Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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- 30 - Validation of Boundary Layer Parameters and Extension of Boundary Conditions of the Climate Model REMO – Estimation of Leaf Area Index from NOAA-AVHRR-Data Birgit Streckenbach und Eberhard Reimer Institute of Meteorology, Free University of Berlin, Carl-Heinrich-Becker-Weg 6-10, streckba@zedat.fu-berlin.de 1. Introduction At this time the climate model REMO of the Max-Planck- Institute Hamburg works with fixed regional vegetation parameters for every month of the year. To obtain a better adaptation to real conditions the estimation of the annual changes in vegetation, especially deciduous forests, which significantly influence the boundary layer processes and in particular the moisture budget, is necessary. The Leaf Area Index (LAI) is a characteristic indicator for the type of vegetation and its activity. 2. Data and Method At the Institute of Meteorology of the Free University Berlin NOAA-AVHRR data for the European region are available day by day since 1989. The data are corrected to eliminate typical errors referring to calibration and shift in observation time. They are also adjusted to geographic coordinates and to a land mask. On the basis of these data from 1995 until 2002 during the vegetation period (April-September) the LAI in the Baltic region was predicted pixelwise from the Normalized Difference Vegetation Index (NDVI) for 1km x 1km resolution using an algorithm from Sellers et al. (1996). At first 10 day composites of the NDVI were created to eliminate the disturbing influence of cloudiness. Missing or incorrect pixels were completed in time by harmonic analysis and regionally by pixel neighbourhood reconstruction. From these NDVI values the Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) was predicted and then the LAI was determined using a modified USGS land cover classification. Finally the 1km x 1km, 10 day averaged LAI data were converted into the 1/6 degree resolution of the REMO climate model. 3. Results In Figures 1 and 2 the Normalized Difference Vegetation Index and the Leaf Area Index are shown for the second decade of June 2000 in the Western part of the BALTEX region, derived from NOAA-AVHRR data. with a resolution of 1km x 1km.The regional variability of the NDVI values is not very strong.. In difference the LAI shows very high values in the Southern parts of Scandinavia and in the Alps region. The landuse in these region is characterised by agriculture, grassland and deciduous forests. This figure is a good example for the LAI as indicator for the activity of vegetation and biomass production. For the detection of the interannual LAI development and its changes from year to year in dependence of the weather conditions LAI data sets over a period of seven years for the whole BALTEX region were created. They allow to make statistical evaluation and to test the sensitvity of the REMO model against varying vegetation parameters. To validate the used land cover classification ASTER and Landsat7 scenes with a resolution of 250m up to 15m for single days were evaluated and compared with the NOAA data sets (DBS GmbH Mohnen). Figure 1. High resolution NDVI for the Western part of the BALTEX region (June 2000) Figure 2. High resolution LAI for the Western part of the BALTEX region (June 2000) References Sellers, P.J., Los, S.O., Tucker, C.J., Justice, C.O., Dazlich, D.A., Collatz, G.J. and D.A. Randall, A revised land surface parameterization (SiB2) for atmospheric GCMs. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. Journal of Climate, Vol. 9, pp. 706-737, 1996
- 31 - Determination and Comparison of Evapotranspiration with Remote Sensing and Numerical Modelling in the LITFASS Area A. Tittebrand , C. Heret , B. Ketzer and F.H. Berger Institute for Hydrology and Meteorology, University of Dresden (atibrand@forst.tu-dresden.de) 1. Introduction This study is part of the network project EVA_GRIPS. The aim of our subproject is the determination and scaling up of biophysical land surface properties, radiant- and energy flux densities with modelling (LM) and remote sensing data analysis (NOAA-AVHRR, Landsat-7 ETM+) with different spatial resolutions. The area of interest is the LITFASS-area (20 x 20 km 2 ), a highly heterogeneous landscape around the meteorological observatory of the DWD in Lindenberg (50 km southeastern from Berlin). 2. Methods To infer radiant and energy flux densities from a given landscape several methods are possible. First modelling efforts are used. The Lokal-Modell (LM) of the DWD is an non-hydrostatic mesoscale model for Central and Western Europe. It contains 35 layers, 10 layers within 1500 m above model orography, and offers results with a spatial resolution of 7 km. Second simulations with the LM were combined with observations from satellites. On the one hand we want to compare the determined results, on the other hand the satellite data can offer realistic input-parameters for the LM to improve the simulations. The used satellite systems are NOAA-AVHRR and LANDSAT-ETM with different spatial resolution (NOAA-AVHRR: 1 km, LANDSAT-ETM: 30 m). For the utilisation of the NOAA-data the modular and hierarchic analysis scheme SESAT (Berger, 2001) was used, The computation of the Landsat data was realised with similar algorithms. To remark and remove atmospheric effects simulations with radiative transfer codes like LOWTRAN-7 and 6S are necessary. During the Litfass03 field campaign in May/June 2003, which offers validation data, a defect of Landsat’s scan line corrector occurred, so the two cloud free Landsat scenes for this time are currently not available. That is why we chose a golden day (30.05.2003) where at least NOAA-AVHRR results in addition to LM values can be provided. Nevertheless it is advantageously to use Landsat scenes because of the very high spatial resolution and to realise methods for scaling up. So we decided to find a common date for compensation (17.04.2003), for which the fluxes, especially the evapotranspiration, can be computed with a variety of methods (LM, NOAA, Landsat). The results of evapotranspiration, inferred with different spatial resolution could be averaged to the LM-grid-cells (7 x 7 km²) in LITFASS-area. Specific results will be given for the two datasets: first for April 17 (Landsat data available) and second for May 30 (only with the AVHRR dataset and LM results). 3. Results A first demonstration of the evapotranspiration (LE), determined with Landsat data and averaged to the LMresolution for the 17th of April, is shown in Fig 1. a) b) Figure 1: Landsat-LE (a) and averaged (b) to the LM-grid
Page 1 and 2: Fourth Stu
Page 3: Preface The 4 th Study</str
Page 7 and 8: - I - Table of Abstracts Title Auth
Page 9 and 10: - III - Sensitivity in Calculation
Page 11 and 12: - V - Parameter Estimation of the S
Page 13 and 14: - VII - The Realism of the ECHAM5.2
Page 15 and 16: Adam, W. K. .......................
Page 17 and 18: - 1 - Activities of the GEWEX Hydro
Page 19 and 20: eference site archive (Cabauw, Neth
Page 21 and 22: - 5 - Remote Sensing of Atmospheric
Page 23 and 24: minute averages around the satellit
Page 25 and 26: - 9 - Precipitation Type Statistics
Page 27 and 28: - 11 - Assimilation of New Land Sur
Page 29 and 30: Multichannel Microwave Radiometer f
Page 31 and 32: output has been processed in an equ
Page 33 and 34: height dependence of the Z-R-relati
Page 35 and 36: - 19 - CERES and Surface Radiation
Page 37 and 38: - 21 - Coastal Wind Mapping from Sa
Page 39 and 40: - 23 - Clouds and Water Vapor over
Page 41 and 42: - 25 - Broadband Cloud Albedo from
Page 43 and 44: - 27 - Observation of Clouds and Wa
Page 45: shows a ground-track of the vessel
Page 49 and 50: - 33 - Spatial Variability of Snow
Page 51 and 52: - 35 - EVA-GRIPS: Regional Evaporat
Page 53 and 54: - 37 - LITFASS-2003 - A Land Surfac
Page 55 and 56: -39- Calibrated Surface Temperature
Page 57 and 58: - 41 - The Marine Boundary Layer -
Page 59 and 60: - 43 - Characteristics of the Atmos
Page 61 and 62: Figure 2. Surface stress from two d
Page 63 and 64: 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
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Figure 2. Monhly mean values of lat
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In the case of an ideal fit, the co
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- 85 - Influence of Atmospheric For
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The largest inter-annual variabilit
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References Andrejev, O, Sokolov, A.
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On the other hand, if the stratific
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Cyberska 1989, Cyberska and Krzymin
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- 95 - Continental-Scale Water-Bala
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- 97 - ICTS (Inter-CSE Transferabil
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The subsurface flow calculation is
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functions only data with higher qua
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- 103 - Modelling the Impact of Ine
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- 105 - Objective Calibration of th
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- 107 - Validation of Boundary Laye
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- 109 - Simulated Dynamical Process
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spreads along isobaths (fig.2). As
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than the precipitation pattern of J
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Figure 2. Long-term variability in
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obvious from temperature charts. Ho
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shortening of the winter seasons by
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Maximum 5 day precipitation (mm) Nu
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scale parameters the correlation be
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similar: the locations of main mini
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- 127 - Storminess on the Western C
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- 129 - Trends in Wind Speed over t
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- 131 - Wind Energy Prognoses for t
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- 133 - Detection of Climate Change
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Figure 3. Cross section of salinity
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of 35 m/s up to 3 hours. Data on wi
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Figure 2. Time series of Mean Sea L
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- 141 - Calculation and Forecast of
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- 143 - An Overview of Long-Term Ti
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- 145 - Baltic Sea Saltwater Inflow
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- 147 - Significance of Feedback in
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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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