Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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- 50 - MESAN Mesoscale Analysis of Total Cloud Cover Günther Haase and Tomas Landelius Swedish Meteorological and Hydrological Institute (SMHI), S – 60176 Norrköping, Sweden, e-mail: gunther.haase@smhi.se 1. Introduction The real-time Mesoscale Analysis System (MESAN) has been designed to provide objective and spatially continuous fields of parameters relevant in meteorology and hydrology: precipitation, 2 m temperature and humidity, wind at 10 m level, visibility, and clouds (Häggmark et al. 2000). This is conducted hourly at a spatial resolution of 0.2° covering the BALTEX (Baltic Sea Experiment) area (Fig. 1). Figure 1. MESAN domain. The basis of the analysis system is the optimal interpolation technique. Input data to MESAN are surface and METAR observations, weather radar and satellite imageries, numerical weather prediction (NWP) model fields, physiographic data, and climate-related information. Users of MESAN information are e.g. forecast meteorologists, a hydrological model, and commercial interests. This paper aims to present the most recent developments of total cloud cover analysis within MESAN. 2. Total Cloud Cover Analysis Cloud cover information is received from different sources: manned and automatic stations, METAR observations, geostationary (METEOSAT) and polar (NOAA) satellites. As first guess, HIRLAM (High Resolution Limited Area Model; Undén et al. 2002) forecasts with the same spatial resolution as MESAN are used. a) current status Beside direct measurements, total cloud cover is estimated from the IR-channel on METEOSAT and NOAA, respectively. When the difference between MESAN’s 2 m temperature and the brightness temperature (effective radiation temperature of a black body) measured by the satellite is greater than two standard deviations from the mean difference between the two temperatures during cloud free situations, it is assumed that clouds are present (Häggmark et al. 2000). Inversions are treated separately. Additionally, total cloud cover estimates are derived from the operational cloud classification scheme (CCS) SCANDIA (Karlsson 1996, 2003). This is a multi-spectral scheme based on NOAA AVHRR (Advanced Very High Resolution Radiometer) data which utilizes the horizontal structure of clouds to distinguish between different cloud types. Since SCANDIA has problems for low sun elevations it is not used when the sun angle is between 2 and 6 degrees and in cases with very skew observation angles. b) recent developments In June 2003 a new project called MESAN-Y was initiated at SMHI in order to improve the precipitation and cloud analysis. This paper will only address the latter. A novel CCS has been developed within EUMETSAT’s SAFNWC (Satellite Application Facility to support Nowcasting and Very Short Range Forecasting) project (Dybbroe and Thoss 2003; Dybbroe et al. 2003a, 2003b). The scheme, which became recently operational, is based on AVHRR data as received from the current NOAA (15 and onwards) and future satellites. The main output of the cloud type product is summarized in Tab. 1. Table 1. Cloud types as defined within the EUMETSAT SAFNWC project. 0 Non-processed 1 Cloud free land 2 Cloud free sea 3 Land contaminated by snow 4 Sea contaminated by snow/ice 5 Very low stratiform clouds (include fog) 6 Very low cumuliform clouds 7 Low stratiform clouds 8 Low cumuliform clouds 9 Medium level stratiform clouds 10 Medium level cumuliform clouds 11 High stratiform clouds 12 High cumuliform clouds 13 Very high stratiform clouds 14 Very high cumuliform clouds 15 High semitransparent very thin cirrus 16 High semitransparent thin cirrus 17 High semitransparent thick cirrus 18 High semitransparent cirrus above low or medium level clouds 19 Fractional clouds (sub-pixel water clouds) 20 Undefined Concerning total cloud cover estimates for MESAN, there are three major benefits for replacing the SCANDIA CCS by the new SAFNWC products: i) the spatial resolution of the new CCS (1 km) is much higher, ii) the new CCS includes so called quality flags (Tab. 2), which indicate for each pixel its reliability and iii) the SAFNWC products will be available for future satellite generations whereas
SCANDIA will not be supported any longer. Furthermore, all SAFNWC products are stored in the HDF5 data format. Table 2. Cloud type quality flags as defined within the EUMETSAT SAFNWC project. 0 Land 1 Coast 2 Night 3 Twilight 4 Sunglint 5 High terrain 6 Low level inversion 7 NWP data present 8 AVHRR channel missing 9 Low quality 10 Reclassified after spatial smoothing 11 Stratiform – cumuliform separation In a first step, we estimated the total cloud cover using the observed cloud types (Tab. 1) and quality flags (Tab. 2) as weights. This is done separately for each satellite swath within a time window of 30 minutes around each analysis time. 3. Future plans In the next stage, cloud classified MSG (METEOSAT Second Generation) imagery will be applied as well. Since its spatial resolution and its quality characteristics are completely different compared with the NOAA products, a weighting algorithm has to be developed in order to combine both data sources in time and space. In this context, the observed cloud types and quality flags will become important. Afterwards, a validation study against daytime observations at manned stations is planned. Automatic stations underestimate total cloudiness in some situations because they can not measure clouds over 3800 m. By means of the new total cloud cover product erroneous measurements can easier be detected and eliminated. Finally, it is assumed that SAFNWC data will also help improving the quality of other MESAN cloud parameters as well (e.g. cloud cover of low, medium, and high level clouds; cloud base and top height). References Dybbroe, A., Karlsson, K.-G. and Thoss, A., NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling — part one: Algorithm description, J. Appl. Meteor., submitted, 2003a. Dybbroe, A., Karlsson, K.-G. and Thoss, A., NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling — part two: Tuning and Validation, J. Appl. Meteor., submitted, 2003b. Dybbroe, A. and Thoss, A., Scientific user manual for the AVHRR/AMSU cloud and precipitation products of the SAFNWC/PPS, 2003. Available from SMHI, S – 60176 Norrköping, Sweden (http://www.smhi.se/saf). Häggmark, L., Ivarsson, K.-I., Gollvik, S. and Olofsson, P.- O., Mesan, an operational mesoscale analysis system, Tellus, 52A, 2—20, 2000. - 51 - Karlsson, K.-G., Cloud classifications with the SCANDIA model, Reports Meteorology and Climatology, 67, 1996. Available from SMHI, S – 60176 Norrköping, Sweden. Karlsson, K.-G., A 10 year cloud climatology over Scandinavia derived from NOAA Advanced Very High Resolution Radiometer imagery, Int. J. Climatology, 23, 1023—1044, 2003. Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo, J., Cats, G., Cuxart, J., Eerola, K., Fortelius, C., Garcia- Moya, J. A., Jones, C., Lenderlink, G., McDonald, A., McGrath, R., Navascues, B., Nielsen, N. W., and V. Ødegaard, V., Rodriguez, E., Rummukainen, M., and R. Rõõm, R., Sattler, K., Sass, B. H., Savijärvi, H., Schreur, B. W., Sigg, R., The, H., and Tijm, A., HIRLAM-5 Scientific Documentation, 2002. Available from SMHI, S – 60176 Norrköping, Sweden.
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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
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 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: While the number of smallest drops
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
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
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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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