• Adler, R. F., and A. J. Negri, 1988: A satellite infrared technique to estimate convective and stratiform precipitation. J. Appl. Meteor, 27 , 3051.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., A. J. Negri, P. R. Keehn, and I. A. Hakkarinen, 1993: Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data. J. Appl. Meteor, 32 , 335356.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., G. J. Huffman, and P. R. Keehn, 1994: Global tropical rain estimates from microwave-adjusted geosynchronous infrared data. Remote Sens. Rev, 11 , 125152.

    • Search Google Scholar
    • Export Citation
  • Berg, W., W. Olson, R. Ferraro, S. J. Goodman, and F. J. LaFontaine, 1998: An assessment of the first and second generation navy operational precipitation retrieval algorithms. J. Atmos. Sci, 55 , 15581575.

    • Search Google Scholar
    • Export Citation
  • Businger, J. A., J. C. Wyngard, Y. Izumi, and E. F. Bradley, 1971: Flux profile relationship in the atmospheric surface layer. J. Atmos. Sci, 28 , 181189.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., 1997: Special sensor microwave imager derived global rainfall estimates for climatological applications. J. Geophys. Res, 102 , (D14),. 1671516735.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., and G. F. Marks, 1995: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J. Atmos. Oceanic Technol, 12 , 755770.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., E. A. Smith, W. Berg, and G. J. Huffman, 1998: A screening methodology for passive microwave precipitation retrieval algorithms. J. Atmos. Sci, 55 , 15831600.

    • Search Google Scholar
    • Export Citation
  • Hakkarinen, I. M., and R. F. Adler, 1988: Observations of precipitating convective systems at 92 and 183 GHz: Aircraft results. Meteor. Atmos. Phys, 38 , 164182.

    • Search Google Scholar
    • Export Citation
  • Harshvardan, and T. G. Corsetti, 1984: Longwave parameterization for the UCLA/GLAS GCM. NASA Tech. Memo. 86072, Goddard Space Flight Center, Greenbelt, MD, 52 pp.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., D. V. Ledvina, A. M. da Silva, S. Q. Zhang, J. Joiner, R. M. Atlas, G. J. Huffman, and C. D. Kummerow, 2000: Assimilation of SSM/I-derived surface rainfall and total precipitable water for improving the GEOS analysis for climate studies. Mon. Wea. Rev, 128 , 509537.

    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., 1975: On numerical prediction over a global tropical belt. Dept. of Meteorology Rep. 75-1, The Florida State University, Tallahassee, FL, 282 pp.

    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., K. Tada, K. Kudo, N. Sato, and S. Ita, 1983: Description of the JMA operational spectral model. J. Meteor. Soc. Japan, 61 , 812828.

    • Search Google Scholar
    • Export Citation
  • Kasahara, A., A. P. Mizzi, and L. J. Donner, 1994: Diabatic initialization for improvement in the tropical analysis of divergence and moisture using satellite radiometric imagery data. Tellus, 46A , 242264.

    • Search Google Scholar
    • Export Citation
  • Kitade, T., 1983: Nonlinear normal mode initialization with physics. Mon. Wea. Rev, 111 , 21942213.

  • Krishnamurti, T. N., Y. Ramanathan, H. L. Pan, R. J. Pasch, and J. Molinari, 1980: Cumulus parameterization and rainfall rates I. Mon. Wea. Rev, 108 , 465472.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., S. Low-Nam, and R. Pasch, 1983a: Cumulus parameterization and rainfall rates II. Mon. Wea. Rev, 111 , 816828.

  • Krishnamurti, T. N., S. Cocke, R. Pasch, and S. Low-Nam, 1983b: Precipitation estimates from raingauge and satellite observations summer MONEX. Rep. 83-7,. Department of Meteorology, The Florida State University, Tallahassee, FL, 373 pp.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., J. Xue, H. S. Bedi, K. Ingles, and D. Oosterhof, 1991: Physical initialization for numerical weather prediction over the tropics. Tellus, 43AB , 5381.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., G. Rohaly, and H. S. Bedi, 1994: On the improvement of precipitation forecast skill from physical initialization. Tellus, 46A , 598614.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, T. LaRow, D. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285 , 15481550.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, D. W. Shin, and C. E. Williford, 2000a: Improving tropical precipitation forecasts from a multianalysis superensemble. J. Climate, 13 , 42174227.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., and and Coauthors, 2000b: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13 , 41964216.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., W. S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens, 34 , 12131232.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol, 15 , 809817.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor, 39 , 19651982.

    • Search Google Scholar
    • Export Citation
  • Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the Earth's atmosphere. J. Atmos. Sci, 31 , 118133.

    • Search Google Scholar
    • Export Citation
  • Louis, J. F., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor, 17 , 187202.

  • Marécal, V., and J. F. Mahfouf, 2000: Variational retrieval of temperature and humidity profiles from TRMM precipitation data. Mon. Wea. Rev, 128 , 38533866.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., F. J. LaFontaine, W. L. Smith, and T. H. Achtor, 1990:: Recommended algorithms for the retrieval of rainfall rates in the tropics using the SSM/I (DMSP-8). Manuscript, University of Wisconsin, Madison, WI, 10 pp.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., C. Kummerow, G. M. Heymsfield, and L. Giglio, 1996: A method for combined passive-active microwave retrievals of cloud and precipitation profiles. J. Appl. Meteor, 35 , 17631789.

    • Search Google Scholar
    • Export Citation
  • Puri, K., and M. J. Miller, 1990: The use of satellite data in the specification of convective heating for diabatic initialization and moisture adjustment in numerical weather prediction models. Mon. Wea. Rev, 118 , 6793.

    • Search Google Scholar
    • Export Citation
  • Shin, D. W., and T. N. Krishnamurti, 1999: Improving precipitation forecasts over the global tropical belt. Meteor. Atmos. Phys, 70 , (1–2),. 114.

    • Search Google Scholar
    • Export Citation
  • Tao, W. K., and J. Simpson, 1993: Goddard cumulus ensemble model. Part I: Model description. TAO: Terr. Atmos. Oceanic, 4 , 3572.

  • Tiedtke, M., 1984: 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.

    • Search Google Scholar
    • Export Citation
  • Treadon, R. E., 1996: Physical initialization in the NMC global data assimilation system. Meteor. Atmos. Phys, 60 , 5786.

  • Treadon, R. E., 1997: Assimilation of satellite derived precipitation estimates within the NCEP. Ph.D. dissertation, The Florida State University, Tallahassee, FL, 348 pp.

    • Search Google Scholar
    • Export Citation
  • Tripoli, G. J., 1992: A nonhydrostatic model designed to simulate scale interaction. Mon. Wea. Rev, 120 , 13421359.

  • Tsuyuki, T., 1996a: Variational data assimilation in the Tropics using precipitation data. Part I: Column model. Meteor. Atmos. Phys, 60 , 87104.

    • Search Google Scholar
    • Export Citation
  • Tsuyuki, T., 1996b: Variational data assimilation in the Tropics using precipitation data. Part II: 3D model. Mon. Wea. Rev, 124 , 25452561.

    • Search Google Scholar
    • Export Citation
  • Tsuyuki, T., 1997: Variational data assimilation in the Tropics using precipitation data. Part III: Assimilation of SSM/I precipitation rates. Mon. Wea. Rev, 125 , 14471464.

    • Search Google Scholar
    • Export Citation
  • Turk, J., C-S. Liou, S. Qiu, R. Scofield, M. Ba, and A. Gruber, 2001:: Capabilities and characteristics of rainfall estimates from geostationary-and geostationary+microwave-based satellite techniques. Preprints, Symp. or Precipitation Extremes: Prediction, Impacts, and Responses, Albuquerque, NM, Amer. Meteor. Soc., 191–194.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., S. Tibaldi, and A. J. Simmons, 1983: Reduction of systematic forecast errors in the ECMWF model through the introduction of envelope orography. Quart. J. Roy. Meteor. Soc, 109 , 683718.

    • Search Google Scholar
    • Export Citation
  • Xie, P., and P. A. Arkin, 1998: Global monthly precipitation estimates from satellite-observed outgoing longwave radiation. J. Climate, 11 , 137164.

    • Search Google Scholar
    • Export Citation
  • Yanai, M., S. Esbensen, and J. H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci, 30 , 611627.

    • Search Google Scholar
    • Export Citation
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Real-Time Multianalysis–Multimodel Superensemble Forecasts of Precipitation Using TRMM and SSM/I Products

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  • * Department of Meteorology, The Florida State University, Tallahassee, Florida
  • | + Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado
  • | # NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | @ NASA Headquarters, Washington, D.C.
  • | 5 Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland
  • | * *Marine Meteorology Division, Naval Research Laboratory, Monterey, California
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Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

Corresponding author address: Dr. T. N. Krishnamurti, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-4520. Email: tnk@io.met.fsu.edu

Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

Corresponding author address: Dr. T. N. Krishnamurti, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-4520. Email: tnk@io.met.fsu.edu

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