Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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- 100 - Parameter Estimation of the SVAT Schemes TERRA/LM and REMO/ECHAM Using a Multi-Criteria Method K.-P. Johnsen, S.Huneke, J.Geyer and H.-T.Mengelkamp GKSS Research Center, D-21502 Geesthacht, Germany, (johnsen@gkss.de) 1. Introduction The major task of the project EVA-GRIPS funded by the German Ministry of Research and Education is the development of a concept to calculate area averaged evaporation and sensible heat fluxes over heterogeneous land surfaces [1] for one pixel of different NWP models. Known concepts like the MOSAIC approach, the tile approach, the flux coupling approach and the application of effective parameters were implemented into standalone versions of the SVAT schemes TERRA/LM and REMO/ECHAM. The latent and sensible heat fluxes derived from the SVAT schemes are currently compared with measurements of the heat fluxes taken around the Meteorological Observatory Lindenberg of the Deutscher Wetterdienst (DWD) during the three LITFASS campaigns 1998, 2002 and 2003. Here we show results obtained with the effective parameter approach. In order to simulate the heat fluxes with the SVAT schemes it is necessary to estimate appropriate values for all model parameters. Because not all parameters necessary to know for the input can be measured this approach allows to obtain the unknown parameters by minimizing objective functions describing the disagreement between the SVAT schemes and the measurements. 2. Some Results 2.1. Calibration To calibrate the SVAT schemes we applied the multiobjective shuffled complex evolution algorithm MOSCEM-UA [2] to obtain global minima of independent objective functions. Sensible Heat Objective Function 0.14 0.13 0.12 0.11 0.1 0.09 0.08 Objective Space Pareto Rank 1 Pareto Rank 2 0.16 0.17 0.18 0.19 0.2 Latent Heat Objective Function Figure 1: Objective space for the parameter sets of the REMO model with pareto rank 1 and 2 (calibration period). Latent Heat Flux [W/m²] 300 200 100 Latent Heat - Calibration Min. Pareto Rank 1 Max. Pareto Rank 1 observed 0 19.5.03 24.5.03 29.5.03 3.6.03 Time -100 Figure 2: Latent heat flux measurements above barley taken during LITFASS 2003 and compared with minimum and maximum REMO model results for parameter sets with pareto rank 1 (calibration period). As independent objective functions we used a modified Nash-Sutcliff measure (optimum at 0) applied to the differences between the measured and calculated heat fluxes. The algorithm allows to optimize N independent objective functions parallel. Here is N=2 for the latent and sensible heat fluxes. Sensible Heat Flux [W/m²] 400 300 200 100 Sensible Heat - Calibration Min. Pareto Rank 1 Max. Pareto Rank 1 observed 0 19.5.03 24.5.03 29.5.03 3.6.03 Time -100 Figure 3: Sensible heat flux measurements above barley taken during LITFASS 2003 and compared with minimum and maximum REMO model results for all parameter sets with pareto rank 1 (calibration period). The algorithm determines the set of pareto-optimal objective function vectors of rank R. A vector x of objective functions is said to dominate (i.e. has a lower rank than) another objective function vector y if an i exist for all xi with x i
functions only data with higher quality (i.e. quality flag lower than 4) were used. Altogether 9 parameter were optimized. Figure 2 shows the latent heat flux measurements above barley compared with the minimal and maximal latent heat fluxes as modeled with REMO/ECHAM for all parameter sets with rank 1.. Similarly, Figure 3 shows the sensible heat fluxes. 2.2. Validation Latent Heat Flux [W/m²] 300 200 100 Min. Pareto Rank 1 Max. Pareto Rank 1 observed Latent Heat - Validation - 101 - 0 3.6.03 8.6.03 13.6.03 18.6.03 -100 Time Figure 4: Latent heat fluxes for the validation period (see also Figure 3). Figures 4 and 5 show the parameter sets with pareto rank 1 for the barley 2003 dataset applied to the validation period between the 3 rd and the 18 th of June 2003. As shown in Figure 6 the objective functions are increased slightly for the validation period in comparison with the calibration period (Figure 1). Similar results can be obtained by using the TERRA/LM model. In the future the MOSCEM-UA algorithm e.g. will be applied to obtain upper and lower boundaries for heat fluxes for one pixel of an NWP model by using different averaging strategies. Sensible Heat Flux [W/m²] 400 300 200 100 Min. Pareto Rank 1 Max. Pareto Rank 1 observed Sensible Heat - Validation 0 05.06.03 10.06.03 15.06.03 -100 Time Figure 5: Sensible heat fluxes for the validation period (see also Figure 3). Sensible Heat Objective Function 0.195 0.19 0.185 0.18 0.175 0.17 0.165 Objective Space - Validation Pareto Rank 1 0.16 0.32 0.325 0.33 0.335 0.34 0.345 0.35 0.355 0.36 0.365 Latent Heat Objective Function Figure 6: Objective Space for the validation period. References H.-T.Mengelkamp and the EVA-GRIPS-Team: EVA- GRIPS: Regional Evaporation at Grid and Pixel Scale over Heterogeneous Land Surfaces, this issue. J.A.Vrugt, H.V.Gupta, L.A.Bastidas, W.Bouten and S.Sorooshian: Effective and efficient algorithm for multi-objective optimization of hydrologic models, Water Resources Research, 39 (8), art. No. 1214, DOI 10.0129/2002WR001746
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Fourth Stu
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Preface The 4 th Study</str
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- I - Table of Abstracts Title Auth
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- III - Sensitivity in Calculation
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- V - Parameter Estimation of the S
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- VII - The Realism of the ECHAM5.2
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Adam, W. K. .......................
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- 1 - Activities of the GEWEX Hydro
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eference site archive (Cabauw, Neth
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- 5 - Remote Sensing of Atmospheric
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minute averages around the satellit
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- 9 - Precipitation Type Statistics
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- 11 - Assimilation of New Land Sur
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Multichannel Microwave Radiometer f
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output has been processed in an equ
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height dependence of the Z-R-relati
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- 19 - CERES and Surface Radiation
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- 21 - Coastal Wind Mapping from Sa
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- 23 - Clouds and Water Vapor over
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- 25 - Broadband Cloud Albedo from
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- 27 - Observation of Clouds and Wa
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shows a ground-track of the vessel
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- 31 - Determination and Comparison
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- 33 - Spatial Variability of Snow
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- 35 - EVA-GRIPS: Regional Evaporat
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- 37 - LITFASS-2003 - A Land Surfac
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-39- Calibrated Surface Temperature
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- 41 - The Marine Boundary Layer -
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- 43 - Characteristics of the Atmos
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Figure 2. Surface stress from two d
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During the experiment the water was
Page 65 and 66: While the number of smallest drops
Page 67 and 68: SCANDIA will not be supported any l
Page 69 and 70: - 53 - Relationships Between Precip
Page 71 and 72: - 55 - Analysis of the Role of Atmo
Page 73 and 74: - 57 - Meteorological Peculiarities
Page 75 and 76: Baltic Sea Inflow Events Jan Piechu
Page 77 and 78: - 61 - The Different Baltic Inflows
Page 79 and 80: - 63 - Observations of Turbulent Ki
Page 81 and 82: - 65 - The Influence of Synoptic Si
Page 83 and 84: -67- Improved Method for the Determ
Page 85 and 86: -69- The Helicopter-Borne Turbulenc
Page 87 and 88: - 71 - CEOP Reference Site Data fro
Page 89 and 90: - 73 - The BALTEX Hydrological Data
Page 91 and 92: - 75 - Hydrological and Hydrochemic
Page 93 and 94: -77- Sea-level Monitoring at MARNET
Page 95 and 96: 1 0.8 0.6 0.4 0.2 GO(CLD-M) ERA40 S
Page 97 and 98: Figure 2. Monhly mean values of lat
Page 99 and 100: In the case of an ideal fit, the co
Page 101 and 102: - 85 - Influence of Atmospheric For
Page 103 and 104: The largest inter-annual variabilit
Page 105 and 106: References Andrejev, O, Sokolov, A.
Page 107 and 108: On the other hand, if the stratific
Page 109 and 110: Cyberska 1989, Cyberska and Krzymin
Page 111 and 112: - 95 - Continental-Scale Water-Bala
Page 113 and 114: - 97 - ICTS (Inter-CSE Transferabil
Page 115: The subsurface flow calculation is
Page 119 and 120: - 103 - Modelling the Impact of Ine
Page 121 and 122: - 105 - Objective Calibration of th
Page 123 and 124: - 107 - Validation of Boundary Laye
Page 125 and 126: - 109 - Simulated Dynamical Process
Page 127 and 128: spreads along isobaths (fig.2). As
Page 129 and 130: than the precipitation pattern of J
Page 131 and 132: Figure 2. Long-term variability in
Page 133 and 134: obvious from temperature charts. Ho
Page 135 and 136: shortening of the winter seasons by
Page 137 and 138: Maximum 5 day precipitation (mm) Nu
Page 139 and 140: scale parameters the correlation be
Page 141 and 142: similar: the locations of main mini
Page 143 and 144: - 127 - Storminess on the Western C
Page 145 and 146: - 129 - Trends in Wind Speed over t
Page 147 and 148: - 131 - Wind Energy Prognoses for t
Page 149 and 150: - 133 - Detection of Climate Change
Page 151 and 152: Figure 3. Cross section of salinity
Page 153 and 154: of 35 m/s up to 3 hours. Data on wi
Page 155 and 156: Figure 2. Time series of Mean Sea L
Page 157 and 158: - 141 - Calculation and Forecast of
Page 159 and 160: - 143 - An Overview of Long-Term Ti
Page 161 and 162: - 145 - Baltic Sea Saltwater Inflow
Page 163 and 164: - 147 - Significance of Feedback in
Page 165 and 166: Lindström, G., Johansson, B., Pers
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- 151 - Air-Sea Fluxes Including Mo
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- 153 - The Realism of the ECHAM5.2
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- 155 - Figure 3. Accumulated total
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Figure 2. Example for Fourier decom
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Figure1. Modeled percent volume cha
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conditions even more challenging th
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surface heat fluxes close to zero.
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- 165 - Figure 1. Modeled seasonal
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- 167 - Present-Day and Future Prec
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- 169 - Extreme Precipitation on a
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at the stations Pärnu (Estonia) an
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6. Results There are many possible
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and vegetation, and to derive the h
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The structure of water bodies cadas
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Figure 1. The area of Gdańsk subje
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- 181 - Climate and Water Resources
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- 183 - Generating Synthetic Daily
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The evapotranspiration is affected
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Model parameters and coefficients:
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3. Hydrodynamic model of nutrient l
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No. 15: Minutes of 8 th Meeting of
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Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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