Ensemble Prediction of Atmospheric Refractivity Conditions for EM Propagation

Qingyun Zhao Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Tracy Haack Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Justin McLay Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Carolyn Reynolds Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Abstract

An ensemble forecast system has been developed at the Naval Research Laboratory to improve the analyses and forecasts of atmospheric refractivity for electromagnetic (EM) propagation with the intention of accounting for uncertainties in model forecast errors. Algorithms for a matrix of ensemble statistics have been developed to analyze the probability, location, intensity, and structure of ducting of various types. Major parameters of ducting layers and their ensemble statistics are calculated from the ensemble forecasts. Their relationships to the large-scale and mesoscale environment are also investigated. The Wallops Island field experiment from late April to early May 2000 is selected to evaluate the system. During the spring season, this coastal region maintains a strong sea surface temperature gradient between cold shelf waters and the warm Gulf Stream, where the boundaries between land, the coastal water, and the Gulf Stream have a strong influence on marine boundary layer structures and the formation of ducting layers. Sounding profiles during the field experiment are used in the study to further understand the structures of the ducting layers and also to validate the ensemble forecast system. While some advantages of the ensemble system over the deterministic forecast for atmospheric refractivity prediction in the boundary layer are studied and demonstrated in this study, the weaknesses of the current ensemble system are revealed for future improvement of the system.

Corresponding author address: Dr. Qingyun Zhao, Naval Research Laboratory, 7 Grace Hopper Ave, Mail Stop II, Monterey, CA 93943. E-mail: allen.zhao@nrlmry.navy.mil

Abstract

An ensemble forecast system has been developed at the Naval Research Laboratory to improve the analyses and forecasts of atmospheric refractivity for electromagnetic (EM) propagation with the intention of accounting for uncertainties in model forecast errors. Algorithms for a matrix of ensemble statistics have been developed to analyze the probability, location, intensity, and structure of ducting of various types. Major parameters of ducting layers and their ensemble statistics are calculated from the ensemble forecasts. Their relationships to the large-scale and mesoscale environment are also investigated. The Wallops Island field experiment from late April to early May 2000 is selected to evaluate the system. During the spring season, this coastal region maintains a strong sea surface temperature gradient between cold shelf waters and the warm Gulf Stream, where the boundaries between land, the coastal water, and the Gulf Stream have a strong influence on marine boundary layer structures and the formation of ducting layers. Sounding profiles during the field experiment are used in the study to further understand the structures of the ducting layers and also to validate the ensemble forecast system. While some advantages of the ensemble system over the deterministic forecast for atmospheric refractivity prediction in the boundary layer are studied and demonstrated in this study, the weaknesses of the current ensemble system are revealed for future improvement of the system.

Corresponding author address: Dr. Qingyun Zhao, Naval Research Laboratory, 7 Grace Hopper Ave, Mail Stop II, Monterey, CA 93943. E-mail: allen.zhao@nrlmry.navy.mil
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  • Abaza, M., F. Anctil, V. Fortin, and R. Turcotte, 2013: A comparison of the Canadian global and regional meteorological ensemble prediction systems for short-term hydrological forecasting. Mon. Wea. Rev., 141, 34623476, doi:10.1175/MWR-D-12-00206.1.

    • Search Google Scholar
    • Export Citation
  • Atkinson, B. W., and M. Zhu, 2005: Radar-duct and boundary-layer characteristics over the area of the Gulf. Quart. J. Roy. Meteor. Soc., 131, 19231953, doi:10.1256/qj.04.113.

    • Search Google Scholar
    • Export Citation
  • Atkinson, B. W., and M. Zhu, 2006: Coastal effects on radar propagation in atmospheric ducting conditions. Meteor. Appl., 13, 5362, doi:10.1017/S1350482705001970.

    • Search Google Scholar
    • Export Citation
  • Atkinson, B. W., J.-G. Li, and R. S. Plant, 2001: Numerical modeling of the propagation environment in the atmospheric boundary layer over the Persian Gulf. J. Appl. Meteor., 40, 586603, doi:10.1175/1520-0450(2001)040<0586:NMOTPE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Burk, S. D., and W. T. Thompson, 1997: Mesoscale modeling of summertime refractive conditions in the Southern California Bight. J. Appl. Meteor., 36, 2231, doi:10.1175/1520-0450(1997)036<0022:MMOSRC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Daley, R., and E. Barker, 2001: NAVDAS source book 2001. Naval Research Laboratory Rep. NRL/PU/7530–01-441, 161 pp. [Available online at http://www.dtic.mil/dtic/tr/fulltext/u2/a396883.pdf.]

  • Doyle, J. D., and T. T. Warner, 1993: The impact of the sea surface temperature resolution on mesoscale coastal processes during GALE IOP 2. Mon. Wea. Rev., 121, 313334, doi:10.1175/1520-0493(1993)121<0313:TIOTSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Du, J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 24272459, doi:10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grisogono, B., L. Strom, and M. Tjernstrom, 1998: Small-scale variability in the coastal atmospheric boundary layer. Bound.-Layer Meteor., 88, 2346, doi:10.1023/A:1000933822432.

    • Search Google Scholar
    • Export Citation
  • Haack, T., C. Wang, S. Garrett, A. Glazer, J. Mailhot, and R. Marshall, 2010: Mesoscale modeling of boundary layer refractivity and atmospheric ducting. J. Appl. Meteor. Climatol., 49, 24372457, doi:10.1175/2010JAMC2415.1.

    • Search Google Scholar
    • Export Citation
  • Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 14141430, doi:10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hogan, T. F., and T. E. Rosmond, 1991: The description of the Navy Operational Global Atmospheric Prediction System’s spectral forecast model. Mon. Wea. Rev., 119, 17861815, doi:10.1175/1520-0493(1991)119<1786:TDOTNO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 12251242, doi:10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McLay, J. G., C. H. Bishop, and C. A. Reynolds, 2008: Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev., 136, 10931108, doi:10.1175/2007MWR2010.1.

    • Search Google Scholar
    • Export Citation
  • McLay, J. G., C. H. Bishop, and C. A. Reynolds, 2010: A local formulation of the ensemble transform (ET) analysis perturbation scheme. Wea. Forecasting, 25, 985993, doi:10.1175/2010WAF2222359.1.

    • Search Google Scholar
    • Export Citation
  • Palmer, T., F. Molteni, R. Mureau, R. Buizza, P. Chapelet, and J. Tribbia, 1992: Ensemble prediction. ECMWF Research Department Tech. Memo. 188, 45 pp. [Available online at http://www.ecmwf.int/sites/default/files/elibrary/1992/11560-ensemble-prediction.pdf.]

  • Peng, M. S., J. A. Ridout, and T. F. Hogan, 2004: Recent modifications of the Emanuel convective scheme in the Navy Operational Global Atmospheric Prediction System. Mon. Wea. Rev., 132, 12541268, doi:10.1175/1520-0493(2004)132<1254:RMOTEC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., J. G. McLay, J. S. Goerss, E. A. Serra, D. Hodyss, and C. R. Sampson, 2011: Impact of resolution and design on the U.S. Navy global ensemble performance in the tropics. Mon. Wea. Rev., 139, 21452155, doi:10.1175/2011MWR3546.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, W. T., and T. Haack, 2011: An investigation of sea surface temperature influence on microwave refractivity: The Wallops-2000 experiment. J. Appl. Meteor. Climatol., 50, 23192337, doi:10.1175/JAMC-D-10-05002.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, W. T., T. Haack, J. D. Doyle, and S. D. Burk, 1997: A nonhydrostatic mesoscale simulation of the 10–11 June 1994 coastally trapped wind reversal. Mon. Wea. Rev., 125, 32113230, doi:10.1175/1520-0493(1997)125<3211:ANMSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 23172330, doi:10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tracton, M. S., and E. Kalnay, 1993: Operational ensemble prediction at the National Meteorological Center: Practical aspects. Wea. Forecasting, 8, 379398, doi:10.1175/1520-0434(1993)008<0379:OEPATN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and A. F. Loughe, 1998: The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev., 126, 32923302, doi:10.1175/1520-0493(1998)126<3292:TRBESA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924, doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Science. 2nd ed. Academic Press, 627 pp.

  • Zhang, F., Z. Meng, and A. Aksoy, 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134, 722736, doi:10.1175/MWR3101.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., Y. Weng, J. Sippel, Z. Meng, and C. Bishop, 2009: Cloud resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 21052125, doi:10.1175/2009MWR2645.1.

    • Search Google Scholar
    • Export Citation
  • Zhao, Q., F. Zhang, T. Holt, C. Bishop, and Q. Xu, 2013: Development of a mesoscale ensemble data assimilation system at the Naval Research Laboratory. Wea. Forecasting, 28, 13221336, doi:10.1175/WAF-D-13-00015.1.

    • Search Google Scholar
    • Export Citation
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