Editorial Type:
Article
Article Type:
Research Article

Radiance Preprocessing for Assimilation in the Hourly Updating Rapid Refresh Mesoscale Model: A Study Using AIRS Data

Haidao Lin NOAA/Earth System Research Laboratory, Boulder, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Stephen S. Weygandt NOAA/Earth System Research Laboratory, Boulder, Colorado

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Agnes H. N. Lim Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Ming Hu NOAA/Earth System Research Laboratory, and Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Boulder, Colorado

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John M. Brown NOAA/Earth System Research Laboratory, Boulder, Colorado

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Stanley G. Benjamin NOAA/Earth System Research Laboratory, Boulder, Colorado

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Print Publication:
01 Oct 2017
DOI:
https://doi.org/10.1175/WAF-D-17-0028.1
Page(s):
1781–1800
Restricted access

Abstract

This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.

Denotes content that is immediately available upon publication as open access.

© 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: Haidao Lin, haidao.lin@noaa.gov

Abstract

This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.

Denotes content that is immediately available upon publication as open access.

© 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: Haidao Lin, haidao.lin@noaa.gov
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  • Auligné, T., and A. P. McNally, 2007: Interaction between bias correction and quality control. Quart. J. Roy. Meteor. Soc., 133, 643–653, doi:10.1002/qj.57.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631–642, doi:10.1002/qj.56.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41, 253–264, doi:10.1109/TGRS.2002.808356.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., and Coauthors, 2004a: An hourly assimilation/forecast cycle: The RUC. Mon. Wea. Rev., 132, 495–518, doi:10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., B. E. Schwartz, E. J. Szoke, and S. E. Koch, 2004b: The value of wind profiler data in U.S. weather forecasting. Bull. Amer. Meteor. Soc., 85, 1871–1886, doi:10.1175/BAMS-85-12-1871.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 1319–1343, doi:10.1175/2009MWR3097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 1669–1694, doi:10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cameron, J., A. Collard, and S. English, 2005: Operational use of AIRS observations at the Met Office. 14th Int. TOVS Study Conf., Beijing, China, Int. ATOVS Working Group, https://cimss.ssec.wisc.edu/itwg/itsc/itsc14/proceedings/B25_Cameron.pdf.

  • 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, doi:10.1029/2007JD009561.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323–3343, doi:10.1256/qj.05.137.

  • Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299, doi:10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Goldberg, M. D., Y. Qu, L. McMillin, W. Wolf, L. Zhou, and M. Divakarla, 2003: AIRS near-real-time products and algorithms in support of operational numerical weather prediction. IEEE Trans. Geosci. Remote Sens., 41, 379–389, doi:10.1109/TGRS.2002.808307.

    • 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, 31 pp., http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/CRTM_v1_NOAAtechReport.pdf.

  • Joo, S., J. Eyre, and R. Marriott, 2013: The impact of MetOp and other satellite data within the Met Office global NWP system using an adjoint-based sensitivity method. Mon. Wea. Rev., 141, 3331–3342, doi:10.1175/MWR-D-12-00232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W.-S. Wu, and S. Lord, 2009: Introduction of the GSI into the NCEP Global Data Assimilation System. Wea. Forecasting, 24, 1691–1705, doi:10.1175/2009WAF2222201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Le Marshall, J., and Coauthors, 2006: Improving global analysis and forecasting with AIRS. Bull. Amer. Meteor. Soc., 87, 891–894, doi:10.1175/BAMS-87-7-891.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lim, A. H. N., J. A. Jung, H.-L. A. Huang, S. A. Ackerman, and J. A. Otkin, 2014: Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecasts using community models. J. Appl. Remote Sens., 8, 083655, doi:10.1117/1.JRS.8.083655.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, H., S. S. Weygandt, S. G. Benjamin, and M. Hu, 2017: Satellite radiance data assimilation within the hourly updated Rapid Refresh. Wea. Forecasting, 32, 1273–1287, https://doi.org/10.1175/WAF-D-16-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, Y., and K. E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, https://ams.confex.com/ams/pdfpapers/83847.pdf.

  • McCarty, W., G. Jedlovec, and T. L. Miller, 2009: Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts. J. Geophys. Res., 114, D18122, doi:10.1029/2008JD011626.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McNally, A. P., P. D. Watts, J. A. Smith, R. Engelen, G. A. Kelly, J. N. Thépaut, and M. Matricardi, 2006: The assimilation of AIRS radiance data at ECMWF. Quart. J. Roy. Meteor. Soc., 132, 935–957, doi:10.1256/qj.04.171.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moninger, W. R., S. G. Benjamin, B. D. Jamison, T. W. Schlatter, T. L. Smith, and E. J. Szoke, 2010: Evaluation of regional aircraft observations using TAMDAR. Wea. Forecasting, 25, 627–645, doi:10.1175/2009WAF2222321.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ota, Y., J. C. Derber, E. Kalnay, and T. Miyoshi, 2013: Ensemble-based observation impact estimates using the NCEP GFS. Tellus, 65A, 20038, doi:10.3402/tellusa.v65i0.20038.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ruston, B., C. Blankenship, W. Campbell, R. Langland, and N. Baker, 2006: Assimilation of AIRS data at NRL. 15th Int. TOVS Study Conf., Maratea, Italy, Int. ATOVS Working Group, 156–161, http://cimss.ssec.wisc.edu/itwg/itsc/itsc15/proceedings/7_2_Ruston.pdf.

  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570–575, doi:10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Singh, R., C. M. Kishtawal, S. P. Ojha, and P. K. Pal, 2012: Impact of assimilation of Atmospheric InfraRed Sounder (AIRS) radiances and retrievals in the WRF 3D-Var assimilation system. J. Geophys. Res., 117, D11107, doi:10.1029/2011JD017367.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., http://dx.doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation

  • Susskind, J., C. D. Barnet, and J. Blaisdell, 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390–409, doi:10.1109/TGRS.2002.808236.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, P., and Coauthors, 2015: Assimilation of thermodynamic information from advanced infrared sounders under partially cloudy skies for regional NWP. J. Geophys. Res. Atmos., 120, 5469–5484, doi:10.1002/2014JD022976.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weng, F. Z., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64, 3799–3807, doi:10.1175/2007JAS2112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905–2916, doi:10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhu, Y., J. Derber, A. Collard, D. Dee, R. Treadon, G. Gayno, and J. A. Jung, 2014: Enhanced radiance bias correction in the National Centers for Environmental Prediction’s Gridpoint Statistical Interpolation data assimilation system. Quart. J. Roy. Meteor. Soc., 140, 1479–1492, doi:10.1002/qj.2233.

    • Crossref
    • Search Google Scholar
    • Export Citation
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