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Improved synthesis data productsusing multiple constraints modeldata assimilationProf Damian Barrett, Luigi Renzullo, Anth...
Background             • Synthesis products key output of TERN             • Need robust methods to combine high volume   ...
‘Traditional’ biophysical modeling                    Vegetation &                     soil classes                    Par...
Model Data Assimilation: (1) Inverse model                        IceSAT           r , n                                ...
Model Data Assimilation: (2) Assimilating observations                             IceSAT            r , n              ...
Gain Matrix  • K is a function of model sensitivities and model + observation covariances       1. Provides spatial inform...
Covariances: (H B HT + R)  Covariance matrix (441 x 441)   Spatially correlated grid (21 x 21)        1.9 x 105 elements  ...
Error covariance matrix: (H B HT + R)Region: 550 x 460 kmMatrix size:4 x 253,000 x 253,000                                ...
Error covariance matrix: (H B HT + R)Shows:1. Length scale correl.2. Similar veg behavior3. Info ‘smearing’4. Source analy...
Inferred canopy conductance based on obs TS & TB   Mean gc [Range 0.1 – 11.1 mm/s]   s gc [Range 0.01 – 10 mm/s]
Analysis corrections (Qanalysis – Qopen loop)    A Horizon [Range 0.14 – 20 mm]   B Horizon [Range 0.14 – 20 mm]
Summary          • Synthesis products key output of TERN          • MDA provides a rigorous mathematical          framewor...
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Damian Barrett_Improved soil moisture and canopy conductance data products for Australia using multiple satellite observations within a multiple constraints model-data assimilation framework

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Damian Barrett_Improved soil moisture and canopy conductance data products for Australia using multiple satellite observations within a multiple constraints model-data assimilation framework

  1. 1. Improved synthesis data productsusing multiple constraints modeldata assimilationProf Damian Barrett, Luigi Renzullo, Anthony AlmarzaUniversity of QueenslandCSIRO
  2. 2. Background • Synthesis products key output of TERN • Need robust methods to combine high volume datasets in computationally efficient manner & provide quantitative estimates of uncertainty • Model Data Assimilation: A synthesis analysis of information contained two satellite datasets: 1. Diagnose canopy conductance (gc) 2. Prognose soil moisture (qA & qB) • Only possible by linking TERN AusCover and NCRIS National Computational Infrastructure (ANU) within eMAST Facility
  3. 3. ‘Traditional’ biophysical modeling Vegetation & soil classes Parameters BIAS! 300 250 q 200 m z SEB TS 150 100 50 0 T V TS 300 0 100 200 300 400 500 600 250 q Sm1 Microwave RT TB 200 150 100 50 0 0 100 200 300 400 500 600 M
  4. 4. Model Data Assimilation: (1) Inverse model IceSAT r , n 300 250 200 150 100 50 0 0 100 200 300 400 500 600 Forward Model Veg Height Op RT-1 LAI 300 250 q TS q z  200 m z SEB TS 150 100 50 0 T V TS 300 0 100 200 300 400 500 600 TB qS1  250 q Sm1 Microwave RT TB 200 150 100 50 0 0 100 200 300 400 500 600 H= M-1
  5. 5. Model Data Assimilation: (2) Assimilating observations IceSAT r , n 300 250 200 150 100 50 0 0 100 200 300 400 500 600 Assimilation Forward Model Veg Height Op RT-1 LAI 300 250 q TS q z  200 m z SEB J TS 150 100 50 0 T V TS 300 0 100 200 300 400 500 600 TB qS1  250 q Sm1 Microwave RT J TB 200 150 100 50 0 0 100 200 300 400 500 600 H q za gc q Sa1 0.15 Infiltration/Runoff 0.1 Diagnostics 0.05 0 0 10 20
  6. 6. Gain Matrix • K is a function of model sensitivities and model + observation covariances 1. Provides spatial information on where observations maximally inform model 2. ‘Smears’ information spatially from where observation are to where they are not 3. Allows calculation of prediction errors for analysis variables • H is the ‘tangent linear operator’: sensitivity of model to states/parameters • Two large computational steps: Calculating covariances (H B HT + R) and then inverting it é ù ê ¶H ¶H  ú ê ¶x1 ¶x2 ú ê x1 x1 ú ê ¶H ¶H ú H =ê  ú ê ¶x1 x2 ¶x2 x2 ú ê ú ê    ú ê ú ë û
  7. 7. Covariances: (H B HT + R) Covariance matrix (441 x 441) Spatially correlated grid (21 x 21) 1.9 x 105 elements Exp lag correlation
  8. 8. Error covariance matrix: (H B HT + R)Region: 550 x 460 kmMatrix size:4 x 253,000 x 253,000 Jan 2003 – Dec 2011= 2.56 x 1011 elements5 km grid cells:4 x 10,120 x 10,120= 20,240 x 20,240= 4.10 x 108 elementsComputation:9 years daily data24 hours wall time20 CPU cores+ GPU routines for QRdecomposition to yieldInverse & generate K 20 0 5 10 15 25 30 covariance units (K2)
  9. 9. Error covariance matrix: (H B HT + R)Shows:1. Length scale correl.2. Similar veg behavior3. Info ‘smearing’4. Source analysis errors 0 5 10 15 20 25 30 covariance units (K2)
  10. 10. Inferred canopy conductance based on obs TS & TB Mean gc [Range 0.1 – 11.1 mm/s] s gc [Range 0.01 – 10 mm/s]
  11. 11. Analysis corrections (Qanalysis – Qopen loop) A Horizon [Range 0.14 – 20 mm] B Horizon [Range 0.14 – 20 mm]
  12. 12. Summary • Synthesis products key output of TERN • MDA provides a rigorous mathematical framework to generate synthesis products through the combination of multiple datasets and biophysical models • The move away from classification data to continuous variables reduces bias introduced into biophysical model output leading to better model prediction • TERN and NCRIS infrastructure have provided the mechanism by which computationally intensive synthesis products can be generated from fundamental satellite observations and models

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