Observing System Experiments in a 3DVAR Data Assimilation System at CPTEC/INPE

Helena Barbieri de Azevedo Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, São Paulo, Brazil

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Luis Gustavo Gonçalves de Gonçalves Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, São Paulo, Brazil

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Carlos Frederico Bastarz Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, São Paulo, Brazil

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Bruna Barbosa Silveira Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, São Paulo, Brazil

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Abstract

The Center for Weather Forecast and Climate Studies [Centro de Previsão e Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] has recently operationally implemented a three-dimensional variational data assimilation (3DVAR) scheme based on the Gridpoint Statistical Interpolation analysis system (GSI). Implementation of the GSI system within the atmospheric global circulation model from CPTEC/INPE (AGCM-CPTEC/INPE) is hereafter referred to as the Global 3DVAR (G3DVAR) system. The results of an observing system experiment (OSE) measuring the impacts of radiosonde, satellite radiance, and GPS radio occultation (RO) data on the new G3DVAR system are presented here. The observational impact of each of these platforms was evaluated by measuring the degradation of the geopotential height anomaly correlation and the amplification of the RMSE of the wind. Losing the radiosonde, GPS RO, and satellite radiance data in the OSE resulted in negative impacts on the geopotential height anomaly correlations globally. Nevertheless, the strongest impacts were found over the Southern Hemisphere and South America when satellite radiance data were withheld from the data assimilation system.

© 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 e-mail: Helena de Azevedo, helenabdeazevedo@gmail.com

Abstract

The Center for Weather Forecast and Climate Studies [Centro de Previsão e Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] has recently operationally implemented a three-dimensional variational data assimilation (3DVAR) scheme based on the Gridpoint Statistical Interpolation analysis system (GSI). Implementation of the GSI system within the atmospheric global circulation model from CPTEC/INPE (AGCM-CPTEC/INPE) is hereafter referred to as the Global 3DVAR (G3DVAR) system. The results of an observing system experiment (OSE) measuring the impacts of radiosonde, satellite radiance, and GPS radio occultation (RO) data on the new G3DVAR system are presented here. The observational impact of each of these platforms was evaluated by measuring the degradation of the geopotential height anomaly correlation and the amplification of the RMSE of the wind. Losing the radiosonde, GPS RO, and satellite radiance data in the OSE resulted in negative impacts on the geopotential height anomaly correlations globally. Nevertheless, the strongest impacts were found over the Southern Hemisphere and South America when satellite radiance data were withheld from the data assimilation system.

© 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 e-mail: Helena de Azevedo, helenabdeazevedo@gmail.com
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  • Alpert, J., M. Kanamitsu, P. Caplan, J. Sela, G. White, and E. Kalnay, 1988: Mountain induced gravity wave drag parameterization in the NMC-MRF model. Preprints, Eighth Conf. on Numerical Weather Prediction, Baltimore, MD, Amer. Meteor. Soc., 726–733.

  • Andreoli, R. V., S. H. S. Ferreira, L. F. Sapucci, R. A. F. Souza, R. W. B. Mendonça, D. L. Herdies, and J. A. Aravéquia, 2008: Contribuição de diversos sistemas de observação na previsão de tempo no CPTEC/INPE. Rev. Bras. Meteor., 23, 218237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., 2002: Observing systems forecast experiments at the DAO. Symp. on Observations, Data Assimilation, and Probabilistic Prediction, Orlando, FL, Amer. Meteor. Soc., 2.1. [Available online at https://ams.confex.com/ams/pdfpapers/30633.pdf.]

  • Bauer, P., G. Radnóti, S. Healy, and C. Cardinali, 2014: GNSS radio occultation constellation observing system experiments. Mon. Wea. Rev., 142, 555572, doi:10.1175/MWR-D-13-00130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonavita, M., 2014: On some aspects of the impact of GPSRO observations in global numerical weather prediction. Quart. J. Roy. Meteor. Soc., 140, 25462562, doi:10.1002/qj.2320.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouttier, F., and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-VAR data assimilation system. Quart. J. Roy. Meteor. Soc., 127, 14691488, doi:10.1002/qj.49712757419.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavalcanti, I. F. A., and Coauthors, 2002: Global climatological features in a simulation using CPTEC–COLA AGCM. J. Climate, 15, 29652988, doi:10.1175/1520-0442(2002)015<2965:GCFIAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Memo. NASA/TM-1999-104606, Vol. 15, 38 pp. [Available online at https://gmao.gsfc.nasa.gov/pubs/docs/Chou136.pdf.]

  • Cohn, S. E., A. da Silva, J. Guo, M. Sienkiewicz, and D. Lamich, 1998: Assessing the effects of data selection with the DAO physical-space statistical analysis system. Mon. Wea. Rev., 126, 29132926, doi:10.1175/1520-0493(1998)126<2913:ATEODS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cucurull, L., and R. A. Anthes, 2014: Impact of infrared, microwave, and radio occultation satellite observations on operational numerical weather prediction. Mon. Wea. Rev., 142, 41644186, doi:10.1175/MWR-D-14-00101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • English, S., and Coauthors, 2013: Impact of satellite data. ECMWF Tech. Memo. 711, 46 pp. [Available online at http://www.ecmwf.int/sites/default/files/elibrary/2013/9301-impact-satellite-data.pdf.]

  • Fisher, M., and E. Andersson, 2001: Developments in 4D-Var and Kalman filtering. ECMWF Tech. Memo. 347, 36 pp. [Available online at http://www.ecmwf.int/sites/default/files/elibrary/2001/9409-developments-4d-var-and-kalman-filtering.pdf.]

  • Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, doi:10.1029/2002GL015311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harshvardhan, R. Davis, D. A. Randall, and T. G. Corsetti, 1987: A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92, 10091016, doi:10.1029/JD092iD01p01009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herdies, D. L., J. A. Aravéquia, S. H. S. Ferreira, R. V. A. Souza, L. F. Sapucci, and J. G. Z. Mattos, 2008: A assimilação de dados no CPTEC/INPE. Bol. Soc. Bras. Meteor., 32, 5764.

    • Search Google Scholar
    • Export Citation
  • Jung, J., 2013: Impacts on global forecasts: Conventional vs satellite data. JCSDA Quarterly, No. 42, Joint Center for Satellite Data Assimilation, College Park, MD, 2–3. [Available online at https://www.star.nesdis.noaa.gov/jcsda/documents/newsletters/201303JCSDAQuarterly.pdf.]

  • Kelly, G., J. N. Thépaut, R. Buizza, and C. Cardinali, 2007: The value of observations. I: Data denial experiments for the Atlantic and the Pacific. Quart. J. Roy. Meteor. Soc., 133, 18031815, doi:10.1002/qj.150.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kinter, L. K., and Coauthors, 1997: The COLA atmosphere–biosphere general circulation model. Vol. 1: Formulation. COLA Rep. 51, 46 pp. [Available online at http://cola.gmu.edu/people/Shukla%27s%20Articles/1997/The%20COLA%20Atmosphere.pdf.]

  • 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, 16911705, doi:10.1175/2009WAF2222201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kubota, P. Y., 2012: Variabilidade da energia armazenada na superfície e seu impacto na definição do padrão de precipitação na América do Sul. Ph.D. thesis, Instituto Nacional de Pesquisas Espaciais, 253 pp. [Available online at http://urlib.net/8JMKD3MGP7W/3CCP5R2.]

  • Lupu, C., P. Gauthier, and S. LaRoche, 2011: Evaluation of the impact of observations on analyses in 3D- and 4D-Var based on information content. Mon. Wea. Rev., 139, 726737, doi:10.1175/2010MWR3404.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maciel, A. P. R., 2009: Avaliação do novo modelo global do CPTEC/INPE na previsão numérica de tempo de fenomenos tropicais. M.S. thesis, Instituto Nacional de Pesquisas Espaciais, 115 pp. [Available online at http://urlib.net/sid.inpe.br/mtc-m18@80/2009/03.23.18.09.]

  • McNally, T., 2012: Observing system experiments to assess the impact of possible future degradation of the global satellite observing network. ECMWF Tech. Memo. 672, 20 pp. [Available online at http://www.ecmwf.int/en/elibrary/11085-observing-system-experiments-assess-impact-possible-future-degradation-global.]

  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panetta, J., S. R. M. Barros, J. P. Bonatti, S. S. Tomita, and P. Y. Kubota, 2006: Computational cost of CPTEC AGCM at use of high performance computing in meteorology. Proc. 12th Workshop on the Use of High Performance Computing in Meteorology, Reading, United Kingdom, ECMWF, 65–83, doi:10.1142/9789812775894_0006.

    • Crossref
    • Export Citation
  • Rasch, P., and J. Kristjansson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parametrizations. J. Climate, 11, 15871614, doi:10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slingo, J. M., 1987: The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899927, doi:10.1002/qj.49711347710.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Talagrand, O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc. Japan, 75, 191209.

  • Tarasova, T., and B. Fomin, 2007: The use of new parameterization for gaseous absorption in the CLIRAD-SW solar radiation code for models. J. Atmos. Oceanic Technol., 24, 11571162, doi:10.1175/JTECH2023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1983: The sensitivity of the time mean large scale flow to cumulus convection in the ECMWF model. Proc. Workshop on Convection in Large Scale Numerical Models, Reading, United Kingdom, ECMWF, 297–316.

  • Tsuyuki, T., and T. Miyoshi, 2007: Recent progress of data assimilation methods in meteorology. J. Meteor. Soc. Japan, 85B, 331361, doi:10.2151/jmsj.85B.331.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla, 1991: A simplified biosphere model for global climate studies. J. Climate, 4, 345364, doi:10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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