensional variational
EnKF: Ensemble Kalman Filter
ETKF: Ensemble Transform Kalman Filter
Advantages:
based on the existed frame of 3DVAR flow –dependent background error
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(c) flow-dependent background error (BE)
Thhhh XXXX ))((B 12241224
Temporal mean
3DVAR uses static BE.In fact, flow-independent is better.
(Hamill et al., 2006)
Non-flow dependent flow dependent
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Data assimilation Tools
Based on the WRF model, we have developed
1. Cycling-3DVAR DA module
2. Hybrid-3DVAR Da module
WPS WRFreal.exe 3DVAR
be
bg obs
WRFreal.exe
3DVAR
be
obs
WRFreal.exe
3DVAR
be
obs
WRFreal.exe
单时次3DVAR 循环3DVAR WRF运行
-0.5△ t 0 0.5△ t △ t 1.5△ t 2△ t 2.5△ t时间
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子系统主要目录结构
create_my_case
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2. Two study cases
20 LST 06 Mar 02 LST 07 Mar 08 LST 07 Mar
12 LST 07 Mar 14 LST 07 Mar 20 LST 07 Mar
02 LST 08 Mar 05 LST 08 Mar
MTSAT IR(Gao et al., 2009)
TBB of IR1
Case1: Observed fact(Year 2006)
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Model configuration
Domains
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.
Radiation LW: RRTM
SW: Dudhia
Surface Noah land-surface model
SimulationPeriod
06_00 ---- 08_00 UTC
Mar 2006 ( 48 h )
Specifications of WRF run
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Comparison of simulated results
Exp-A
Exp-B
Exp-C
Exp-D
Exp-E
FNL only
Single
3DVAR
Cycling
3DVAR
Hybrid
Ens=12
Obs
Hybrid
Ens=24
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Case2: Observed facts (Year 2007)
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Model configuration
Domains
Back-ground
FNL Data (1.0°x 1.0°)
NEAR-GOOS Daily SST
(0.25°x 0.25°)
Resolution 10km, 2.5km; 44 levels
PBL YSU
Cumulus Kain-Fritsch
Moisture Lin et al.
Radiation LW: RRTM
SW: Dudhia
Surface Noah land-surface model
SimulationPeriod
29_00 ---- 29_06 UTC
Apr 2007 ( 6 h )
Specifications of WRF run
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Assimilating MTSAT-derived humidity
RH ~ 100%
fog top
height
MTSAT-IR Dual-channel detection
Step1
Step2
Step3 DA
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Result
Single
3DVAR
Cycling
3DVAR
Cycling
3DVAR +MTSAT
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Assimilating MTSAT-derived humidityWang et al. (2014)