Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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Fig. 4: Comparison of measured and simulated latent heat flux above rape during the calibration period during the LITFASS 2003 experiment. Fig. 5: Comparison of measured and simulated sensible heat flux above rape during the calibration period during the LITFASS 2003 experiment. First results show a good agreement between the simulation and the measurement for the calibration period above rape from the 24 th to 31 th May 2003. Figure 4 shows the model calibration against the latent heat flux, Figure 5 the result of the calibration only against the sensible heat flux of the same time period. The different parameter sets of the two calibrations were obtained from the different objective functions. In the future the multi-criteria optimization algorithm MOSCEM (Multi-Objective Shuffled Complex Evolution Metropolis) [3] will be used to solve several objective functions simultaneously to reduce uncertainties in parameter values for the whole system of heat fluxes. References: [1] H.-T. Mengelkamp, K. Warrach and E. Raschke, 1999: SEWAB – a parameterization of the surface energy and water balance for atmospheric and hydrologic models, Adv. Water Res, 23, 2. - 106 - [2] H.-T. Mengelkamp and the EVA-GRIPS-Team: EVA-GRIPS: Regional Evaporation at Grid and Pixel Scale over Heterogeneous Land Surfaces, this issue. [3] K.-P. Johnsen et. al: Parameter estimation of the SVAT schemes TERRA/LM and REMO/ECHAM using a multi-criteria method, this issue.
- 107 - Validation of Boundary Layer Parameters and Extension of Boundary Conditions of the Climate Model REMO – Snow Cover Michael Woldt, Eberhard Reimer Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6-8, 12165 Berlin, Germany. woldt@zedat.fu-berlin.de 1. Introduction The observations of snow cover in the BALTEX region is of special interest in the REMO-model evaluation because of its strong affects on energy balances and dynamics in the boundary layer. For the attempt of spatial field interpolations of the snow cover and depth, the three-hourly standard measurements of hundreds of synoptic stations within the BALTEX region have been used. The data are provided by the national weather services and are received by the Meteorological Institute at the FU Berlin from the Deutsche Wetterdienst. The resulting interpolations have been compared with the output-parameter “snow water equivalent” of the REMOmodel for the period 1999-2001. 2. Data and Methods The field interpolation of the snow cover and snow depth is based on the standard measurements of about one thousand meteorological synoptic stations. Not all station were available for the whole period, but at leased 700 station could always be used. First analyses showed numerous and different errors in the data, resulting from observation and coding mistakes, disturbances during data transmissions and processing errors. To achieve reliable data, multiple error detection methods have been developed to sort out erroneous data. Synoptic stations only report snow depths but not that there is no snow. So, to distinguish between stations with no snow cover and missing observations, analysis schemes have been developed, using other synoptic parameters, like observed weather, state of ground, surface and air temperature. Additionally it was tried to determine probabilities for each observation time for snow cover or no snow cover. The snow measurements have then been interpolated for the three-hourly synoptic observation times, using weighted interpolation, if possible. So 12 years (1990-2001) of snow cover time series were determined for each available synoptic station in the BAL- TEX area. In a first step, these data were used to interpolate daily snow covers and snow depths on a grid of 1/60° to 1/120° (about 1 km to 1 km). During the interpolation process for each box of the grid, the distance to the synoptic stations and their differences in height above sea level were taken into account. Up to eight synoptic stations nearest to the boxes (within a given range) are used for interpolation. Stations with too large differences in height to the boxes were ignored. Only if no stations could be found within the first range, it was extended in several steps, up to a given maximum. There were only a few areas where, at certain times, no interpolation could be calculated due to the lack of synoptic stations. The resulting interpolated fields were then used for testing the REMO-model for the period 1999-2001. To compare the interpolated snow depth data with REMO, they first had to be converted to the 1/6° grid used by REMO. Like most models REMO gives no snow depth, but the snow water equivalent. That is the thickness of the water layer that would result, if all snow is melted. It depends on the density of the snow, which is variable. Snow water equivalent Snow density = ---------------------------------- Snow depth The snow density can range from about 0.05 for new snow at low temperatures to about 0.5 for old snow after gravitational settling, wind packing, melting and recrystallization (NARC). Since both parameters can not be compared directly, snow densities were calculated by using the snow water equivalent from the REMO-model and the snow depth from the interpolation. If both are in a comparable range, a sensible snow density will result. (Correct results are, of course, also determined, if both show no snow). 3. Results The daily snow densities have been added and analyzed for months, seasons and years. Generally they show good compliances between the REMO-model output and the interpolated snow cover for larger snow depths and long lasting snow covers. References NARC, What is Snow Water Equivalent?, http://www.or.nrcs.usda.gov/snow/about/swe.html
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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
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While the number of smallest drops
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SCANDIA will not be supported any l
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- 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 and 116: The subsurface flow calculation is
Page 117 and 118: functions only data with higher qua
Page 119 and 120: - 103 - Modelling the Impact of Ine
Page 121: - 105 - Objective Calibration of th
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
Page 167 and 168: - 151 - Air-Sea Fluxes Including Mo
Page 169 and 170: - 153 - The Realism of the ECHAM5.2
Page 171 and 172: - 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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