• Bougeault, P., and Coauthors, 2010: The THORPEX Interactive Grand Global Ensemble. Bull. Amer. Meteor. Soc., 91, 10591072.

  • Bowler, N. E., , Arribas A. , , Mylne K. R. , , Robertson K. B. , , and Beare S. E. , 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703722.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., , Bidlot J.-R. , , Wedi N. , , Fuentes M. , , Hamrud M. , , Holt G. , , and Vitart F. , 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Quart. J. Roy. Meteor. Soc., 133, 681695.

    • Search Google Scholar
    • Export Citation
  • Charron, M., , Pellerin G. , , Spacek L. , , Houtekamer P. L. , , Gagnon N. , , Mitchell H. L. , , and Michelin L. , 2010: Toward random sampling of model error in the Canadian Ensemble Prediction System. Mon. Wea. Rev., 138, 18771901.

    • Search Google Scholar
    • Export Citation
  • Clark, W., , Yuan H. , , Jensen T. L. , , Wick G. , , Tollerud E. I. , , Bullock R. G. , , andSukovich E. , 2011: Evaluation of GFS water vapor forecasting errors during the 2009-2010 West Coast cool season using the MET/MODE object analysis package. Preprints, 25th Conf. on Hydrology, Seattle, WA, Amer. Meteor. Soc., 378. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper183894.html.]

  • Dettinger, M. D., 2011: Climate change, atmospheric rivers and floods in California—A multimodel analysis of storm frequency and magnitude changes. J. Amer. Water Resour. Assoc., 47, 514523.

    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., , Ralph F. M. , , Das T. , , Neiman P. J. , , and Cayan D. , 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3, 455478.

    • Search Google Scholar
    • Export Citation
  • Froude, L. S. R., 2010: TIGGE: Comparison of the prediction of Northern Hemisphere extratropical cyclones by different ensemble prediction systems. Wea. Forecasting, 25, 819836.

    • Search Google Scholar
    • Export Citation
  • Guan, B., , Molotch N. P. , , Waliser D. E. , , Fetzer E. J. , , and Neiman P. J. , 2010: Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements. Geophys. Res. Lett., 37, L20401, doi:10.1029/2010GL044696.

    • Search Google Scholar
    • Export Citation
  • Kunkee, D. B., , Swadley S. D. , , Poe G. A. , , Hong Y. , , and Werner M. F. , 2008: Special Sensor Microwave Imager Sounder (SSMIS) radiometric calibration anomalies—Part I: Identification and characterization. IEEE Trans. Geosci. Remote Sens., 46, 10171033.

    • Search Google Scholar
    • Export Citation
  • Mastin, M. C., , Gendaszek A. S. , , and Barnas C. R. , 2010: Magnitude and extent of flooding at selected river reaches in western Washington, January 2009. U.S. Geological Survey Scientific Investigations Rep. 20105177, 34 pp.

  • McMurdie, L. A., , and Mass C. , 2004: Major numerical forecast failures over the northeast Pacific. Wea. Forecasting, 19, 338356.

  • McMurdie, L. A., , and Casola J. H. , 2009: Weather regimes and forecast errors in the Pacific Northwest. Wea. Forecasting, 24, 829842.

  • Mears, C. A., , Santer B. D. , , Wentz F. J. , , Taylor K. E. , , and Wehner M. F. , 2007: Relationship between temperature and precipitable water changes over tropical oceans. Geophys. Res. Lett., 34, L24709, doi:10.1029/2007GL031936.

    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., , Ralph F. M. , , Wick G. A. , , Kuo Y.-H. , , Wee T.-K. , , Ma Z. , , Taylor G. H. , , and Dettinger M. D. , 2008a: Diagnosis of an intense atmospheric river impacting the Pacific Northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals. Mon. Wea. Rev., 136, 43984420.

    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., , Ralph F. M. , , Wick G. A. , , Lundquist J. D. , , and Dettinger M. D. , 2008b: Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations. J. Hydrometeor., 9, 2247.

    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., , Schick L. J. , , Ralph F. M. , , Hughes M. , , and Wick G. A. , 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., , and Dettinger M. D. , 2012: Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull. Amer. Meteor. Soc., 93, 783790.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., , Neiman P. J. , , and Wick G. A. , 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 17211745.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., , Neiman P. J. , , and Rotunno R. , 2005: Dropsonde observations in low-level jets over the northeastern Pacific Ocean from CALJET-1998 and PACJET-2001: Mean vertical-profile and atmospheric-river characteristics. Mon. Wea. Rev., 133, 889910.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., , Neiman P. J. , , Wick G. A. , , Gutman S. I. , , Dettinger M. D. , , Cayan D. R. , , and White A. B. , 2006: Flooding on California's Russian River: The role of atmospheric rivers. Geophys. Res. Lett., 33, L13801, doi:10.1029/2006GL026689.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., , Sukovich E. , , Reynolds D. , , Dettinger M. , , Weagle S. , , Clark W. , , and Neiman P. J. , 2010: Assessment of extreme quantitative precipitation forecasts and development of regional extreme event thresholds using data from HMT-2006 and COOP observers. J. Hydrometeor., 11, 12881306.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., , Fasullo J. , , and Smith L. , 2005: Trends and variability in column integrated atmospheric water vapor. Climate Dyn., 24, 741758, doi:10.1007/s00382-005-0017-4.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., 1995: The intercomparison of 53 SSM/I water vapor algorithms. Remote Sensing Systems Tech. Rep. on the WetNet Water Vapor Intercomparison Project (VIP), Remote Sensing Systems, Santa Rosa, CA, 19 pp.

  • Wentz, F. J., 1997: A well-calibrated ocean algorithm for Special Sensor Microwave/Imager. J. Geophys. Res., 102 (C4), 87038718.

  • Wentz, F. J., , Ricciardulli L. , , Hilburn K. , , and Mears C. A. , 2007: How much more rain will global warming bring? Science, 317, 233235.

    • Search Google Scholar
    • Export Citation
  • White, A. B., and Coauthors, 2012: NOAA's rapid response to the Howard A. Hanson Dam flood risk management crisis. Bull. Amer. Meteor. Soc., 93, 189207.

    • Search Google Scholar
    • Export Citation
  • Wick, G. A., , Kuo Y.-H. , , Ralph F. M. , , Wee T.-K. , , and Neiman P. J. , 2008: Intercomparison of integrated water vapor retrievals from SSM/I and COSMIC. Geophys. Res. Lett., 35, L21805, doi:10.1029/2008GL035126.

    • Search Google Scholar
    • Export Citation
  • Wick, G. A., , Neiman P. J. , , and Ralph F. M. , 2013: Description and validation of an automated objective technique for identification and characterization of the integrated water vapor signature of atmospheric rivers. IEEE Trans. Geosci. Remote Sens., 51, 21662176, doi:10.1109/TGRS.2012.2211024.

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

  • Yan, B., , and Weng F. , 2008: Intercalibration between Special Sensor Microwave Imager/Sounder and Special Sensor Microwave Imager. IEEE Trans. Geosci. Remote Sens., 46, 984995.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y., , and Newell R. E. , 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735.

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Evaluation of Forecasts of the Water Vapor Signature of Atmospheric Rivers in Operational Numerical Weather Prediction Models

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  • 1 NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, Colorado
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Abstract

The ability of five operational ensemble forecast systems to accurately represent and predict atmospheric rivers (ARs) is evaluated as a function of lead time out to 10 days over the northeastern Pacific Ocean and west coast of North America. The study employs the recently developed Atmospheric River Detection Tool to compare the distinctive signature of ARs in integrated water vapor (IWV) fields from model forecasts and corresponding satellite-derived observations. The model forecast characteristics evaluated include the prediction of occurrence of ARs, the width of the IWV signature of ARs, their core strength as represented by the IWV content along the AR axis, and the occurrence and location of AR landfall. Analysis of three cool seasons shows that while the overall occurrence of ARs is well forecast out to a 10-day lead, forecasts of landfall occurrence are poorer, and skill degrades with increasing lead time. Average errors in the position of landfall are significant, increasing to over 800 km at 10-day lead time. Also, there is a 1°–2° southward position bias at 7-day lead time. The forecast IWV content along the AR axis possesses a slight moist bias averaged over the entire AR but little bias near landfall. The IWV biases are nearly independent of forecast lead time. Model spatial resolution is a factor in forecast skill and model differences are greatest for forecasts of AR width. This width error is greatest for coarser-resolution models that have positive width biases that increase with forecast lead time.

Corresponding author address: Gary A. Wick, NOAA/ESRL/Physical Sciences Division, 325 Broadway, Boulder, CO 80305. E-mail: gary.a.wick@noaa.gov

Abstract

The ability of five operational ensemble forecast systems to accurately represent and predict atmospheric rivers (ARs) is evaluated as a function of lead time out to 10 days over the northeastern Pacific Ocean and west coast of North America. The study employs the recently developed Atmospheric River Detection Tool to compare the distinctive signature of ARs in integrated water vapor (IWV) fields from model forecasts and corresponding satellite-derived observations. The model forecast characteristics evaluated include the prediction of occurrence of ARs, the width of the IWV signature of ARs, their core strength as represented by the IWV content along the AR axis, and the occurrence and location of AR landfall. Analysis of three cool seasons shows that while the overall occurrence of ARs is well forecast out to a 10-day lead, forecasts of landfall occurrence are poorer, and skill degrades with increasing lead time. Average errors in the position of landfall are significant, increasing to over 800 km at 10-day lead time. Also, there is a 1°–2° southward position bias at 7-day lead time. The forecast IWV content along the AR axis possesses a slight moist bias averaged over the entire AR but little bias near landfall. The IWV biases are nearly independent of forecast lead time. Model spatial resolution is a factor in forecast skill and model differences are greatest for forecasts of AR width. This width error is greatest for coarser-resolution models that have positive width biases that increase with forecast lead time.

Corresponding author address: Gary A. Wick, NOAA/ESRL/Physical Sciences Division, 325 Broadway, Boulder, CO 80305. E-mail: gary.a.wick@noaa.gov
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