Editorial Type:
Article
Article Type:
Research Article

Height Correction of Atmospheric Motion Vectors Using Satellite Lidar Observations from CALIPSO

Kathrin Folger Hans Ertel Centre for Weather Research, Data Assimilation Branch, Ludwig-Maximilians-Universität, Munich, Germany

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Martin Weissmann Hans Ertel Centre for Weather Research, Data Assimilation Branch, Ludwig-Maximilians-Universität, Munich, Germany

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Print Publication:
01 Jul 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0337.1
Page(s):
1809–1819
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Abstract

Atmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

Corresponding author address: Kathrin Folger, LMU Meteorologie, Theresienstraße 37, 80333 Munich, Germany. E-mail: kathrin.folger@lmu.de

Abstract

Atmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

Corresponding author address: Kathrin Folger, LMU Meteorologie, Theresienstraße 37, 80333 Munich, Germany. E-mail: kathrin.folger@lmu.de
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Journal of Applied Meteorology and Climatology

  • Baker, W. E., and Coauthors, 2014: Lidar-measured wind profiles—The missing link in the global observing system. Bull. Amer. Meteor. Soc., 95, 543–564, doi:10.1175/BAMS-D-12-00164.1.

    • Search Google Scholar
    • Export Citation

  • Borde, R., M. Doutriaux-Boucher, G. Dew, and M. Carranza, 2014: A direct link between feature tracking and height assignment of operational EUMETSAT atmospheric motion vectors. J. Atmos. Oceanic Technol., 31, 33–46, doi:10.1175/JTECH-D-13-00126.1.

    • Search Google Scholar
    • Export Citation

  • Bormann, N., and J.-N. Thépaut, 2004: Impact of MODIS polar winds in ECMWF’s 4DVAR data assimilation system. Mon. Wea. Rev., 132, 929–940, doi:10.1175/1520-0493(2004)132<0929:IOMPWI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bormann, N., S. Saarinen, G. Kelly, and J.-N. Thépaut, 2003: The spatial structure of observation errors in atmospheric motion vectors from geostationary satellite data. Mon. Wea. Rev., 131, 706–718, doi:10.1175/1520-0493(2003)131<0706:TSSOOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bresky, W. C., J. M. Daniels, A. A. Bailey, and S. T. Wanzong, 2012: New methods toward minimizing the slow speed bias associated with atmospheric motion vectors. J. Appl. Meteor. Climatol., 51, 2137–2151, doi:10.1175/JAMC-D-11-0234.1.

    • Search Google Scholar
    • Export Citation

  • Di Michele, S., T. McNally, P. Bauer, and I. Genkova, 2013: Quality assessment of cloud-top height estimates from satellite IR radiances using the CALIPSO lidar. IEEE Trans. Geosci. Remote Sens., 51, 2454–2464, doi:10.1109/TGRS.2012.2210721.

    • Search Google Scholar
    • Export Citation

  • Hernandez-Carrascal, A., and N. Bormann, 2014: Atmospheric motion vectors from model simulations. Part II: Interpretation as spatial and vertical averages of wind and role of clouds. J. Appl. Meteor. Climatol., 53, 65–82, doi:10.1175/JAMC-D-12-0337.1.

    • Search Google Scholar
    • Export Citation

  • Hunt, W. H., D. M. Winker, M. A. Vaughan, K. A. Powell, P. L. Lucker, and C. Weimer, 2009: CALIPSO lidar description and performance assessment. J. Atmos. Oceanic Technol., 26, 1214–1228, doi:10.1175/2009JTECHA1223.1.

    • Search Google Scholar
    • Export Citation

  • Salonen, K., and N. Bormann, 2012: Atmospheric motion vector observations in the ECMWF system: Second year report. EUMETSAT/ECMWF Fellowship Programme Research Rep. 28, 41 pp. [Available online at http://old.ecmwf.int/publications/library/ecpublications/_pdf/saf/rr28.pdf.]

  • Velden, C. S., and K. M. Bedka, 2009: Identifying the uncertainty in determining satellite-derived atmospheric motion vector height attribution. J. Appl. Meteor. Climatol., 48, 450–463, doi:10.1175/2008JAMC1957.1.

    • Search Google Scholar
    • Export Citation

  • Velden, C. S., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205–223, doi:10.1175/BAMS-86-2-205.

    • Search Google Scholar
    • Export Citation

  • Weissmann, M., K. Folger, and H. Lange, 2013: Height correction of atmospheric motion vectors using airborne lidar observations. J. Appl. Meteor. Climatol., 52, 1868–1877, doi:10.1175/JAMC-D-12-0233.1.

    • Search Google Scholar
    • Export Citation

  • Winker, D. M., M. A. Vaughan, A. H. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 2310–2323, doi:10.1175/2009JTECHA1281.1.

    • Search Google Scholar
    • Export Citation

  • Winker, D. M., and Coauthors, 2010: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteor. Soc., 91, 1211–1229, doi:10.1175/2010BAMS3009.1.

    • Search Google Scholar
    • Export Citation
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