data assimilation for sea fog over the yellow sea

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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)