• Baordo, F., and A. J. Geer, 2016: Assimilation of SSMIS humidity-sounding channels in all-sky conditions over land using a dynamic emissivity retrieval. Quart. J. Roy. Meteor. Soc., 142, 28542866, https://doi.org/10.1002/qj.2873.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., and F. Weng, 2016: Modeling land surface roughness effect on soil microwave emission in Community Surface Emissivity Model. IEEE Trans. Geosci. Remote Sens., 54, 17161726, https://doi.org/10.1109/TGRS.2015.2487885.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., F. Weng, Y. Han, and Q. Liu, 2008: Validation of the Community Radiative Transfer Model by using CloudSat data. J. Geophys. Res., 113, D00A03, https://doi.org/10.1029/2007JD009561.

    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sens. Environ., 116, 140158, https://doi.org/10.1016/j.rse.2010.10.017.

    • 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
  • 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, 40 pp., https://repository.library.noaa.gov/view/noaa/1157.

    • Search Google Scholar
    • Export Citation
  • Kim, M. J., J. Jin, A. E. L. Akkraoui, W. McCarty, R. Todling, G. U. Wei, and R. Gelaro, 2020: The framework for assimilating all-sky GPM microwave imager brightness temperature data in the NASA GEOS data assimilation system. Mon. Wea. Rev., 148, 24332455, https://doi.org/10.1175/MWR-D-19-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model 1. Model structure. J. Geophys. Res., 105, 24 80924 822, https://doi.org/10.1029/2000JD900327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maeda, T., Y. Taniguchi, and K. Imaoka, 2016: GCOM-W1 AMSR2 level 1R product: Dataset of brightness temperature modified using the antenna pattern matching technique. IEEE Trans. Geosci. Remote Sens., 54, 770782, https://doi.org/10.1109/TGRS.2015.2465170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mätzler, C., 1994: Passive microwave signatures of landscapes in winter. Meteor. Atmos. Phys., 54, 241260, https://doi.org/10.1007/BF01030063.

  • Munchak, S. J., S. Ringerud, L. Brucker, Y. You, I. de Gelis, and C. Prigent, 2020: An active–passive microwave land surface database from GPM. IEEE Trans. Geosci. Remote Sens., 58, 62246242, https://doi.org/10.1109/TGRS.2020.2975477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NASEM, 2018: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. National Academies Press, 716 pp., https://doi.org/10.17226/24938.

    • Search Google Scholar
    • Export Citation
  • Petty, G. W., and R. Bennartz, 2017: Field-of-view characteristics and resolution matching for the Global Precipitation Measurement (GPM) Microwave Imager (GMI). Atmos. Meas. Tech., 10, 745758, https://doi.org/10.5194/amt-10-745-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, R., and Coauthors, 2020: RTTOV-13 science and validation report. EUMETSAT Rep. NWPSAF-MO-TV-046, 106 pp., https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov13/rttov13_svr.pdf.

    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stieglitz, M., A. Ducharne, R. Koster, and M. Suarez, 2001: The impact of detailed snow physics on the simulation of snow cover and subsurface thermodynamics at continental scales. J. Hydrometeor., 2, 228242, https://doi.org/10.1175/1525-7541(2001)002<0228:TIODSP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todling, R., and A. El Akkraoui, 2018: The GMAO Hybrid Ensemble-Variational Atmospheric Data Assimilation System: Version 2.0. NASA Tech. Rep. NASA/TM-2018-104606, 184 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Todling1019.pdf.

    • Search Google Scholar
    • Export Citation
  • Wang, D., and Coauthors, 2017: Surface emissivity at microwaves to millimeter waves over polar regions: Parameterization and evaluation with aircraft experiments. J. Atmos. Oceanic Technol., 34, 10391059, https://doi.org/10.1175/JTECH-D-16-0188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, F., B. Yan, and N. C. Grody, 2001: A microwave land emissivity model. J. Geophys. Res., 106, 20 11520 123, https://doi.org/10.1029/2001JD900019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., R. Todling, and J. Jin, 2021: Improving the use of surface-sensitive radiances in the GMAO GEOS system. 23rd Int. TOVS Study Conf., Online, ITWG, http://cimss.ssec.wisc.edu/itwg/itsc/itsc23/agendas/posters/poster.2p.20.zhu.pdf.

    • Search Google Scholar
    • Export Citation
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Assessment of Retrieved GMI Emissivity over Land, Snow, and Sea Ice in the GEOS System

Bryan Mills KarpowiczaGlobal Modeling and Assimilation Office, Goddard Space Flight Center, Greenbelt, Maryland
bGoddard Earth Sciences and Technology and Research, Greenbelt, Maryland
cUniversity of Maryland, Baltimore County, Baltimore, Maryland

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Yanqiu ZhuaGlobal Modeling and Assimilation Office, Goddard Space Flight Center, Greenbelt, Maryland
bGoddard Earth Sciences and Technology and Research, Greenbelt, Maryland

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Stephen Joseph MunchakdMesoscale Atmospheric Processes Laboratory, Goddard Space Flight Center, Greenbelt, Maryland

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Will McCartyaGlobal Modeling and Assimilation Office, Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System–Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

© 2022 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: Bryan Mills Karpowicz, bryan.m.karpowicz@nasa.gov

Abstract

Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System–Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

© 2022 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: Bryan Mills Karpowicz, bryan.m.karpowicz@nasa.gov
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