Satellite-based Land-Atmosphere

Coupled Data Assimilation

Toshio KoikeEarth Observation Data Integration & Fusion Research Initiative

(EDITORIA)Department Civil Engineering, Engineering School

The University of Tokyo

OBS 1998 (2003 unavailable)

NCEP

JMA

UKMO

Seasonal variation （ May - September)

Sensible （ H ） － Latent(LE ）－

H RMSE[W/ ㎡ ]

LE RMSE[W/ ㎡ ]

LDASUT 32.0 42.5

NCEP 40.2 68.4

JMA 32.3 79.8

UKMO 35.3 80.1

LE daily-mean ( June)

Obs

erve

d

Modeled

GCMs

Land Surface Scheme

Snow Physics

Model

CloudPhysicsModel

Soil MoistureSoil Moisture SnowSnow

Microwave Microwave RadiometerRadiometer

PrecipitationPrecipitation

Surface Emissivity & Temp.

Aqua

TRMM

Land-Atmosphere Data AssimilationLand-Atmosphere Data Assimilation

Land Surface Scheme

Satellite Data MinimizationScheme

Radiative Transfer

Model

Cost Function

LDAS

GCM

Forcing

OBS 1998 (2003 unavailable)

NCEP

JMA

UKMO

Seasonal variation （ May - September)

Sensible （ H ） － Latent(LE ）－

H RMSE[W/ ㎡ ]

LE RMSE[W/ ㎡ ]

LDASUT 32.0 42.5

NCEP 40.2 68.4

JMA 32.3 79.8

UKMO 35.3 80.1

LE daily-mean ( June)

Obs

erve

d

Modeled

LDASUT

LDASUT- GCMs

GCM

Land Surface Scheme

Satellite Data MinimizationScheme

Radiative Transfer

Model

Cost Function

GCM

Forcing

Regional ModelPhysical Down-scaling

Surface skin Soil moisture at MS6308

0.100

0.150

0.200

0.250

0.300

0.350

0.400

7/3/1998 6:00 7/4/1998 6:00 7/5/1998 6:00 7/6/1998 6:00 7/7/1998 6:00Date

Mvs_MS3608_Ass Mvs_MS3608_NoAss Mvs_MS3608_obs

Surface skin soil moisture at MS3637

0.000

0.050

0.100

0.150

0.200

0.250

0.300

7/3/1998 6:00 7/4/1998 6:00 7/5/1998 6:00 7/6/1998 6:00 7/7/1998 6:00 7/8/1998 6:00

Date

Mvs_MS3637_Ass Mvs_MS3637_NoAss Mvs_MS3637_obs

Surface skin soil moisture at MS3478

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

7/3/1998 6:00 7/4/1998 6:00 7/5/1998 6:00 7/6/1998 6:00 7/7/1998 6:00 7/8/1998 6:00

Date

Mvs_MS3478_Ass Mvs_MS3478_NoAss Mvs_4cm_Obs

soil moisture

(Surface perspective)

Assimilation No Assimilation

(Atmospheric perspective)

Vertical Wind field

GMS IR1-based convective Index

No Assimilation case Assimilation case

Vertical Wind field

Radar at BJ

With LandAssimilation

WithoutAssimilation

9-15 16-22 23-04

Cloud Physics Scheme

Radiative Transfer

Model

Cost Function

Regional Model

Satellite Data MinimizationScheme

GCM

Physical Down-scaling

IF Jmin

No

Yes

ARPS Model Output (Initial Guess)

Observation Operator (RTM) (Tbmod)

Model Operator (Lin Ice Scheme)(Assim. Parameter:ICLWC, IWV)

Cost (J)= (Tbmod - Tbobs )2

Global Minimization Scheme (Shuffled Complex Evolution) Duan et al, 1992

Optimized Initial

Condition

Cloud Parameter Update

IMDAS FrameworkIMDAS Framework

Tbobs

Precipitation Prediction by ARPS

12z 16z 20z 24z 04z 08z 12z

ARPS Model Simulation16:30z

16:30z 17:10z

Assimilation Window: 40 mins

TBobs AMSR-EInitial Guess

29th Jan 2003

30th Jan 2003

16:30z 17:00 　　　　　　　 18:00 19:00 20:00

Assimilation Window: 40 mins

PredictionStart of Prediction

with Improved Initial Condition

Initial conditionwith no assimilation

2929thth Jan, 17:00z Jan, 17:00z

dbz=200R**1.60 (Aonashi, 2004)dbz=200R**1.60 (Aonashi, 2004)Precipitation Rate(mm/hr)Precipitation Rate(mm/hr)

IMDAS

Initial conditionwith assimilation

3hour predictionwith no assimilation

2929thth Jan, 20:00z Jan, 20:00z

dbz=200R**1.60 (Aonashi, 2004)dbz=200R**1.60 (Aonashi, 2004)Precipitation Rate(mm/hr)Precipitation Rate(mm/hr)

IMDAS

3hour predictionwith assimilation

Cloud Physics Scheme

Radiative Transfer

Model

Cost Function

Regional Model

Land Surface Scheme

Satellite Data MinimizationScheme

Radiative Transfer

Model

Cost Function

GCM

Coupled Soil Atmosphere RTM2004/08/14 - 19:23 UTC

200

210

220

230

240

250

260

270

280

6v 6h 10v 10h 18v 18h 23v 23h 36v 36h 89v 89h

TB

[K]

Coupled

Observed

AIEM

By coupling AIEM with atmosphere RTM we get better agreement.

For wetter cases AIEM is sufficient.

Atmosphere-Land Coupled Data Assimilation System

Tb Error

Atmospheric effect derived from AMSR-E vs. MODIS Cloud Top Temperature

LDAS only MODIS/IR A-L Coupled DAS

Integrated Cloud Liquid Water

Atmospheric effect derived from AMSR-E vs. MODIS Cloud Top Temperature

LDAS only MODIS/IR A-L Coupled DAS

24 hour Prediction of Rainfall over the Tibetan Plateau

Prediction withthe A-L CoupledData Assimilation

As an Initial Condition

Only Nesting GOES IR

Coupler SystemCoupler System

Preliminary Design for Multi-scalePreliminary Design for Multi-scaleLand Impact Research by of L-A Land Impact Research by of L-A

Coupled DASCoupled DAS Regional-scale approach by L-A DAS without CMDASRegional-scale approach by L-A DAS without CMDAS

Extent: 40ºE - 160ºE and 0ºN - 60ºNExtent: 40ºE - 160ºE and 0ºN - 60ºN Grid size: 25 km → nx = 355, ny = 223, nz = 35Grid size: 25 km → nx = 355, ny = 223, nz = 35

Meso-scale “mobile” approach by L-A DAS with Meso-scale “mobile” approach by L-A DAS with CMDASCMDAS

Point-scale by the CEOP Reference Sites NetworkPoint-scale by the CEOP Reference Sites Network + +

モデルによる統合化