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IGARSS 2011, Jul. 26, Vancouver 1

Improving Land Surface Energy and Water Fluxes Simulation over the Tibetan Plateau with Using a Land

Data Assimilation System

Hui Lu (Tsinghua University)Toshio Koike, Hiroyuki Tsutsui, Katsunori Tamagawa

(The University of Tokyo)Kun Yang, Xin Li (Chinese Academy of Science)

Xiangde Xu (Chinese Meteorological Admistration)


Contents

• Background and Objective• Land Data Assimilation System• Application Region and Data

– Simulation domain and ground sites– Used Data

• Results– Surface soil moisture– Land surface energy fluxes

• Remarks


Background and objective• Tibetan Plateau is important in the progress of the

Asian summer monsoon – land surface processes – direct Orographic and thermal effects

• Land-atmosphere interaction in T-P is the key to– improve the understanding of Asian monsoon – improve the accuracy of numerical weather prediction in

east Asia – mitigate weather disaster in this region

• Objectives of this research– To identify the potential of LDAS to improve the modeling

of land surface fluxes.– To generate reliable regional distribution of soil moisture

and energy fluxes


Land Data Assimilation System• Why LDAS

– Shortage of model• Maybe biased, can not correct errors from forcing, parameter setting

and model physics – Shortage of satellite remote sensing

• Limited information, both temporal and spatial

• Structure of LDAS: three parts of a variational system – Dynamic model: Land surface scheme :

• SiB2– TB observation:

• RTM: Advanced Integral Equation Method (AIEM)– Optimization scheme:

• Shuffled Complex Evolution (SCE)


LSM

lEH τP

↓lR ↓

sR

Radiation transferin canopy

Interception

↑↑sl RR

Roff

Base flow

Infiltrate and Diffuse

Transpira-tion

vqTU lEH τ

P↓lR ↓

sR

Radiation transferin canopy

Interception

↑↑sl RR

Roff

Base flow

Infiltrate and Diffuse

Transpira-tion

vqTU

Minimization schemeF(Tbobs-Tbsim)

Tg, Tc, Mv

Tbsim

Mv

Vegetation layer

Surface

Surface radiation Vegetation emission

RTM

Tbobs

MicrowaveTMI/AMSR/AMSR-E

(6.9/10.6 and 18.7 GHz)

SiB2/New SiB

DMRT-AIEM

Shuffled Complex Evolution

Optimization + Assimilation LDAS


Introduction of LDAS-UT: Input and Output

LDAS-UT

Meteorological ForcingMeteorological Forcing: Wind, Temp., Humidity, Pressure, Precipitation, Radiation

In situ observation, Satellite Products,

model outputs,

Default Parameters:Default Parameters:Land Cover Type,

Soil Type,……

ISLSCP

Output Status Output Status VariablesVariables:

Energy fluxesSoil Moisture profile

Soil Temp. profileCanopy Temperature

……

Semi-dynamic Vegetation informationSemi-dynamic Vegetation information:

MODIS, LAI VWCMODIS, NDVI Vegetation

Fractional coverage:

Observation:Observation:Microwave TBMicrowave TB

TMI/AMSR/AMSR-E


Application Region

• Domain:– Lat: 25-40N– Lon: 70-105E

• Simulated Period – May. - Sep., 2008

• Two local sites– West: Gaize– East: Naqu


Used Data

• In situ observation– Soil moisture at two sites– AWS observation– Energy fluxes derived from AWS observation by Bowen Ratio

• Reanalysis data from NCEP– Meteorological forcing for region simulation– Biases in radiation and precipitation, but not corrected for regional

application.

• Satellite remote sensing data– Soil moisture retrieval from AMSR-E (JAXA)– Brightness temperature from AMSR-E


ResultsSoil Moisture


0

10

20

30

40

50

60

5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28

Sur

face

soi

l moi

stur

e(%)

Obs AMSR LDAS NCEP

Result: Soil moisture at Gaize

0.44226.1325.28NCEP

0.6016.13-3.01AMSR-E

0.3618.462.74LDAS

RRMSEMBE　

0

100

200

300

400

500

600

700

5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28

Acc

umul

ated

pre

cipi

tatio

n (m

m)

Obs NCEP


0

10

20

30

40

50

60

70

5- 1 5- 31 6- 30 7- 30 8- 29 9- 28

Sur

face

Soi

l Moi

stur

e (%

)

Ob s AMSR LDAS NCEP

Result: Soil moisture at Naqu

0.41712.1510.02NCEP

0.56221.2510.16AMSR-E

0.8533.88-0.17LDAS

RRMSEMBE　

0

100

200

300

400

500

600

5/ 1 5/ 31 6/ 30 7/ 30 8/ 29 9/ 28

Acc

umul

ated

pre

cipi

tatio

n (m

m)

Obs NCEP


Result: Energy flux:Bowen Ratio

Clean wet/dry division is showed by LDAS result, while NCEP failed to represent such a feature.


Result: energy fluxes at GaizeMonthly Averaged Diurnal Cycle of lE at Gaize

-50

0

50

100

150

200

250

300

La

tent

Hea

t

Obs LDAS NCEP

May Jun Jul Aug Sep

0.94977.19-15.09G0

0.87831.0512.88lE

0.87954.2143.38Hs

RRMSEMBE　


Result: energy fluxes at NaquMonthly Averaged Diurnal Cycle of lE at Naqu

-100

0

100

200

300

400

Late

nt H

eat

[W/m

/m]

NCEP Obs LDAS

May Jun Jul Aug Sep

0.96768.853.72G0

0.97535.146.93lE

0.93442.3635.64Hs

RRMSEMBE　


Result: Dynamic variation

Cyclonic brings moisture from the Bay of Bengal to the SE of T-P, and brings dry air mass from Taklamagan desert

LDAS-UT is able to provide more realistic land surface status for research in other principles

LDAS-UT NCEP


Remark

• Land-atmosphere interaction in T-P is very important for Asian monsoon development.

• Combining MW remote sensing and LSM, LDAS could improve the land surface fluxes simulation.

• LDAS produce more realistic land surface status, which is in good agreement with monsoon development.

• Feeding LDAS fluxes into atmosphere model is expected


Acknowledgments

• The data is get from “Japan-China JICA project”. Colleges contribute to this project are:– UT: T. Koike, K. Tamagawa, H. Tutsui, L.

Wang– Tsukuba U.: K. Ueno– ITP: K. Yang, Y.M. Mao– CAREERI: X. Li, Z.Y. Hu, W.Q. Ma, M.S.Li– CAMS: X.D. Xu, H. Peng


Thank you for your attention!

Download - Hui-Lu-improving-flux.ppt

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