Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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In this paper the main aim is to show the meteorological reasons for the occurrence of the extreme precipitation. The question arises whether there is a close connection between the specific type of synoptic situation and the occurrence of extreme rainfalls. References Cebulak Elżbieta, Wpływ sytuacji synoptycznej na maksymalne opady dobowe w dorzeczu górnej Wisły, Folia Geographica, Seria Geographica-Physica, No. 23, pp. 81-95, 1992 Milata Władysław, Synoptyka wielkich opadów atmosferycznych w Karpatach, Przegląd Meteorologiczny i Hydrologiczny, No. 6, pp. 210-217, 1955 Niedźwiedź Tadeusz, Heavy rainfall in the Polish Carpathians during the flood in July 1970, Studia Geomorphologica Carpatho.-Balcanica, No. 6, pp. 194-199 , 1972 Niedźwiedź Tadeusz, Sytuacje synoptyczne i ich wpływ na zróżnicowanie przestrzenne wybranych elementów klimatu w dorzeczu górnej Wisły, Rozprawy Habilitacyjne UJ, No. 58, 1981 Niedźwiedź Tadeusz, Catalogue of synoptic situations in the upper Vistula river basin (1951-1985), Zeszyty Naukowe UJ, Prace Geograficzne, No. 71, pp. 37-86, 1988 - 66 -
-67- Improved Method for the Determination of Turbulent Surface Fluxes Using Low-Level Flights and Inverse Modelling Jens Bange, Thomas Spieß, and Peter Zittel Aerospace Systems, TU Braunschweig, Hermann-Blenk-Str. 23, 38108 Braunschweig - Germany. E-Mail: j.bange@tu-bs.de 1. Introduction The determination of area-averaged vertical turbulent fluxes of heat, humidity, and momentum at surface level from flight measurements in the framework of a joint field experiment is an useful task for several reasons. E.g., the measured area-averaged fluxes act as ground-truth data for remotely sensed data. This ground-truth is also the base for the development of averaging strategies of ground-based point measurements in a heterogeneous terrain. Large eddy simulations (LES) can be initialized or verified, as well as forecast models. The knowledge achieved from the analysis of turbulent fluxes under various terrain and synoptic conditions helps to improve the numerical weather prediction as well as the climate models. Figure . 1 : 3D box flight pattern. 2. Flight Strategies Generally aircraft are very suitable instruments to measure area-averaged turbulent fluxes. For the determination of the mean surface heat flux in a convective boundary layer (CBL), the usual method is to fly square-shaped flight patterns at at least three different altitudes within the CBL (3D-box flights, see Fig. 1).At each flight level z the areaaveraged flux (in this example the vertical flux of sensible heat) is determined by averaging over all four flight legs (straight flight sections). Assuming a linear heat flux profile in the CBL the area-averaged fluxes are then extrapolated to the ground. The drawbacks of this method are obvious:First, flights at minimum three different altitudes are necessary, which consumes time and money. Second, for the time of flights stationarity or at least a linear temporal development of the CBL must be assumed. And third, a linear flux profile through the entire atmospheric boundary layer (ABL) must be assumed, which is not problematic for the heat fluxes in a CBL, but unlikely for momentum and latent heat near the surface and for other types of thermal stratification. Grunwald et al. (1998) introduced the low-level flight method (LLF) to determine the surface fluxes from flights at only one low altitude by solving the budget equation. For the turbulent sensible heat flux H, this is: 1 ∂H ∂θ ⎛ ∂θ ∂θ ⎞ ⋅ = − − ⎜u ⋅ + v ⋅ ⎟ ρ ⋅ cP ∂z ∂t ⎝ ∂x ∂y ⎠ where u and v are the mean horizontal wind velocities, and θ is the mean potential temperature. The LLF strategy consumes less flight time and has therefore big advantages compared to the 3D method. Of course assumptions for the vertical profiles of the fluxes between the surface and the flight level have to be made further on. And the LLF method requires additional measurements with e.g. ground-based systems to receive the horizontal gradients and the temporal development of temperature, humidity, and wind. Therefore with the LLF method alone an airborne system can not be used autonomously but depends on supporting systems. 3. The Inverse Method To obtain a stand-alone procedure the low-level flights were combined with the inverse theory (e.g., Tarantola 1987) to calculate the missing parameters in the budget equations. The inverse modeling technique uses a measured data set dobs of an atmospheric quantity and an assumed model relationship G that describes physical processes of the quantity to reproduce the measured data as a set of parameters m (Wolff and Bange 2000). In other words the technique uses appropriate model assumptions that are based on theoretical assumptions to fit measured data. For the energy budget (2) we assumed a linear relationship (linear operator G) between the model parameters m and the measurements dobs: r d obs = G = m r ( m) 0 r r r r + m x + m y + m z + m t 1 with Cartesian coordinates x, y, z and time t, and r ⎛ ∂d ⎜ ⎝ ∂x ( ) ⎟ m m = ⎜ obs obs K , , K, 2 3 r ∂d ∂t ⎞ ⎠ 4 (1) (2) 1 , 4 (3) For the turbulent sensible heat flux H, the data dobs(x;y; z) represent the measured potential temperature θ. To reproduce the potential temperature, the inverse model was initialized with a realistic range of values of the mean potential temperature gradient and the mean temporal development of θ. Also, the statistical uncertainties of the sensors and the probing strategy were taken into account. The output m of the inverse model then provided the gradient and the temporal development of the mean potential temperature. The vertical gradient of the heat flux was then calculated by inserting the parameters m from the inverse model output into the budget equation (2). Finally the surface heat flux was calculated by integrating (2) assuming a linear profile of H:
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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
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 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: - 65 - The Influence of Synoptic Si
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 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
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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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