An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System

Kevin J. Mueller Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Junjie Liu Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Will McCarty NASA Goddard Space Flight Center, Greenbelt, Maryland

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Ron Gelaro NASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

This study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground track, as opposed to when equal uncertainties were assigned to the eastward and northward components as in previous studies. Assimilating only the cross-track component provided 60% of the benefit of both components. When optimally assimilated, MISR CMV proved broadly beneficial throughout the Earth, with the greatest benefit evident at high latitudes where there is a confluence of more frequent CMV coverage and gaps in coverage from other MERRA-2 wind observations. Globally, MISR represented 1.6% of the total forecast benefit, whereas regionally that percentage was as large as 3.7%.

© 2017 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: Junjie Liu, junjie.liu@jpl.nasa.gov

Abstract

This study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground track, as opposed to when equal uncertainties were assigned to the eastward and northward components as in previous studies. Assimilating only the cross-track component provided 60% of the benefit of both components. When optimally assimilated, MISR CMV proved broadly beneficial throughout the Earth, with the greatest benefit evident at high latitudes where there is a confluence of more frequent CMV coverage and gaps in coverage from other MERRA-2 wind observations. Globally, MISR represented 1.6% of the total forecast benefit, whereas regionally that percentage was as large as 3.7%.

© 2017 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: Junjie Liu, junjie.liu@jpl.nasa.gov
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  • Baker, N. L., P. M. Pauley, R. H. Langland, K. Mueller, and D. Wu, 2014: An assessment of the impact of the assimilation of NASA TERRA MISR atmospheric motion vectors on the NRL global atmospheric prediction system. Second Symp. on the Joint Center for Satellite Data Assimilation, Atlanta, GA, Amer. Meteor. Soc., J3.1, https://ams.confex.com/ams/94Annual/webprogram/Paper231106.html.

  • Borde, R., O. Hautecoeur, and M. Carranza, 2016: EUMETSAT global AVHRR wind product. J. Atmos. Oceanic Technol., 33, 429438, https://doi.org/10.1175/JTECH-D-15-0155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohn, S. E., 1997: An introduction to estimation theory. J. Meteor. Soc. Japan, 75, 257288, https://doi.org/10.2151/jmsj1965.75.1B_257.

  • Cress, A., 2014: Assessment of MISR wind vector quality and impact using the global NWP system at DWD. 12th Int. Winds Workshop, Copenhagen, Denmark, EUMETSAT, 7 pp., http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_CONF_P61_S4_02_CRESS_V&RevisionSelectionMethod=LatestReleased&Rendition=Web.

  • Gelaro, R., Y. Zhu, and R. M. Errico, 2007: Examination of various-order adjoint-based approximations of observation impact. Meteor. Z., 16, 685692, https://doi.org/10.1127/0941-2948/2007/0248.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., R. H. Langland, S. Pellerin, and R. Todling, 2010: The THORPEX observation impact intercomparison experiment. Mon. Wea. Rev., 138, 40094025, https://doi.org/10.1175/2010MWR3393.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horváth, Á., 2013: Improvements to MISR stereo motion vectors. J. Geophys. Res. Atmos., 118, 56005620, https://doi.org/10.1002/jgrd.50466.

  • Horváth, Á., and R. Davies, 2001: Feasibility and error analysis of cloud motion wind extraction from near-simultaneous multiangle MISR measurements. J. Atmos. Oceanic Technol., 18, 591608, https://doi.org/10.1175/1520-0426(2001)018<0591:FAEAOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 341 pp.

    • Crossref
    • Export Citation
  • Key, J. R., D. Santek, C. S. Velden, N. Bormann, J.-N. Thepaut, L. P. Riishøjgaard, Y. Zhu, and W. P. Menzel, 2003: Cloud-drift and water vapor winds in the polar regions from MODISIR. IEEE Trans. Geosci. Remote Sens., 41, 482492, https://doi.org/10.1109/TGRS.2002.808238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and N. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A, 189201, https://doi.org/10.3402/tellusa.v56i3.14413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lazzara, M. A., R. Dworak, D. A. Santek, B. T. Hoover, C. S. Velden, and J. R. Key, 2014: High-latitude atmospheric motion vectors from composite satellite data. J. Appl. Meteor. Climatol., 53, 534547, https://doi.org/10.1175/JAMC-D-13-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marseille, G. J., A. Stoffelen, and J. Barkmeijer, 2008: Sensitivity Observing System Experiment (SOSE)—A new effective NWP-based tool in designing the global observing system. Tellus, 60A, 216233, https://doi.org/10.1111/j.1600-0870.2007.00288.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, K. J., C. M. Moroney, V. Jovanovic, M. J. Garay, J.-P. Muller, L. Di Girolamo, and R. Davies, 2013a: MISR Level 2 Cloud Product Algorithm Theoretical Basis. Tech. Rep. JPL D-73327, Jet Propulsion Laboratory, 51 pp., https://eospso.gsfc.nasa.gov/sites/default/files/atbd/MISR_L2_CLOUD_ATBD-1.pdf.

  • Mueller, K. J., C. M. Moroney, and V. Jovanovic, 2013b: MISR level 2 cloud product quality statement, September 14, 2012. Atmospheric Science Data Center, 4 pp., https://eosweb.larc.nasa.gov/sites/default/files/project/misr/quality_summaries/L2TC_Cloud_Product.pdf.

  • Mueller, K. J., and Coauthors, 2017: Assessment of MISR Cloud Motion Vectors (CMVs) relative to GOES and MODIS Atmospheric Motion Vectors (AMVs). J. Appl. Meteor. Climatol., 56, 555572, https://doi.org/10.1175/JAMC-D-16-0112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purser, R. J., W. Wu, D. F. Parrish, and N. M. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 15241535, https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purser, R. J., W. Wu, D. F. Parrish, and N. M. Roberts, 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 15361548, https://doi.org/10.1175//2543.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riishøjgaard, L. P., R. Atlas, and G. D. Emmitt, 2004: The impact of Doppler lidar wind observations on a single-level meteorological analysis. J. Appl. Meteor., 43, 810820, https://doi.org/10.1175/2091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, K., J. Cotton, N. Bormann, and M. Forsythe, 2015: Characterizing AMV height-assignment error by comparing best-fit pressure statistics from the Met Office and ECMWF Data Assimilation Systems. J. Appl. Meteor. Climatol., 54, 225242, https://doi.org/10.1175/JAMC-D-14-0025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santek, D., 2010: The impact of satellite-derived polar winds on lower-latitude forecasts. Mon. Wea. Rev., 138, 123139, https://doi.org/10.1175/2009MWR2862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoffelen, A., G. Marseille, E. Andersson, and D. G. Tan, 2005: Comments on “The impact of Doppler wind observations on a single-level meteorological analysis.” J. Appl. Meteor., 44, 12761277, https://doi.org/10.1175/JAM2268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, X., J. Derber, and J. Jung, 2012: Recent work on satellite atmospheric motion vectors in the NCEP data assimilation system. 11th Int. Winds Workshop, Auckland, New Zealand, EUMETSAT., 6 pp., https://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_CONF_P60_S4_13_SU_V&RevisionSelectionMethod=LatestReleased&Rendition=Web.

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 29052916, https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamashita, K., 2014: The impact of NASA TERRA MISR atmospheric motion vector assimilation into JMA’s operational global NWP system. CAS/JSC WGNE Research Activities in Atmospheric Oceanic Models, 2 pp., http://www.wcrp-climate.org/WGNE/BlueBook/2014/individual-articles/01_Yamashita_Koji_WGNE_BB2014_MISR_yamashita_final.pdf.

  • Zhu, Y., and R. Gelaro, 2008: Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Wea. Rev., 136, 335351, https://doi.org/10.1175/MWR3525.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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