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Single Column Modeling: Sensitivity Analysis, Parameter Estimation, and Land-Atmosphere Interactions Yuqiong LiuUniversity of Arizona, USA Hoshin GuptaUniversity.

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Presentation on theme: "Single Column Modeling: Sensitivity Analysis, Parameter Estimation, and Land-Atmosphere Interactions Yuqiong LiuUniversity of Arizona, USA Hoshin GuptaUniversity."— Presentation transcript:

1 Single Column Modeling: Sensitivity Analysis, Parameter Estimation, and Land-Atmosphere Interactions Yuqiong LiuUniversity of Arizona, USA Hoshin GuptaUniversity of Arizona, USA Luis BastidasUtah State University, USA Soroosh SorooshianUniversity of California, Irvine, USA GABLS/GLASS Workshop De Bilt, The Netherlands, September 19-21, 2005

2 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , “Systems” Description of a Model Model Structure M X t = f x ( X t-1, , I t-1 ) O t = f o ( X t,  ) I Inputs X State Variables Outputs O  Parameters (time-invariant properties of the system) XoXo Initial States (Time-variant quantities) B.C. Each model component has some uncertainty associated with it … Parameter Estimation

3 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Parameter Estimation Real World Model ({  }) Measured Inputs Measured Outputs Computed Outputs t YtYt Error  Parameter Tuning Parameter Optimization {  }: Parameters Parameter Sensitivity Analysis Parameter Estimation

4 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Context TOA Radiation Land Surface Model (www.arm.gov) A Single Column Model Single Column Model

5 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Single Column Model (SCM) ATMO ( , x A ) LAND ( , x L ) PRLRL RSRS EE HRSRS RLRL B. C. R TOA Everything may have an error in it: , , X A, X L, B.C. ….

6 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Biophysical fluxes radiative transfer sensible/latent heat stomatal physiology momentum flux soil heat/snow melt temperatures Column hydrology interception throughfall/stemflow snow hydrology infiltration/surface runoff soil water redistribution capillary rise/drainage Integrated surface albedo direct albedo (VIS, NIR) diffuse albedo (VIS, NIR) Dry convection Dry adiabatic adjustment on T, q Moist convection Deep conv. (zhang- McFarlane, 1996) shallow/mid-level conv. (Hack, 1994) => Convective precip Large-scale condensation super-saturation => largescale precip Cloud fraction convective cloud - convective moisture flux (high+mid)layer clouds - brunt-vaisalla frequency q threshold low clouds - fixed q threshold (0.9 or 0.8) => Total cloud Radiation solar (VIS+NIR) -  -Eddington scheme long wave radiation - absorbtivity and emissivity  - direct solar (VIS, NIR) - diffuse solar (VIS,NIR) - downward longwave DRIVER Initialization u, v, T, q etc. If nstep=1 surface pressure atm pressure wind (u, v) temperature specific humidity convective precip. large-scale precip. downward longwave bottom layer height direct incident solar (VIS, NIR) diffuse incident solar (VIS, NIR) latent/sensible heat water vapor flux momentum flux emitted longwave direct albedo (VIS, NIR) diffuse albedo (VIS, NIR) Vertical diffusion and boundary layer process Rayleigh friction (u,v tendency) Gravity wave drag - u, v, T tendencies Semi-Lagrangian transport for vertical advection forecast (u, v, T, q) nstep +1 MODEL – NCAR SCM

7 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Observations P: precipitation R net  : net surface radiation E: latent Heat H: Sensible Heat T a : Air temperature q a : air specific humidity T g : ground temperature S: Soil moisture Boundary Conditions 85%: Crops %15: bare ground DATA – ARM SGP IOP Datasets

8 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Impacts of Land-Atmosphere Interactions observation default Ground Temperature Sensible Heat Latent Heat  default: simulation with the default parameter set calibrated  calibrated: simulation with an optimal parameter set from multi-objective calibration Sensible Heat Latent Heat Offline Coupled

9 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Part I: Parameter Sensitivity Analysis (SA) Purpose: Understand model behavior with respect to each parameter Reduce dimensionality of parameter space to facilitate parameter optimization Two-step process: One-at-A-Time (OAT) independent analysis for first screening multi-parameter, multi-objective analysis for further reduction

10 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , One-at-A-Time (OAT) Independent SA # of Parameters (59  40): Land 45  32 Vegetation: 12 Soil: 16 Init. Soil water: 4 Atmo 14  8 convection: 4 clouds: 4

11 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Multi-parameter, Multi-objective Sensitivity Analysis Multi-Objective Generalized Sensitivity Analysis (MOGSA) 3 Cases to explore the impacts of land-atmo interactions: offline LSM (32 land par) coupled SCM (32 land par only) coupled SCM (32 land par + 8 atmo par = 40) RMSE of latent heat, sensible heat, and ground temperature 4 kinds of sensitivities: Multi-criteria E H Tg Parameters Objectives Algorithm Calculates both multi-objective and single-objective sensitivities

12 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Multi-parameter, multi-objective SA results (1) Multi-Criteria LatentHeat SensibleHeat GroundTemp. Vegetation Parameters LSM SCM (32 par) SCM (40 par)

13 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Multi-parameter, multi-objective SA results (2) Soil Parameters Multi-Criteria LatentHeat SensibleHeat GroundTemp. LSM SCM (32 par) SCM (40 par)

14 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Multi-parameter, multi-objective SA results (3) Initial Soil Moisture Multi-Criteria LatentHeat SensibleHeat GroundTemp. LSM SCM (32 par) SCM (40 par)

15 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Multi-parameter, multi-objective SA results (4) Atmospheric Parameters Multi-Criteria LatentHeat SensibleHeat GroundTemp. SCM (40 par)

16 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Part I Summary land-atmosphere interactions may have significant influences on parameter sensitivities Parameter sensitivities depend on the flux/variable being analyzed (H, E, Tg, or multi-criteria) For vegetation parameters, land-atmosphere interactions seem to have much less influence on H than E and Tg Sensitivities of soil thickness and initial soil water decrease with depth into soil Some atmospheric parameter are very sensitive and should be considered in coupled calibration studies Sensitivity Analysis

17 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Part II: Parameter Optimization  31 parameters (23 land, 8 atmosphere) to be optimized  Calibration case design: which parameters and fluxes/variables to focus on  Use a modified Shuffled Complex Evolution algorithm for multi-objective, multi-parameter optimization

18 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Calibration case design Objectives Land fluxes/variables ( E, H, Tg) FLFL cases Atmospheric forcing variables (Pcp, Rnet, Ta) FAFA Both ( E, H, Tg) and (Pcp, Rnet, Ta) F LA Parameters Land parameters (  L, 23) LL cases Atmo. parameters (  A, 8) AA Both land and atmo parameters (  LA, 31)  LA FLLFLL FLAFLA F L  LA F L  L, F L  A, F L  LA, F L  LA S, F L  LA P F A  L, F A  A, F A  LA, F A  LA S, F A  LA P F LA  L, F LA  A, F LA  LA, F LA  LA S, F LA  LA P 15 cases in total: Opt.  L offline first,then opt.  A in the SCCM S Step-wise Decouple Pcp and Rnet, opt.  LA P Partially decoupled

19 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Calibration – Land surface parameters Default F L only F A onlyF L & F A

20 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Calibration – atmospheric parameters Default F L only F A onlyF L & F A

21 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Calibration – Objective Function Values Normalized by default (a priori) RSM errors  L  A  LA  LA S  LA P F L F A F LA

22 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Latent Heat (W/m 2 ) Sensible Heat (W/m 2 ) Ground Temp (K) Calibration – Time series, land surface

23 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Precip. (mm/day) Net Radiation (W/m 2 ) Air Temp (K) Calibration – Time series, atmosphere

24 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Part II Summary Parameter Optimization Including atmospheric parameters can greatly reduce the errors Including atmospheric variables can help make the parameter sets better converged Calibration results are best for ground temperature, worst for sensible heat Step-wise calibration scheme works well Decoupling Precipitation and net radiation greatly improves the calibration results

25 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Other parameter estimation method Using ensemble-based data assimilation methods by recasting parameters as state variables: Potential concerns: l Constant parameters are adjusted instantly/frequently l Lead to unstable model simulations l Difficult to apply to complex, dynamical models l Not suitable to real-time applications

26 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Model calibration for parameter estimation – Pros & Cons Parameters are time-invariant properties (i.e., constants) of the physical system … l Traditional Model Calibration methods ülong-term systematic errors properly corrected üParameter uncertainties considered ûState uncertainties ignored ûEstimated parameters could be biased if substantial state and observational errors

27 GABLS/GLASS Workshop De Bilt, The Netherlands Sept , Future work: combine parameter optimization & state estimation Outer loop: parameter calibration Inner loop: ensemble state assimilation M


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