• Brown, B. G., and Coauthors, 2004: New verification approaches to convective weather forecasts. Preprints, 11th Conf. Aviation, Range, and Aerospace Meteorology, Hyannis, MA, Amer. Meteor. Soc., 9.4. [Available online at http://ams.confex.com/ams/pdfpapers/82068.pdf.].

  • Browning, K. A., 1982: Nowcasting. Academic Press, 256 pp.

  • Carvalho, L. M. V., and Jones C. , 2001: A satellite method to identify structural properties of mesoscale convective systems based on maximum spatial correlation tracking technique (MASCOTTE). J. Appl. Meteor., 40 , 16831701.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Conway, B. J., 1998: An overview of nowcasting techniques. Proc. SAF Training Workshop Nowcasting and Very Short Range Forecasting, Madrid, Spain, EUMETSAT, 34–43.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., and McBride J. L. , 1998: Routine verification of NWP quantitative verification forecasts for weather systems. Preprints, 12th Conf. on Numerical Weather Prediction, Phoenix, AZ, Amer. Meteor. Soc., J119–J122.

  • Ebert, E. E., and McBride J. L. , 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239 , 179202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., Wilson L. J. , Brown B. G. , Nurmi P. , Brooks H. E. , Bally J. , and Jaeneke M. , 2004: Verification of nowcasts from the WWRP Sydney 2000 Forecast Demonstration Project. Wea. Forecasting, 19 , 7396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feidas, H., and Cartalis C. , 2001: Monitoring mesoscale convective cloud systems associated with heavy storms with the use of Meteosat imagery. J. Appl. Meteor., 40 , 491512.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feijt, A., de Valk P. , and van der Veen S. , 2000: Cloud detection using Meteosat imagery and numerical weather prediction model data. J. Appl. Meteor., 39 , 10171030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gepard GmbH, 2001: CineSat software user manual. Operation Manual 120, Vienna, Austria, 225 pp.

  • Golding, B. W., 1998: Nimrod: A system for generating automated very short range forecasts. Meteor. Appl., 5 , 116.

  • Grams, J. S., Gallus W. A. , Wharton L. S. , Koch S. , Loughe A. , and Ebert E. E. , 2006: The use of a modified Ebert–McBride technique to evaluate mesoscale model QPF as a function of convective system morphology during IHOP 2002. Wea. Forecasting, 21 , 288306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., Rossow W. B. , Guedes R. L. , and Walker A. W. , 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126 , 16301654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mathon, V., and Laurent H. , 2001: Life cycle of the Sahelian mesoscale convective cloud systems. Quart. J. Roy. Meteor. Soc., 127 , 377406.

  • Minnis, P., Garber D. P. , Young D. F. , Arduini R. F. , and Takano Y. , 1998: Parameterizations of reflectance and effective emittance for satellite remote sensing of cloud properties. J. Atmos. Sci., 55 , 33133339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porcú, F., and Levizzani V. , 1992: Cloud classification using METEOSAT VIS-IR imagery. Int. J. Remote Sens., 13 , 893909.

  • Puca, S., De Leonibus L. , Zauli F. , Rosci P. , and Musmanno L. , 2003: Automatic detection and forecast of convective system based on multispectral satellite data (IR window and absorption Meteosat channels) and neural network technique. Proc. Sixth European Conf. on Applications of Meteorology, Rome, Italy, European Meteor. Soc., Paper 101.

  • Rossow, W. B., and Schiffer R. A. , 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smolarkiewicz, P. K., 1984: A fully multidimensional positive definite advection transport algorithm with small implicit diffusion. J. Comput. Phys., 54 , 325362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van der Veen, S., 2003: Assimilation of MODIS cloud products in METCAST. CLOUDMAP2: Report describing the presentation at the Workshop on Assimilation of Moisture Variables for NWP, Norrköping, Sweden, SMHI.

  • Van der Veen, S., 2006: Nowcasting fog and low clouds with Meteosat Second Generation. EUMETSAT Meteorological Satellite Conf., Helsinki, Finland, EUMETSAT. [Available online at http://www.eumetsat.int/Home/Main/Publications/Conference_and_Workshop_Proceedings/groups/cps/documents/document/pdf_conf_p48_s2a_03_vanderve_v.pdf.].

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Verification of an MSG Image Forecast Model: METCAST

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  • 1 Astronomy and Meteorology Department, University of Barcelona, Barcelona, Spain
  • | 2 Royal Netherlands Meteorological Institute, De Bilt, Netherlands
  • | 3 Astronomy and Meteorology Department, University of Barcelona, Barcelona, Spain
  • | 4 Royal Netherlands Meteorological Institute, De Bilt, Netherlands
  • | 5 Astronomy and Meteorology Department, University of Barcelona, Barcelona, Spain
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Abstract

A validation of a very short-range forecast model is presented: the Meteosat Cloud Advection System (METCAST). The model forecasts IR (10.8 μm) images based on Meteosat Second Generation (MSG) data and uses ouput from the Royal Netherlands Meteorological Institute’s [Koninklijk Nederlands Meteorologisch Instituut (KNMI)] NWP model, the High Resolution Limited Area Model (HIRLAM). METCAST advects clouds and takes into account the evaporation–condensation processes in the atmosphere. To assimilate the satellite images into METCAST, an MSG image is converted to a modified image with coarser resolution. The relative performance of METCAST is evaluated, comparing the model results with persistence and a second nowcasting model called CineSat.

Two statistical techniques are used to evaluate the forecasts: (a) the computation of the BIAS, RMSE, and Hanssen and Kuiper (HK) discriminant for a cloud mask selected in the modified and forecast images and (b) the contiguous rain areas (CRAs) technique, which permits a decomposition of the mean-squared error (MSE) of cloud clusters in three components: displacement, intensity, and shape.

Five months of data, from June to November 2006 (August was not available), are used for this study. METCAST BIAS shows poor skill in comparison to CineSat and persistence. METCAST performs better in terms of the RMSE and HK discriminant. The CRA application reveals that although METCAST has a greater MSE volume component than CineSat, its displacement error component is smaller. Two interesting conclusions can be drawn: METCAST performs well when advecting cloudy pixels, but improvement in the atmospheric physics of the nowcast model may be required.

Corresponding author address: Germán Delgado, Dept. D’Astronomia i Meteorologia, Universitat de Barcelona, Diagonal, 647, 08028 Barcelona, Spain. Email: isohipsa@yahoo.es

Abstract

A validation of a very short-range forecast model is presented: the Meteosat Cloud Advection System (METCAST). The model forecasts IR (10.8 μm) images based on Meteosat Second Generation (MSG) data and uses ouput from the Royal Netherlands Meteorological Institute’s [Koninklijk Nederlands Meteorologisch Instituut (KNMI)] NWP model, the High Resolution Limited Area Model (HIRLAM). METCAST advects clouds and takes into account the evaporation–condensation processes in the atmosphere. To assimilate the satellite images into METCAST, an MSG image is converted to a modified image with coarser resolution. The relative performance of METCAST is evaluated, comparing the model results with persistence and a second nowcasting model called CineSat.

Two statistical techniques are used to evaluate the forecasts: (a) the computation of the BIAS, RMSE, and Hanssen and Kuiper (HK) discriminant for a cloud mask selected in the modified and forecast images and (b) the contiguous rain areas (CRAs) technique, which permits a decomposition of the mean-squared error (MSE) of cloud clusters in three components: displacement, intensity, and shape.

Five months of data, from June to November 2006 (August was not available), are used for this study. METCAST BIAS shows poor skill in comparison to CineSat and persistence. METCAST performs better in terms of the RMSE and HK discriminant. The CRA application reveals that although METCAST has a greater MSE volume component than CineSat, its displacement error component is smaller. Two interesting conclusions can be drawn: METCAST performs well when advecting cloudy pixels, but improvement in the atmospheric physics of the nowcast model may be required.

Corresponding author address: Germán Delgado, Dept. D’Astronomia i Meteorologia, Universitat de Barcelona, Diagonal, 647, 08028 Barcelona, Spain. Email: isohipsa@yahoo.es

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