Microwave data assimilation practice in the Tibetan Plateau and … · 2014-09-15 · Microwave...
Transcript of Microwave data assimilation practice in the Tibetan Plateau and … · 2014-09-15 · Microwave...
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Microwave data assimilation practice in the Tibetan
Plateau and its implication for land hydrological modeling
Kun Yang1 ([email protected])
Jun Qin1, Long Zhao1,2, Yingying Chen1, Lazhu1
1. Institute of Tibetan Plateau Research, Chinese Academy of Sciences
2. JSG, University of Texas at Austin
CAHMDA-VI and HEPEX-DAFOH III Workshop
September 8-12, 2014
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Outline
Status of soil moisture observations and microwave retrievals in the Tibetan Plateau
Validations and applications of an auto-calibrated land data assimilation system (LDAS)
LDAS potential for improving land hydrological modeling
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Validations of satellite soil moisture products needs
dense in situ measurements, because soil moisture has
high spatial variability.
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The Tibetan Plateau observatory of plateau scale soil
moisture and soil temperature (Tibet-Obs)
(Su et al., 2011 HESS)
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2020
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Humid
Semi-aridDry
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A multi-scale soil moisture and freeze-thaw network in CTP
(Yang et al., 2013 BAMS)
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1 deg
0.25 deg
0.1 deg
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very high soil organic matters content
Sensor calibration according to soil texture and SOC
Based on relationship established by laboratory experiments
6(Yang et al., 2013 BAMS)
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Upscale from points to pixels by introducing MODIS LST
7(Qin et al., 2013RSE)
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Data sharing through ISMN
• Cooperation with ISMN Network– Network
• Required: general info/contact person(s)
– Station• Required: name/Lon/Lat/Ele
• Optional: Image/photo…
– Data• Required: monitoring depth(s)/sensor
info/acquisition time (UTC!)/
• Optional: meteorological variables/soil properties/land cover…
– Data sharing policy• Scientific use only
• No onward distribution
• Acknowledgement and citation (the original data provider and the ISMN)
• Delivery may be suspended or terminated at any moment.
• Data access– Data portal through DAM
http://www.ipf.tuwien.ac.at/insitu/
http://dam.itpcas.ac.cn/rs/?q=data8
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Evaluation: four AMSR-E products have large biases
Descending
Ascending
(Chen et al., 2013 JGR) 9
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Evaluation: The accuracy of SMOS L2 SM data is scale-
dependent; higher accuracy at coarser resolution
Ascending
15km res
Descending
15km res
Ascending
100 km res
Descending
100 km res
10(Zhao et al., 2014 RSE)
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Ascending
Descending
Evaluation: SMOS L3 SM data performs not well
11(Zhao et al., 2014 RSE)
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• SMOS L2 SM data at coarse resolution show acceptable accuracy in this region and good accuracy can be achieved when assimilated into land surface model (Zhao et al., RSE, 2014)
• Other products have low accuracy and the RMSE in the semi-arid area is > 0.12 m3 m-3
(Su et al., 2011; Chen et al., 2013)
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Outline
Status of soil moisture observations and microwave retrievals in the Tibetan Plateau
Validations and applications of an auto-calibrated land data assimilation system (LDAS)
LDAS potential for improving land hydrological modeling
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Land surface modeling MV remote sensing
Physically consistent
Temporally continuous
Model-dependent
Parameter-dependent
“Observed” or Objective
Spatially continuous
Discontinuity in time
Parameter-dependent
Couple
Regional soil moisture estimation through
microwave TB assimilation
LDAS
(Land Data Assimilation System)
Overall goal
Reliable regional soil moisture product
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TBobs
TBsim(Q, h, b, Wsat, soil texture, …)
How to understand the gap (dTB) between model and observation?
1. Search for observation and model error covariances
2. Attribute the gap to inappropriate specification of model
parameter values optimize parameters based on the gap
t0 t1
Parameter calibration issue in LDAS
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dTBModel
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Dual-pass (calibration + assimilation) Algorithm
(Yang et al., 2007JMSJ)
RTMTbLSMTg, Tc, Wsfc
F=sum(Tbobs-Tbsim)2Min(F) Parameters
F=(Tbobs-Tbsim)2Min(F) Wsfc
Pass 2
(t ~ 1 day)
Pass 1
(t ~ month-year)
• Pass 1: Optimize parameter values in a long-term window (month-year),
• Tuning parameters with satellite data/products instead of in situ data
• time-consuming but only conduct once.
• Pass 2: Estimate land state in a short-term window (daily)
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Parameters:
Wind, Ta, qa
Pa, Prec, Rad
Spatial coverage:
Mainland China
Temporal coverage:
1979-2012
Resolution:
0.1 deg, 3 hrs
Fusion
CMA station
TRMM
GLDAS
SRB
Reanalysis
1717
Develop a China Meteorological Forcing Dataset (CMFD)
to drive LDAS in China
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Validations at five sites
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5620
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1 CEOP Mongolian site
4 Tibet_obs sites
(Maqu, Naqu (Medium+large), Ngari)
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Evaluate: LDAS output against the Mongolian network
Observation
Assimilate AMSR-E TBv data (6.9, 10.7, 18.7 HGz) with TBv-
based auto-calibration, driven by GLDAS forcing
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Evaluate: LDAS output against the Naqu Medium-network
Assimilate AMSR-E TBv data (6.9, 10.7, 18.7 HGz) with TBv-
based auto-calibration, driven by CMFD forcing
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Evaluate: LDAS output against the Naqu Large-network
Observation
Assimilate AMSR-E TBv data (6.9, 10.7, 18.7 HGz) with TBv-
based auto-calibration, driven by CMFD forcing
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Evaluate: LDAS output against the Maqu network
Assimilate AMSR-E TBv data (6.9, 10.7, 18.7 HGz) with TBv-
based auto-calibration, driven by CMFD forcing
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Evaluate: LDAS output against the Ngari network
Assimilate AMSR-E TBv data (6.9, 10.7, 18.7 HGz) with TBv-
based auto-calibration, driven by CMFD forcing
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Outline
Status of soil moisture observations and microwave retrievals in the Tibetan Plateau
Validations and applications of an automatically calibrated land data assimilation system (LDAS)
LDAS potential for improving land hydrological modeling
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LDAS potential for improving land hydrological modeling
Improving soil moisture initial conditions
Improve land model parameters
– The LDAS uses AMSR-E TB data to calibrate
land model parameters so as the modeled TB is close to
the observed one
– Question: do the estimated parameter values really
improve open-loop land hydrological modeling?
Improve process understanding
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Open-loop land surface modeling comparisonfor Mongolian network (2003 summer)
With IGBP
soil parameters
With LDAS
calibrated parameters
observation
observation
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Open-loop land surface modeling comparisonfor Naqu Medium-network (2011 summer)
Much underestimate summer soil moisture
With LDAS
calibrated parameters
With IGBP
soil parameters
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Open-loop land surface modeling comparisonfor Naqu Large-network (2010-2011)
Much underestimate summer soil moisture
With LDAS
calibrated parameters
No rainfall input
Frozen season
Liquid
Total
observation
observation
With IGBP
soil parameters
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Open-loop land surface modeling comparisonfor Maqu network (2008-2011)
Frozen season
With LDAS
calibrated parameters
Liquid
Total
With IGBP
soil parameters
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LDAS gives low soil moisture
values in Sichuan Basin
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Sichuan Basin
LDAS can reproduce some unique regional feature
The soil in Sichuan Basin is a kind
of purple soil with low porosity.
Soil porosity map
(Shangguan et al., 2013)
LDAS-estimated soil moisture
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Jan Mar May
Jul Sep Nov
LDAS-estimated soil moisture for Tibetan Plateau
Summer rainfall decreases from SE to NW Plateau, but soil moisture content does
not strictly vary along the direction, perhaps due to terrain complexity. The circled
flat area in the central Plateau has higher soil water content.31
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Summary and outlook
• Soil moisture data collecting platform has been well
established on the Tibetan Plateau.
• The auto-calibrated LDAS can produce reliable
regional soil moisture estimates for the Plateau
(short grass)
• The LDAS has potential to be a feasible tool to
calibrate land surface modeling at grid scale
• Future work:
– Introduce prior information to avoid over-fitting
– The nature of estimated parameter s: model-dependent?
32Thanks for your attention!