Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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- 156 - Classification of Precipitation Type and its Diurnal Cycle in REMO Simulation and in Observations Andi Walther 1 , Ralf Bennartz 1, 2 , Daniela Jacob 3 and Jürgen Fischer 1 1 Institut für Weltraumwissenschaften, Freie Universität Berlin,Carl-Heinrich-Beckerweg 6-10, D-12165 Berlin, Germany 2 Atmospheric & Oceanic Sciences, University of Wisconsin, Madison, WI 3 Max Planck Institut für Meteorologie, Hamburg, Germany E-mail: andi.walther@wew.fu-berlin.de 1. Introduction The overall objective of the BALTIMOS project is the development of a coupled model system for the Baltic Sea and its catchment basin in order to understand and model exchange processes between atmosphere, sea, land surface, and lakes including hydrology. BALTIMOS is a contribution to the BMBF research program DEKLIM. Observations and modeling of spatial and temporal variability of precipitation are an important factor for understanding the water cycle. Sumner (1988) classifies precipitation as an expression of origin and considered three main types: convectional, cyclonic and orographic. One focal point of this presentation is the application of a method to divide precipitation in frontal and non-frontal fraction. Models simulate the diurnal cycle of precipitation often wrongly. Operational models tend to produce the maximum of precipitation over land at about local noon, corresponding to the time of maximum heating (Trenberth et al. 2003). Observations have shown that this timing is a few hours before. One further goal of this study is to compare the diurnal cycle of REMO simulations with observations from radar data. 2. Regional model REMO The regional climate model REMO that will be used in this study is developed by the Max Planck Institut for Meteorology (MPI) in Hamburg (e.g. Jacob and Claussen,M.. (1995)). It is a three-dimensional, hydrostatic atmosphere model, that is based on the DWD physical parameterization model. The horizontal resolution is 1/6° or in a grid size equal of about 18 x 18 km². Our investigations were processed in the “climate” modus. 3. Radar Data Source The primary data used in our study are the BALTRAD composite radar reflectivity data set. The properties of this product include 2 km spatial grid with 15 minutes temporal resolution and 8-bit dBZ converter. The BALTRAD data are gauge-adjusted and can be transformed into rain intensity information with two fixed seasonal-dependent Z-R relations. This procedure was undertaken by the Swedish Weather service (SMHI) and introduced by Michelson et al. (1999). In order to compare with REMO output it was necessary to transform the data from a 2 km spatial grid into REMO grid (1/6 ° - about 18 km spatial resolution). The rain rates of all BRDC-pixel, rain or non-rain pixel inside a REMO pixel are averaged. 4. Frontal/Non-frontal Distinction The main attribute is to classify contiguous precipitation systems in contrast to classification of each individual pixel in other investigations (e.g. Steiner et al. (1995)). That means, that each pixel of a contiguous rain area gets a common classification, frontal or non-frontal. Threshold for rain pixel was defined as 0.2 mm/hr. Any rain areas smaller than 4,000 km² are initially considered, by virtue of their space scale, to be non-frontal. Larger echoes are subject of a classification algorithm with help of an artificial neural network (ANN) algorithm. Input parameters of the ANN are 9 texture and shape information. Texture information is a result of statistical analysis of spatial rain rate differences and gives a hint of homogeneity and the inner structure of the rain field. Shape parameters are the size of the major axis, the eccentricity and the size of the area. 400 scenes from the available dataset of BALTRAD network were randomly selected to classify manually with help by weather reanalysis maps. Both, the calculated parameters of a rain area, and the corresponding classification represent one vector of the training dataset of the ANN. The resulting algorithm are applied to BALTRAD as well as REMO products. Figure 1 shows the fraction of frontal rain events for BALTRAD data in 2000. Figure 1. Fraction of frontal rain events of all rain events in 2000. Incoherence in Middle Sweden and close to Stockholm is due to bad quality of radar data at individual radar sites. 5. Diurnal Cycle Diurnal variations were analyzed by Fourier decomposition. For expediency, both, the radar and REMO products were grouped by the hour. Instead of using the rain rate and averaging, the final field is the fraction of time that a rain intensity exceeds a threshold of 0.2 mm/hr at each grid point. The zeroth and first components of the discrete Fourier decomposition corresponds to the daily mean and the diurnal cycle of precipitation. This procedure was undertaken for all precipitation events as well as separated in precipitation type.
Figure 2. Example for Fourier decomposition on two grid points for non-frontal events in radar data in nine summer months from 2000 to 2002. Curves with symbols are normalized occurrence of rain events grouped by hour. Curves without symbols are the first harmonics. Solid lines represent a grid point above land, the dash-dot line above sea. Figure 3. Local solar time for peak of first harmonic derived from hourly time series of percentage time with rain rate >= 0.2 mm/hr for nine summer months of three years (2000-2002) in Radar data. Note that blue and red color stand both for nocturnal peaks. - 157 - Figure 4. Same as Figure 3 for REMO output Figure 2 shows an example for non-frontal events in radar data for one pixel above sea and one above land. Typical features are a strong diurnal variability above land with a maximum in the afternoon hours and a less strong variability above sea with a nocturnal maximum. Figures 3 and 4 illustrate the time point of diurnal maximum derived from the phase of the first harmonic for each grid point in BALTRAD and REMO data irrespective of precipitation type. While the average time point of the peak in REMO over land is more than 1 hour ahead of the radar observations, the general pattern are in a good agreement. References Jacob, D., and M. Claussen, REMO-A Model for Climate Research and Weather Forecast, First <strong>Study</strong> <strong>Conference</strong> on BALTEX (ed. A.Omstedt). International BALTEX Publication No.3, International Baltex Secretariat, GKSS-Research Center. P.O. Box 1160 D- 21494 Michelson, D., Andersson, T., Koistinen, J., Collier, C., Riedl, J., Szturc, J., Gjertsen, U., Nielsen, A., and Overgaard, S., BALTEX Radar Center Products and their Methodologies. RMK 90, SMHI, SE-60176 Norrköping, Sweden. 76pp., 2000 Steiner, M., Houze JR, R. A., and Yuter, S., Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data., Journal of Applie Meteorology, 34, pp. 1978- 2007, 1995 Sumner, G., Precipitation Process and Analysis, John Wiley & Sons Chichester, 1988 Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B., The changing character of precipitation, Bulletin of American Meteorological Society, 84, pp. 1205-1217, 2003
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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
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- 55 - Analysis of the Role of Atmo
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- 57 - Meteorological Peculiarities
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Baltic Sea Inflow Events Jan Piechu
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- 61 - The Different Baltic Inflows
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- 63 - Observations of Turbulent Ki
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- 65 - The Influence of Synoptic Si
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-67- Improved Method for the Determ
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-69- The Helicopter-Borne Turbulenc
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- 71 - CEOP Reference Site Data fro
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- 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
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
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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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Page 169 and 170: - 153 - The Realism of the ECHAM5.2
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Page 177 and 178: conditions even more challenging th
Page 179 and 180: surface heat fluxes close to zero.
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Page 183 and 184: - 167 - Present-Day and Future Prec
Page 185 and 186: - 169 - Extreme Precipitation on a
Page 187 and 188: at the stations Pärnu (Estonia) an
Page 189 and 190: 6. Results There are many possible
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Page 193 and 194: The structure of water bodies cadas
Page 195 and 196: Figure 1. The area of Gdańsk subje
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Page 199 and 200: - 183 - Generating Synthetic Daily
Page 201 and 202: The evapotranspiration is affected
Page 203 and 204: Model parameters and coefficients:
Page 205: 3. Hydrodynamic model of nutrient l
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Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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