Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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3. Water Vapor Results The present water vapor product is a satellite-only product, based on operational ATOVS (Advanced TIROS observation of vertical sounding) products of NOAA satellites distributed by GTS (SATEMs). It describes the total precipitable water or integrated water vapor from the surface up to 300 hPa given in mm water column corresponding to the amount of water if all water vapor of the unit column would fall out as precipitation. In this case a simple merging of all available NOAA satellites was applied and missing grid points were interpolated daily by an inverse distance method (Shepard 1968; Willmot et al. 1984). Monthly averages from gridded daily values have been developed as a preliminary product for the Satellite Application Facility on Climate Monitoring of EUMETSAT (Woick et al. 2000). In near future, this product will be replaced by a new humidity composite product, derived by merging of data from various satellite data sources. In Fig. 3 monthly averages of the water vapor amount for January, April, July and October 2001-2003 over Europe are shown. Figure 3. Monthly averages for January, April, July and October of the precipitable water over Europe for the years 2001-2003 in a 1° regular grid resolution. Colours represent 3mm intervals (see legend below). Main influence on the precipitable water has the air temperature. Thus a clear dependency on the latitude can be seen with increasing values from north to south and a clear seasonal course with highest values in July and August and lowest values in January and February. Differences between the years correspond to the humidity advection and temperature distribution within the individual months. - 24 - 4. Conclusions First results of the products of the new DWD satellite based climate monitoring programme reveal some basic patterns of climate variability over Europe and the North Atlantic, and in particular also over large catchments like the Baltic Sea area. Especially the averaged cloud cover patterns show the variability of large-scale circulation processes and orographical impacts, while the water vapor is influenced mainly by the latitudinal temperature distribution. After realization of the operational monitoring of the remaining climate elements mentioned above, some of the climate variability might be explained by special processes of energy transformation and thus a physical explanation of climate variations in various scales might be possible. References Bissolli, P., Nitsche H., Dittmann, E., Müller- Westermeier, G., Rosenow, W., SAT-KLIM: the satellite climate monitoring programme of the German Meteorological Service, Proceedings of the 2002 EUMETSAT Met. Sat. Conf., EUMETSAT Publ., EUM P 36, pp. 661-668, 2002. Rosenow, W., Güldner, J., Spänkuch, D., The “Satellite Weather” of the German Weather Service – an assimilation procedure with a spectral component, Proceedings of the 2001 EUMETSAT Met. Data User’s Conf., EUMETSAT Publ., EUM P 33, pp. 541- 545, 2001. Shepard, D., A two-dimensional interpolation function for irregularly-spaced data, Proc. 23rd National <strong>Conference</strong> ACM, ACM, pp. 517-524, 1968. Willmot, C.J., Rowe, C..M., Philpot, W.D., Legates, D.R., A Program for Interpolation and Contouring Geographic Data, University of Delaware, Center for Climatic Research, 1984. Woick, H., Wollenweber, G., Karlsson, K.G., Feijt, A., Gratzki, A., Hollmann, R., Laine, R., Dewitte, S., Löwe, P., Nitsche, H., The SAF on Climate Monitoring; objectives and concept, Proceedings of the 2000 EUMETSAT Met. Sat. Data User’s Conf., EUMETSAT Publ., EUM P 29, pp. 609-612, 2000.
- 25 - Broadband Cloud Albedo from MODIS Anja Hünerbein, Rene Preusker, Jürgen Fischer Institut für Weltraumwissenschaften, Freie Universität Berlin, Germany Carl-Heinrich Becker Weg 6-10, D-12165 Berlin, e-mail: anja.huenerbein@wew.fu-berlin.de 1. Introduction One of the significant components of Earth’s energy balance is the cloud-reflection of incoming solar radiation back to space. The radiative budged is modified by the components of the Earth-atmosphere system such as clouds, aerosols and the surface. Accurate global observation of top of the atmosphere (ToA) radiative flux is crucial for improving global climate models and for the understanding of climate processes. An algorithm of the estimation of broadband albedo above clouds is developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) King et al. (1992). 2. Materials and Methods Basic idea is to use a forward model to get the upward flux at ToA and a inverse model to get a relation between the measured MODIS radiance and the simulated upward flux. The forward model is based on a radiative transfer model (MOMO) Fell and Fischer (2001) and for the inverse model a neural network is used. Besides the physical properties of the cloud like stratification, particle radius, cloud height and thickness the ToA albedo depends on several additional parameters such as ground reflection, aerosol and water vapour. The radiative transfer model MOMO is used for the radiative properties to receive upward radiance at ToA. The model assumes a plan parallel atmosphere, however any vertical inhomogeneity and media of any optical thickness as well as any spectral resolution can be considered. The simulations are monochromatic. The spectrum (200-3700nm) of the solar is divided in 183 individual narrow bands. The simulated monochromatic upward radiance is integrated over wavelength and weighted with incoming radiance at ToA to receive the upward flux. Arbitrary chosen cases are simulated to represent natural variability of the atmosphere. Thereafter the model is used to simulate the radiance in the short-wave MODIS channel for each defined case. The simulated dataset is used for neural network training. The input parameters are the spherical angles, surface type, water vapour and the radiance of the MODIS channels. The artificial neural network estimates the upward flux at ToA on a pixel by pixel basis. 3. Results Clouds and Earth’s Radiant Energy System (CERES) NASA (2004) also onboard the Terra platform is chosen to compare the results of the developed algorithm. CERES data is processed by the ERBE-like Inversion Subsystem to provide estimates of radiant flux at the ToA. The spatial resolution of CERES is 20 km, while MODIS has a 1km resolution. That is why CERES images like figure1 has smoother pattern than MODIS’ images. A close look is given in figure 2, where one cross section is shown. MODIS has a higher variability than CERES, but the values are in the same range. Comparison has shown the results being reasonable. This method allows a rapid processing of satellite data and will be incorporated into the near-real-time processing at the Frei Universität Berlin. Figure 1. On the left sight: neural network derived cloud albedo [W/m²] for MODIS and on the left sight: upward short-wave radiance [W/m²] from CERES for the 16.06.2002 at 9.32 am. Figure 2. Cross section from figure 1 for latitudes 54°, red line is upward short-wave radiance [W/m²] of CERES, black line is cloud albedo [W/m²] from MODIS References Fell, F., and J. Fischer, Numerical simulation of the light field in the atmosphere-ocean system using the matrixoperator method, J. Quant. Spectroscop. Radiat. Transfer, Vol. 69, pp. 351-388, 2001 King, M. D., Y. J. Kaufman, W.P. Menzel and D. Tanré, Remote sensing of cloud, aerosol, and water vapour properties from the Moderate Resolution Imaging Spectroradiometer (MODIS), IEEE Trans. Geosci. Remote. Sensing, Vol.30, 1992 NASA, Langley Atmospheric Science Data Hierarchical Data Format Web site, URL = http://eosweb/HBDOC/hdf.html, 2004
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: - 23 - Clouds and Water Vapor over
Page 43 and 44: - 27 - Observation of Clouds and Wa
Page 45 and 46: shows a ground-track of the vessel
Page 47 and 48: - 31 - Determination and Comparison
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
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- 75 - Hydrological and Hydrochemic
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-77- Sea-level Monitoring at MARNET
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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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