Satellite Radiance Assimilation in the JMA Operational Mesoscale 4DVAR System

Masahiro Kazumori Japan Meteorological Agency, Tokyo, Japan

Search for other papers by Masahiro Kazumori in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The direct radiance assimilation scheme used in the Japan Meteorological Agency (JMA) global analysis system is applied to the JMA mesoscale four-dimensional variational data assimilation (4DVAR) system with two modifications. First, the data-thinning distance is shortened, and, second, the atmospheric profiles are extrapolated from the mesoscale model top to the radiative transfer model top using the U.S. Standard Atmosphere lapse rate. Although the variational bias correction method is widely used in many numerical weather prediction centers for global radiance assimilations, a radiance bias correction method for regional models has not been established because of difficulties in estimating the biases within limited regions and times. This paper examined the use of the bias correction coefficients estimated in the global system for the mesoscale system when the radiance data were introduced instead of the retrievals. It was found that the profile extrapolation was necessary to reduce the biases. Moreover, the use of common bias coefficients enables the use of the radiance data in the same way as the global system. The radiance data assimilation experiments in the mesoscale system demonstrated considerable improvements to the tropospheric geopotential height forecasts and precipitation forecasts. The improvements resulted from the introduction of radiance data from multiple satellites into data-sparse regions and times. However, the major effect of the radiance assimilation on the precipitation forecasts was limited to weak precipitation areas over oceans; the effects on deep convective areas and over land were relatively small.

Corresponding author address: Masahiro Kazumori, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan. E-mail: kazumori@met.kishou.go.jp

Abstract

The direct radiance assimilation scheme used in the Japan Meteorological Agency (JMA) global analysis system is applied to the JMA mesoscale four-dimensional variational data assimilation (4DVAR) system with two modifications. First, the data-thinning distance is shortened, and, second, the atmospheric profiles are extrapolated from the mesoscale model top to the radiative transfer model top using the U.S. Standard Atmosphere lapse rate. Although the variational bias correction method is widely used in many numerical weather prediction centers for global radiance assimilations, a radiance bias correction method for regional models has not been established because of difficulties in estimating the biases within limited regions and times. This paper examined the use of the bias correction coefficients estimated in the global system for the mesoscale system when the radiance data were introduced instead of the retrievals. It was found that the profile extrapolation was necessary to reduce the biases. Moreover, the use of common bias coefficients enables the use of the radiance data in the same way as the global system. The radiance data assimilation experiments in the mesoscale system demonstrated considerable improvements to the tropospheric geopotential height forecasts and precipitation forecasts. The improvements resulted from the introduction of radiance data from multiple satellites into data-sparse regions and times. However, the major effect of the radiance assimilation on the precipitation forecasts was limited to weak precipitation areas over oceans; the effects on deep convective areas and over land were relatively small.

Corresponding author address: Masahiro Kazumori, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan. E-mail: kazumori@met.kishou.go.jp
Save
  • Andersson, E., J. Pailleux, J.-N. Thepaut, J. R. Eyre, A. P. McNally, G. A. Kelly, and P. Courtier, 1994: Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 120, 627653.

    • Search Google Scholar
    • Export Citation
  • Bennartz, R., A. Thoss, A. Dybbroe, and D. B. Michelson, 2002: Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications. Meteor. Appl., 9, 177189.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233343.

  • 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, 22872299.

    • Search Google Scholar
    • Export Citation
  • Eyre, J. R., 1992: A bias correction scheme for simulated TOVS brightness temperatures. ECMWF Tech. Memo. 186, 33 pp.

  • Harris, B. A., and G. A. Kelly, 2001: A satellite radiance bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 14531468.

    • Search Google Scholar
    • Export Citation
  • Honda, Y., and K. Sawada, 2009: Upgrade of the Operational Mesoscale 4D-Var System at the Japan Meteorological Agency. WGNE Blue Book: Research Activities in Atmospheric and Ocean Modelling, WMO/Working Group on Numerical Experimentation, 1.11–1.12. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2009/documents/sections.html.]

  • Honda, Y., M. Nishijima, K. Koizumi, Y. Ohta, K. Tamiya, T. Kawabata, and T. Tsuyuki, 2005: A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results. Quart. J. Roy. Meteor. Soc., 131, 34653475.

    • Search Google Scholar
    • Export Citation
  • Ishikawa, Y., and K. Koizumi, 2002: Meso-scale analysis. Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Technical Progress Report on the Global Data-Processing and Forecasting System (GDPFS) and Numerical Weather Prediction (NWP), Japan Meteorological Agency, Tokyo, Japan, 26–31.

  • JMA, 2007: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Technical Progress Report on the Global Data-Processing and Forecasting System (GDPFS) and Numerical Weather Prediction (NWP), Japan Meteorological Agency, Tokyo, Japan. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline-nwp/index.htm.]

  • JMA, 2013: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Technical Progress Report on The Global Data-Processing and Forecasting System (GDPFS) and Numerical Weather Prediction (NWP) Research, Japan Meteorological Agency, Tokyo, Japan. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp.]

  • Kawabata, T., T. Kuroda, H. Seko, and K. Saito, 2011: A cloud-resolving 4DVAR assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area. Mon. Wea. Rev., 139, 19111931.

    • Search Google Scholar
    • Export Citation
  • Kazumori, M., 2009a: Impact study of the RTTOV-9 fast radiative transfer model in the JMA Global 4D-Var data assimilation system. WGNE Blue Book: Research Activities in Atmospheric and Ocean Modelling, WMO/Working Group on Numerical Experimentation, 1.21–1.22. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2009/documents/sections.html.]

  • Kazumori, M., 2009b: The impacts of an improved quality control and ocean emissivity model for microwave radiance assimilation in the JMA global 4D-Var data assimilation system. WGNE Blue Book: Research Activities in Atmospheric and Ocean Modelling, WMO/Working Group on Numerical Experimentation, 1.19–1.20. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2009/documents/sections.html.]

  • Kazumori, M., Q. Liu, R. Treadon, and J. C. Derber, 2008: Impact study of AMSR-E radiances in the NCEP Global Data Assimilation System. Mon. Wea. Rev., 136, 541559.

    • Search Google Scholar
    • Export Citation
  • Kazumori, M., T. Kadowaki, T. Komori, H. Nishihata, and K. Okamoto, 2010: Operational status and recent developments on cloud and precipitation assimilation at JMA. Proc. Workshop on Assimilating Satellite Observations of Clouds and Precipitation into NWP Models, Reading, United Kingdom, ECMWF/JCSDA, 1619. [Available online at http://www.ecmwf.int/newsevents/meetings/workshops/2010/Satellite_observations/presentations/index.html.]

  • Kazumori, M., T. Egawa, and K. Yoshimoto, 2012: A retrieval algorithm of atmospheric water vapor and cloud liquid water for AMSR-E. European J. Remote Sens., 45, 6374, doi:10.5721/EuJRS20124507.

    • Search Google Scholar
    • Export Citation
  • Koizumi, K., Y. Ishikawa, and T. Tsuyuki, 2005: Assimilation of precipitation data to JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts. SOLA, 1, 4548.

    • Search Google Scholar
    • Export Citation
  • Okamoto, K., K. Kazumori, and H. Owada, 2005: The assimilation of ATOVS radiances in the JMA global analysis system. J. Meteor. Soc. Japan, 83, 201217.

    • Search Google Scholar
    • Export Citation
  • Saito, K., and Coauthors, 2006: The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 12661298.

  • Sato, Y., 2006: Introduction of variational bias correction technique into JMA global data assimilation system. WGNE Blue Book: Research Activities in Atmospheric and Ocean Modelling, WMO/Working Group on Numerical Experimentation, 1–19. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2006/documents/sections.html.]

    • Search Google Scholar
    • Export Citation
  • Sato, Y., Y. Takeuchi, and T. Tauchi, 2004: Use of TMI and SSM/I data in the JMA operational meso analysis. WGNE Blue Book: Research Activities in Atmospheric and Ocean Modelling, WMO/Working Group on Numerical Experimentation, 1.27–1.28. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2006/documents/sections.html.]

  • Saunders, R. W., 2008: RTTOV-9 science and validation report. EUMETSAT, 74 pp.

  • Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 14071425.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., Z. Liu, Y. Chen, and Z. Hung, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, 27, 424437.

    • Search Google Scholar
    • Export Citation
  • Takeuchi, Y., and T. Kurino, 1997: Document of algorithm to derive rain rate and precipitation with SSM/I and AMSR: Algorithm description of PIs for SSM/I and ADEOS-II/AMSR, Second AMSR Workshop, Tokyo, Japan, NASDA, 61.161.9.

  • Uesawa, D., 2009: Clear sky radiance (CSR) product from MTSAT-1R. Meteorological Satellite Center Tech. Note 52, 63 pp.

  • Warner, T. T., R. A. Peterson, and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 25992617.

    • Search Google Scholar
    • Export Citation
  • Weng, F., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64, 37993807.

  • Weng, F., L. Zhao, R. R. Ferrao, G. Poe, X. Li, and N. C. Grody, 2002: Advanced Microwave Sounding Unit cloud and precipitation algorithms. Radio Sci., 38, 8068, doi:10.1029/2002RS002679.

    • Search Google Scholar
    • Export Citation
  • WMO, 2009: Final report of the WMO RARS Implementation Group and IGDDS Implementation Group joint third meeting. 37 pp. [Available online at http://www.wmo.int/pages/prog/sat/documents/RARS-IGDDS-IG-3_Final-Report.pdf.]

  • Zapotocny, T. H., W. P. Menzel, J. A. Jung, and J. P. Nelson III, 2005: A four-season impact study of rawinsonde, GOES, and POES data in the Eta Data Assimilation System. Part I: The total contribution. Wea. Forecasting, 20, 161171.

    • Search Google Scholar
    • Export Citation
  • Zou, X., Z. Qin, and F. Weng, 2011: Improved coastal precipitation forecasts with direct assimilation of GOES-11/12 imager radiances. Mon. Wea. Rev., 139, 37113729.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 903 267 35
PDF Downloads 525 86 3