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)
IGARSS 2011, Jul. 26, Vancouver 2
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
IGARSS 2011, Jul. 26, Vancouver 3
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
IGARSS 2011, Jul. 26, Vancouver 4
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)
IGARSS 2011, Jul. 26, Vancouver 5
LSM
lEH P
lR
sR
Radiation transferin canopy
Interception
sl RR
Roff
Base flow
Infiltrate and Diffuse
Transpira-tion
vqTU lEH
PlR
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 layerSurface
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
IGARSS 2011, Jul. 26, Vancouver 6
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
IGARSS 2011, Jul. 26, Vancouver 7
Application Region
• Domain:– Lat: 25-40N
– Lon: 70-105E
• Simulated Period – May. - Sep., 2008
• Two local sites– West: Gaize
– East: Naqu
IGARSS 2011, Jul. 26, Vancouver 8
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
IGARSS 2011, Jul. 26, Vancouver 9
ResultsSoil Moisture
IGARSS 2011, Jul. 26, Vancouver 10
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
MBE RMSE R
LDAS 2.74 8.46 0.361
AMSR-E -3.01 6.13 0.601
NCEP 25.28 26.13 0.442
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
IGARSS 2011, Jul. 26, Vancouver 11
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 (%
)
Obs AMSR LDAS NCEP
Result: Soil moisture at Naqu
MBE RMSE R
LDAS -0.17 3.88 0.853
AMSR-E 10.16 21.25 0.562
NCEP 10.02 12.15 0.417
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
IGARSS 2011, Jul. 26, Vancouver 12
Result: Energy flux:Bowen Ratio
Clean wet/dry division is showed by LDAS result, while NCEP failed to represent such a feature.
IGARSS 2011, Jul. 26, Vancouver 13
Result: energy fluxes at GaizeMonthly Averaged Diurnal Cycle of lE at Gaize
-50
0
50
100
150
200
250
300
La
ten
t He
at
Obs LDAS NCEP
May Jun Jul Aug Sep
MBE RMSE R
Hs 43.38 54.21 0.879
lE 12.88 31.05 0.878
G0 -15.09 77.19 0.949
IGARSS 2011, Jul. 26, Vancouver 14
Result: energy fluxes at NaquMonthly Averaged Diurnal Cycle of lE at Naqu
-100
0
100
200
300
400
La
ten
t He
at [
W/m
/m]
NCEP Obs LDAS
May Jun Jul Aug Sep
MBE RMSE R
Hs 35.64 42.36 0.934
lE 6.93 35.14 0.975
G0 3.72 68.85 0.967
IGARSS 2011, Jul. 26, Vancouver 15
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
IGARSS 2011, Jul. 26, Vancouver 16
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
IGARSS 2011, Jul. 26, Vancouver 17
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
IGARSS 2011, Jul. 26, Vancouver 18
Thank you for your attention!