Editorial Type:
Article
Article Type:
Research Article

Expansion of the All-Sky Radiance Assimilation to ATMS at NCEP

Yanqiu Zhu I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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George Gayno I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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R. James Purser I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Xiujuan Su I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Runhua Yang I.M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Print Publication:
01 Jul 2019
DOI:
https://doi.org/10.1175/MWR-D-18-0228.1
Page(s):
2603–2620
Restricted access

Abstract

Since the implementation of all-sky radiance assimilation of the Advanced Microwave Sounding Unit-A (AMSU-A) in the operational hybrid 4D ensemble–variational Global Forecast System at NCEP in 2016, the all-sky approach has been tested to expand to the radiances of Advanced Technology Microwave Sounder (ATMS) in the Gridpoint Statistical Interpolation analysis system (GSI). Following the all-sky framework implemented for the AMSU-A radiances, ATMS radiance assimilation adopts similar procedures in quality control, bias correction, and model of observation error. Efforts have been focused on special considerations that are necessary because of the unique features of the ATMS radiances and water vapor channels, including surface properties based on fields of view size and shape, and taking care of large departures from the first guess (OmF) along coastlines and radiances affected by strong scattering. More importantly, it is shown that this work makes microwave radiance OmFs become more consistent among different sensors, and provides indications of the deficiencies in quality control procedures of the original ATMS and Microwave Humidity Sounder (MHS) clear-sky radiance assimilation. While the generalized tracer effect is noticed, the overall impact on the forecast skill is neutral. This work is included in the upcoming operational implementation in 2019.

© 2019 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: Yanqiu Zhu, yanqiu.zhu@noaa.gov

Abstract

Since the implementation of all-sky radiance assimilation of the Advanced Microwave Sounding Unit-A (AMSU-A) in the operational hybrid 4D ensemble–variational Global Forecast System at NCEP in 2016, the all-sky approach has been tested to expand to the radiances of Advanced Technology Microwave Sounder (ATMS) in the Gridpoint Statistical Interpolation analysis system (GSI). Following the all-sky framework implemented for the AMSU-A radiances, ATMS radiance assimilation adopts similar procedures in quality control, bias correction, and model of observation error. Efforts have been focused on special considerations that are necessary because of the unique features of the ATMS radiances and water vapor channels, including surface properties based on fields of view size and shape, and taking care of large departures from the first guess (OmF) along coastlines and radiances affected by strong scattering. More importantly, it is shown that this work makes microwave radiance OmFs become more consistent among different sensors, and provides indications of the deficiencies in quality control procedures of the original ATMS and Microwave Humidity Sounder (MHS) clear-sky radiance assimilation. While the generalized tracer effect is noticed, the overall impact on the forecast skill is neutral. This work is included in the upcoming operational implementation in 2019.

© 2019 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: Yanqiu Zhu, yanqiu.zhu@noaa.gov
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  • Andersson, E., and H. Järvinen, 1998: Variational quality control. ECMWF Tech. Memo. 250, 31 pp., https://doi.org/10.21957/lqz2wn16g.

    • Crossref
    • Export Citation

  • Baordo, F., and A. Geer, 2015: All-sky assimilation of SSMIS humidity sounding channels over land within the ECMWF system. EUMETSAT/ECMWF Research Rep. 38, 26 pp.

  • Bathmann, K., 2018: Justification for estimating observation-error covariances with the Desroziers diagnostic. Quart. J. Roy. Meteor. Soc., 144, 1965–1974, https://doi.org/10.1002/qj.3395.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bauer, P., E. Moreau, F. Chevallier, and U. O’Keeffe, 2006: Multiple-scattering microwave radiative transfer for data assimilation applications. Quart. J. Roy. Meteor. Soc., 132, 1259–1281, https://doi.org/10.1256/qj.05.153.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bauer, P., A. J. Geer, P. Lopez, and D. Salmond, 2010: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Quart. J. Roy. Meteor. Soc., 136, 1868–1885, https://doi.org/10.1002/qj.659.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bennartz, R., A. Thoss, A. Dybbroe, and B. Michelson, 2002: Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications. Meteor. Appl., 9, 177–189, https://doi.org/10.1017/S1350482702002037.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Collard, A., J. Derber, R. Treadon, N. Atkinson, J. Jung, and K. Garrett, 2012: Toward assimilation of CrIS and ATMS in the NCEP Global Model. 18th Int. TOVS Study Conf., Toulouse, France, International TOVS Working Group, 1.18, https://cimss.ssec.wisc.edu/itwg/itsc/itsc18/program/files/Collard_ITSC18.pdf.

  • 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, https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Derber, J. C., D. F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting, 6, 538–547, https://doi.org/10.1175/1520-0434(1991)006<0538:TNGOAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ding, S., P. Yang, F. Weng, Q. Liu, Y. Han, P. v. Delst, J. Li, and B. Baum, 2011: Validation of the community radiative transfer model. J. Quant. Spectrosc. Radiat. Transfer, 112, 1050–1064, https://doi.org/10.1016/j.jqsrt.2010.11.009.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Geer, A. J., and P. Bauer, 2011: Observation errors in all-sky data assimilation. Quart. J. Roy. Meteor. Soc., 137, 2024–2037, https://doi.org/10.1002/qj.830.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Geer, A. J., P. Bauer, and S. English, 2012: Assimilating AMSU-A temperature sounding channels in the presence of cloud and precipitation. ECMWF Tech. Memo. 670, 41 pp.

  • Geer, A. J., and Coauthors, 2018: All-sky satellite data assimilation at operational weather forecasting centres. Quart. J. Roy. Meteor. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grody, N., F. Weng, and R. Ferraro, 1999: Application of AMSU for obtaining water vapor, cloud liquid water, precipitation, snow cover and sea ice concentration. 10th Int. TOVS Study Conf., Boulder, CO, International TOVS Working Group, 230–240, http://library.ssec.wisc.edu/research_Resources/publications/pdfs/ITSC10/grody01_ITSC10_1999.pdf.

  • Grody, N., J. Zhao, R. Ferraro, F. Weng, and R. Boers, 2001: Determination of precipitable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit. J. Geophys. Res., 106, 2943–2953, https://doi.org/10.1029/2000JD900616.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4D EnVar and hybrid variants. Mon. Wea. Rev., 143, 452–470, https://doi.org/10.1175/MWR-D-13-00350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, Q., and F. Weng, 2006: Advanced doubling-adding method for radiative transfer in planetary atmosphere. J. Atmos. Sci., 63, 3459–3465, https://doi.org/10.1175/JAS3808.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lorenc, A. C., and O. Hammon, 1988: Objective quality control of observations using Bayesian methods. Theory and a practical implementation. Quart. J. Roy. Meteor. Soc., 114, 515–543, https://doi.org/10.1002/qj.49711448012.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Martinet, P., N. Fourrie, V. Guidard, F. Rabier, T. Montmerle, and P. Brunel, 2013: Towards the use of microphysical variables for the assimilation of cloud affected infrared radiances. Quart. J. Roy. Meteor. Soc., 139, 1402–1416, https://doi.org/10.1002/qj.2046.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Migliorini, S., A. Lorenc, and W. Bell, 2017: A moisture-incrementing operator for the assimilation of humidity and cloud-sensitive observations: Formulation and preliminary results. Quart. J. Roy. Meteor. Soc., 144, 443–457, https://doi.org/10.1002/qj.3216.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Okamoto, K., 2013: Assimilation of overcast cloudy infrared radiances of the geostationary MTSAT-1R imager. Quart. J. Roy. Meteor. Soc., 139, 715–730, https://doi.org/10.1002/qj.1994.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Purser, R. J., 1984: A new approach to the optimal assimilation of meteorological data by iterative Bayesian analysis. 10th Conf. on Weather Forecasting and Analysis, Clearwater Beach, FL, Amer. Meteor. Soc., 102–105.

  • Purser, R. J., 2011: Mathematical principles of the construction and characterization of a parameterized family of Gaussian mixture distributions suitable to serve as models for the probability distributions of measurement errors in nonlinear quality control. NOAA/NCEP Office Note 468, 42 pp.

  • Purser, R. J., 2018: Convenient parameterization of super-logistic probability models of effective observation error. NOAA/NCEP Office Note 495, 8 pp., https://doi.org/10.25923/kvmz-vf34.

    • Crossref
    • Export Citation

  • Stegmann, P. G., G. Tang, P. Yang, and B. T. Johnson, 2018: A stochastic model for density-dependent microwave snow- and graupel scattering coefficients of the NOAA JCSDA community radiative transfer model. J. Quant. Spectrosc. Radiat. Transfer, 211, 9–24, https://doi.org/10.1016/j.jqsrt.2018.02.026.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, X., and T. Lei, 2014: GSI-based four-dimensional ensemble-variational (4DEnsVar) data assimilation: Formulation and single-resolution experiments with real data for NCEP Global Forecast System. Mon. Wea. Rev., 142, 3303–3325, https://doi.org/10.1175/MWR-D-13-00303.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weng, F., L. Zhao, R. R. Ferraro, G. Poe, X. Li, and N. C. Grody, 2003: Advanced microwave sounding unit cloud and precipitation algorithms. Radio Sci., 38, 8086–8096, https://doi.org/10.1029/2002RS002679.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Whitaker, J., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • 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, https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, Q., and F. H. Carr, 1997: A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 1931–1953, https://doi.org/10.1175/1520-0493(1997)125<1931:APCSFO>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, 2014a: 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, https://doi.org/10.1002/qj.2233.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhu, Y., and Coauthors, 2014b: Variational bias correction in the NCEP’s data assimilation system. 19th Int. TOVS Study Conf., Jeju Island, South Korea, International TOVS Working Group, http://cimss.ssec.wisc.edu/itwg/itsc/itsc19/program/papers/10_02_zhu.pdf.

  • Zhu, Y., and Coauthors, 2016: All-sky microwave radiance assimilation in NCEP’s GSI analysis system. Mon. Wea. Rev., 144, 4709–4735, https://doi.org/10.1175/MWR-D-15-0445.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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