• Baldauf, M., A. Seifert, J. Förstner, D. Majewski, M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 38873905, https://doi.org/10.1175/MWR-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., G. Ohring, C. Kummerow, and T. Auligne, 2011a: Assimilating satellite observations of clouds and precipitation into NWP models. Bull. Amer. Meteor. Soc., 92 (Suppl.), https://doi.org/10.1175/2011BAMS3182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., and Coauthors, 2011b: Satellite cloud and precipitation assimilation at operational NWP centres. Quart. J. Roy. Meteor. Soc., 137, 19341951, https://doi.org/10.1002/qj.905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bierdel, L., P. Friederichs, and S. Bentzien, 2012: Spatial kinetic energy spectra in the convection-permitting limited-area NWP model COSMO-DE. Meteor. Z., 21, 245258, https://doi.org/10.1127/0941-2948/2012/0319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bikos, D., and Coauthors, 2012: Synthetic satellite imagery for real-time high-resolution model evaluation. Wea. Forecasting, 27, 784795, https://doi.org/10.1175/WAF-D-11-00130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bugliaro, L., T. Zinner, C. Keil, B. Mayer, R. Hollmann, M. Reuter, and W. Thomas, 2011: Validation of cloud property retrievals with simulated satellite radiances: A case study for SEVIRI. Atmos. Chem. Phys., 11, 56035624, https://doi.org/10.5194/acp-11-5603-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deneke, H. M., and R. A. Roebeling, 2010: Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel. Atmos. Chem. Phys., 10, 97619772, https://doi.org/10.5194/acp-10-9761-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Giuseppe, F., and A. M. Tompkins, 2015: A parameterization of cloud overlap as a function of wind shear and its impact in ECMWF forecast. ECMWF Tech. Memo. 750, 22 pp.

  • EUMETSAT, 2015: MSG meteorological products extraction facility algorithm specification document. EUMETSAT Doc. EUM/MSG/SPE/022, 297 pp.

  • Fu, Q., 1996: An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 20582082, https://doi.org/10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., and Coauthors, 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3179, in press.

    • Crossref
    • Export Citation
  • Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber, 2006: JCSDA Community Radiative Transfer Model (CRTM)—Version 1. NOAA Tech. Rep. NESDIS 122, 33 pp.

  • Harnisch, F., M. Weissmann, and Á. Periáñez, 2016: Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system. Quart. J. Roy. Meteor. Soc., 142, 17971808, https://doi.org/10.1002/qj.2776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heinze, R., and Coauthors, 2017: Large-eddy simulations over Germany using ICON: A comprehensive evaluation. Quart. J. Roy. Meteor. Soc., 143, 69100, https://doi.org/10.1002/qj.2947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, R. J., and A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 126, 29032909, https://doi.org/10.1002/qj.49712656914.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y. X., and K. Stamnes, 1993: An accurate parameterization of the radiative properties of water clouds suitable for use in climate models. J. Climate, 6, 728742, https://doi.org/10.1175/1520-0442(1993)006<0728:AAPOTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112126, https://doi.org/10.1016/j.physd.2006.11.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakub, F., and B. Mayer, 2015: A three-dimensional parallel radiative transfer model for atmospheric heating rates for use in cloud resolving models—The tenstream solver. J. Quant. Spectrosc. Radiat. Transfer, 163, 6371, https://doi.org/10.1016/j.jqsrt.2015.05.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jonkheid, B. J., R. A. Roebeling, and E. van Meijgaard, 2012: A fast SEVIRI simulator for quantifying retrieval uncertainties in the CM SAF cloud physical property algorithm. Atmos. Chem. Phys., 12, 10 95710 969, https://doi.org/10.5194/acp-12-10957-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kostka, P. M., M. Weissmann, R. Buras, B. Mayer, and O. Stiller, 2014: Observation operator for visible and near-infrared satellite reflectances. J. Atmos. Oceanic Technol., 31, 12161233, https://doi.org/10.1175/JTECH-D-13-00116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marquart, S., and B. Mayer, 2002: Towards a reliable GCM estimation of contrail radiative forcing. Geophys. Res. Lett., 29, 1179, https://doi.org/10.1029/2001GL014075.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, G. M., D. W. Johnson, and A. Spice, 1994: The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds. J. Atmos. Sci., 51, 18231842, https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matricardi, M., 2005: The inclusion of aerosols and clouds in RTIASI, the ECMWF fast radiative transfer model for the infrared atmospheric sounding interferometer. ECMWF Tech. Memo. 474, 53 pp.

  • Mayer, B., 2009: Radiative transfer in the cloudy atmosphere. ERCA 2008—From the Human Dimensions of Global Environmental Change to the Observation of the Earth from Space, C. Boutron, Ed., Vol. 1, EDP Sciences, 75–99.

    • Crossref
    • Export Citation
  • Meirink, J. F., R. A. Roebeling, and P. Stammes, 2013: Inter-calibration of polar imager solar channels using SEVIRI. Atmos. Meas. Tech., 6, 24952508, https://doi.org/10.5194/amt-6-2495-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morcrette, J.-J., and C. Jakob, 2000: The response of the ECMWF model to changes in the cloud overlap assumption. Mon. Wea. Rev., 128, 17071732, https://doi.org/10.1175/1520-0493(2000)128<1707:TROTEM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., H. W. Barker, and J.-J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res., 108, 4376, https://doi.org/10.1029/2002JD003322.

    • Search Google Scholar
    • Export Citation
  • Quaas, J., 2012: Evaluating the critical relative humidity as a measure of subgrid-scale variability of humidity in general circulation model cloud cover parameterizations using satellite data. J. Geophys. Res., 117, D09208, https://doi.org/10.1029/2012JD017495.

    • Search Google Scholar
    • Export Citation
  • Räisänen, P., H. W. Barker, M. F. Khairoutdinov, J. Li, and D. A. Randall, 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130, 20472067, https://doi.org/10.1256/qj.03.99.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ritter, B., and J.-F. Geleyn, 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, 303325, https://doi.org/10.1175/1520-0493(1992)120<0303:ACRSFN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 14071425, https://doi.org/10.1002/qj.1999.49712555615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheck, L., P. Frèrebeau, R. Buras-Schnell, and B. Mayer, 2016a: A fast radiative transfer method for the simulation of visible satellite imagery. J. Quant. Spectrosc. Radiat. Transfer, 175, 5467, https://doi.org/10.1016/j.jqsrt.2016.02.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheck, L., J. Hocking, and R. Saunders, 2016b: A comparison of MFASIS and RTTOV-DOM. Version 1, NWP SAF Doc. NWPSAF-MO-VS-054, 18 pp., http://nwpsaf.eu/vs_reports/nwpsaf-mo-vs-054.pdf.

  • Schraff, C., H. Reich, A. Rhodin, A. Schomburg, K. Stephan, A. Periez, and R. Potthast, 2016: Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Quart. J. Roy. Meteor. Soc., 142, 14531472, https://doi.org/10.1002/qj.2748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Senf, F., and H. Deneke, 2017: Uncertainties in synthetic Meteosat SEVIRI infrared brightness temperatures in the presence of cirrus clouds and implications for evaluation of cloud microphysics. Atmos. Res., 183, 113129, https://doi.org/10.1016/j.atmosres.2016.08.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shonk, J. K. P., R. J. Hogan, J. M. Edwards, and G. G. Mace, 2010: Effect of improving representation of horizontal and vertical cloud structure on the Earth’s global radiation budget. Part I: Review and parametrization. Quart. J. Roy. Meteor. Soc., 136, 11911204, https://doi.org/10.1002/qj.647.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmer, C., and Coauthors, 2016: HErZ: The German Hans-Ertel Centre for Weather Research. Bull. Amer. Meteor. Soc., 97, 10571068, https://doi.org/10.1175/BAMS-D-13-00227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stamnes, K., S.-C. Tsay, W. J. Wiscombe, and I. Lazlo, 2000: DISORT, a general-purpose Fortran program for discrete-ordinate-method radiative transfer in scattering and emitting layered media: Documentation of methodology. Version 1.1, NASA GSFC Doc., 112, ftp://climate1.gsfc.nasa.gov/wiscombe/Multiple_Scatt/DISORTReport1.1.pdf.

  • Sundqvist, H., E. Berge, and J. E. Kristjansson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117, 16411657, https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., and F. Di Giuseppe, 2007: Generalizing cloud overlap treatment to include solar zenith angle effects on cloud geometry. J. Atmos. Sci., 64, 21162125, https://doi.org/10.1175/JAS3925.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wapler, K., and B. Mayer, 2008: A fast three-dimensional approximation for the calculation of surface irradiance in large-eddy simulation models. J. Appl. Meteor. Climatol., 47, 30613071, https://doi.org/10.1175/2008JAMC1842.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weissmann, M., and Coauthors, 2014: Initial phase of the Hans-Ertel Centre for Weather Research—A virtual centre at the interface of basic and applied weather and climate research. Meteor. Z., 23, 193208, https://doi.org/10.1127/0941-2948/2014/0558.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wissmeier, U., R. Buras, and B. Mayer, 2013: paNTICA: A fast 3D radiative transfer scheme to calculate surface solar irradiance for NWP and LES models. J. Appl. Meteor. Climatol., 52, 16981715, https://doi.org/10.1175/JAMC-D-12-0227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyser, K., 1998: The effective radius in ice clouds. J. Climate, 11, 17931802, https://doi.org/10.1175/1520-0442(1998)011<1793:TERIIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 24 24 10
PDF Downloads 19 19 9

Efficient Methods to Account for Cloud-Top Inclination and Cloud Overlap in Synthetic Visible Satellite Images

View More View Less
  • 1 Hans-Ertel Centre for Weather Research, and Ludwig-Maximilian University of Munich, Munich, Germany
© Get Permissions
Restricted access

Abstract

Visible satellite images contain high-resolution information about clouds that would be well suited for convective-scale data assimilation. This application requires a forward operator to generate synthetic images from the output of numerical weather prediction models. Only recently have 1D radiative transfer (RT) solvers become sufficiently fast for this purpose. Here computationally efficient methods are proposed to increase the accuracy and consistency of an operator based on the Method for Fast Satellite Image Synthesis (MFASIS) 1D RT. Two important problems are addressed: the 3D RT effects related to inclined cloud tops and the overlap of subgrid clouds. It is demonstrated that in a rotated frame of reference, an approximate solution for the 3D RT problem can be obtained by solving a computationally much cheaper 1D RT problem. Several deterministic and stochastic schemes that take the overlap of subgrid clouds into account are discussed. The impact of the inclination correction and the overlap schemes is evaluated for synthetic 0.6-μm SEVIRI images computed from operational forecasts of the German-focused COSMO (COSMO-DE) Model for a test period in May–June 2016. The cloud-top inclination correction increases the information content of the synthetic images considerably and reduces systematic errors, in particular for larger solar zenith angles. Taking subgrid cloud overlap into account is essential to avoid large systematic errors. The results obtained using several different 2D cloud overlap schemes are very similar, whereas small but significant differences are found for the most consistent 3D method, which accounts for the fact that the RT problem is solved for columns tilted toward the satellite.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Leonhard Scheck, leonhard.scheck@lmu.de

Abstract

Visible satellite images contain high-resolution information about clouds that would be well suited for convective-scale data assimilation. This application requires a forward operator to generate synthetic images from the output of numerical weather prediction models. Only recently have 1D radiative transfer (RT) solvers become sufficiently fast for this purpose. Here computationally efficient methods are proposed to increase the accuracy and consistency of an operator based on the Method for Fast Satellite Image Synthesis (MFASIS) 1D RT. Two important problems are addressed: the 3D RT effects related to inclined cloud tops and the overlap of subgrid clouds. It is demonstrated that in a rotated frame of reference, an approximate solution for the 3D RT problem can be obtained by solving a computationally much cheaper 1D RT problem. Several deterministic and stochastic schemes that take the overlap of subgrid clouds into account are discussed. The impact of the inclination correction and the overlap schemes is evaluated for synthetic 0.6-μm SEVIRI images computed from operational forecasts of the German-focused COSMO (COSMO-DE) Model for a test period in May–June 2016. The cloud-top inclination correction increases the information content of the synthetic images considerably and reduces systematic errors, in particular for larger solar zenith angles. Taking subgrid cloud overlap into account is essential to avoid large systematic errors. The results obtained using several different 2D cloud overlap schemes are very similar, whereas small but significant differences are found for the most consistent 3D method, which accounts for the fact that the RT problem is solved for columns tilted toward the satellite.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Leonhard Scheck, leonhard.scheck@lmu.de
Save