Editorial Type:
Article
Article Type:
Research Article

Statistically and Dynamically Downscaled, Calibrated, Probabilistic 10-m Wind Vector Forecasts Using Ensemble Model Output Statistics

Bryan P. Holman Department of Ocean Engineering and Sciences, Florida Institute of Technology, Melbourne, Florida

Search for other papers by Bryan P. Holman in
Current site
Google Scholar
PubMed Close
,
Steven M. Lazarus Department of Physics and Space Sciences, Florida Institute of Technology, Melbourne, Florida

Search for other papers by Steven M. Lazarus in
Current site
Google Scholar
PubMed Close
, and
Michael E. Splitt College of Aeronautics, Florida Institute of Technology, Melbourne, Florida

Search for other papers by Michael E. Splitt in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Sep 2018
DOI:
https://doi.org/10.1175/MWR-D-17-0338.1
Page(s):
2859–2880
Restricted access

Abstract

A computationally efficient method is developed that performs gridded postprocessing of ensemble 10-m wind vector forecasts. An expansive set of idealized WRF Model simulations are generated to provide physically consistent, high-resolution winds over a coastal domain characterized by an intricate land/water mask. The ensemble model output statistics (EMOS) technique is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. In a yearlong study, the method is applied to 24-h wind forecasts from the Global Ensemble Forecast System (GEFS) at 28 east-central Florida stations. Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicates that the postprocessed forecasts are calibrated. A downscaling case study illustrates the method as applied to a quiescent easterly flow event. Strengths and weaknesses of the approach are presented and discussed.

© 2018 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: Bryan P. Holman, bholman2013@my.fit.edu

Abstract

A computationally efficient method is developed that performs gridded postprocessing of ensemble 10-m wind vector forecasts. An expansive set of idealized WRF Model simulations are generated to provide physically consistent, high-resolution winds over a coastal domain characterized by an intricate land/water mask. The ensemble model output statistics (EMOS) technique is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. In a yearlong study, the method is applied to 24-h wind forecasts from the Global Ensemble Forecast System (GEFS) at 28 east-central Florida stations. Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicates that the postprocessed forecasts are calibrated. A downscaling case study illustrates the method as applied to a quiescent easterly flow event. Strengths and weaknesses of the approach are presented and discussed.

© 2018 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: Bryan P. Holman, bholman2013@my.fit.edu
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Akylas, E., V. Kotroni, and K. Lagouvardos, 2007: Sensitivity of high-resolution operational weather forecasts to the choice of the planetary boundary layer scheme. Atmos. Res., 84, 49–57, https://doi.org/10.1016/j.atmosres.2006.06.001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bao, L., T. Gneiting, E. P. Grimit, P. Guttorp, and A. E. Raftery, 2010: Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Mon. Wea. Rev., 138, 1811–1821, https://doi.org/10.1175/2009MWR3138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Beljaars, A., 1995: The parametrization of surface fluxes in large‐scale models under free convection. Quart. J. Roy. Meteor. Soc., 121, 255–270, https://doi.org/10.1002/qj.49712152203.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Berrocal, V. J., A. E. Raftery, and T. Gneiting, 2007: Combining spatial statistical and ensemble information in probabilistic weather forecasts. Mon. Wea. Rev., 135, 1386–1402, https://doi.org/10.1175/MWR3341.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Buizza, R., P. Houtekamer, G. Pellerin, Z. Toth, Y. Zhu, and M. Wei, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 1076–1097, https://doi.org/10.1175/MWR2905.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cheng, W. Y. Y., Y. Liu, Y. Liu, Y. Zhang, W. P. Mahoney, and T. T. Warner, 2013: The impact of model physics on numerical wind forecasts. Renewable Energy, 55, 347–356, https://doi.org/10.1016/j.renene.2012.12.041.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Delle Monache, L., J. P. Hacker, Y. Zhou, X. Deng, and R. B. Stull, 2006: Probabilistic aspects of meteorological and ozone regional ensemble forecasts. J. Geophys. Res., 111, D24307, https://doi.org/10.1029/2005JD006917.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Delle Monache, L., T. Nipen, Y. Liu, G. Roux, and R. Stull, 2011: Kalman filter and analog schemes to postprocess numerical weather predictions. Mon. Wea. Rev., 139, 3554–3570, https://doi.org/10.1175/2011MWR3653.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Feldmann, K., M. Scheuerer, and T. L. Thorarinsdottir, 2015: Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression. Mon. Wea. Rev., 143, 955–971, https://doi.org/10.1175/MWR-D-14-00210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gneiting, T., 2014: Calibration of medium-range weather forecasts. Tech. Memo. 719, European Centre for Medium-Range Weather Forecasts, 30 pp., https://www.ecmwf.int/sites/default/files/elibrary/2014/9607-calibration-medium-range-weather-forecasts.pdf.

  • Gneiting, T., and A. E. Raftery, 2005: Weather forecasting with ensemble methods. Science, 310, 248–249, https://doi.org/10.1126/science.1115255.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098–1118, https://doi.org/10.1175/MWR2904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gneiting, T., L. I. Stanberry, E. P. Grimit, L. Held, and N. A. Johnson, 2008: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds. TEST, 17, 211–235, https://doi.org/10.1007/s11749-008-0114-x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread–error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203–221, https://doi.org/10.1175/MWR3262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550–560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Harper, B. A., J. D. Kepert, and J. D. Ginger, 2010: Guidelines for converting between various wind averaging periods in tropical cyclone conditions. Tech. Doc. WMO/TD-1555, World Meteorological Organization, 64 pp., https://www.wmo.int/pages/prog/www/tcp/documents/WMO_TD_1555_en.pdf.

  • Holman, B. P., S. M. Lazarus, and M. E. Splitt, 2017: A fetch-based statistical method to bias correct and downscale wind speed over unresolved water bodies. Wea. Forecasting, 32, 1637–1657, https://doi.org/10.1175/WAF-D-17-0016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Homer, C. G., and Coauthors, 2015: Completion of the 2011 National Land Cover Database for the conterminous United States—Representing a decade of land cover change information. Photogramm. Eng. Remote Sens., 81, 345–354.

    • Search Google Scholar
    • Export Citation

  • Hong, S. Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hsu, S. A., E. A. Meindl, and D. B. Gilhousen, 1994: Determining the power-law wind-profile exponent under near-neutral stability conditions at sea. J. Appl. Meteor., 33, 757–765, https://doi.org/10.1175/1520-0450(1994)033<0757:DTPLWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Junk, C., L. von Bremen, M. Kühn, S. Späth, and D. Heinemann, 2014: Comparison of postprocessing methods for the calibration of 100-m wind ensemble forecasts at off-and onshore sites. J. Appl. Meteor. Climatol., 53, 950–969, https://doi.org/10.1175/JAMC-D-13-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kann, A., C. Wittmann, Y. Wang, and X. Ma, 2009: Calibrating 2-m temperature of limited-area ensemble forecasts using high-resolution analysis. Mon. Wea. Rev., 137, 3373–3387, https://doi.org/10.1175/2009MWR2793.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kleiber, W., A. E. Raftery, J. Baars, T. Gneiting, C. F. Mass, and E. Grimit, 2011: Locally calibrated probabilistic temperature forecasting using geostatistical model averaging and local Bayesian model averaging. Mon. Wea. Rev., 139, 2630–2649, https://doi.org/10.1175/2010MWR3511.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lerch, S., and S. Baran, 2017: Similarity‐based semilocal estimation of post‐processing models. J. Roy. Stat. Soc., 66, 29–51, https://doi.org/10.1111/rssc.12153.

    • Search Google Scholar
    • Export Citation

  • Mass, C. F., 2003: IFPS and the future of the national weather service. Wea. Forecasting, 18, 75–79, https://doi.org/10.1175/1520-0434(2003)018<0075:IATFOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mass, C. F., J. Baars, G. Wedam, E. Grimit, and R. Steed, 2008: Removal of systematic model bias on a model grid. Wea. Forecasting, 23, 438–459, https://doi.org/10.1175/2007WAF2006117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mass, C. F., and Coauthors, 2009: PROBCAST: A Web-based portal to mesoscale probabilistic forecasts. Bull. Amer. Meteor. Soc., 90, 1009–1014, https://doi.org/10.1175/2009BAMS2775.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nadolski, V. L., 1998: Automated Surface Observing System (ASOS) user’s guide. National Oceanic and Atmospheric Administration, Department of Defense, Federal Aviation Administration, U. S. Navy, 74 pp., https://www.weather.gov/media/asos/aum-toc.pdf.

  • Pinson, P., 2012: Adaptive calibration of (u, v)-wind ensemble forecasts. Quart. J. Roy. Meteor. Soc., 138, 1273–1284, https://doi.org/10.1002/qj.1873.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Scheuerer, M., and L. Büermann, 2014: Spatially adaptive post-processing of ensemble forecasts for temperature. J. Roy. Stat. Soc., 63, 405–422, https://doi.org/10.1111/rssc.12040.

    • Search Google Scholar
    • Export Citation

  • Scheuerer, M., and G. König, 2014: Gridded, locally calibrated, probabilistic temperature forecasts based on ensemble model output statistics. Quart. J. Roy. Meteor. Soc., 140, 2582–2590, https://doi.org/10.1002/qj.2323.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Scheuerer, M., and D. Möller, 2015: Probabilistic wind speed forecasting on a grid based on ensemble model output statistics. Ann. Appl. Stat., 9, 1328–1349, https://doi.org/10.1214/15-AOAS843.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schuhen, N., T. L. Thorarinsdottir, and T. Gneiting, 2012: Ensemble model output statistics for wind vectors. Mon. Wea. Rev., 140, 3204–3219, https://doi.org/10.1175/MWR-D-12-00028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Skamarock, W., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation

  • Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2010: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Amer. Stat. Assoc., 105, 25–35, https://doi.org/10.1198/jasa.2009.ap08615.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2013: Probabilistic wind vector forecasting using ensembles and Bayesian model averaging. Mon. Wea. Rev., 141, 2107–2119, https://doi.org/10.1175/MWR-D-12-00002.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thorarinsdottir, T. L., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics using heteroskedastic censored regression. J. Roy. Stat. Soc., 173, 371–388, https://doi.org/10.1111/j.1467-985X.2009.00616.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Toth, Z., E. Kalnay, S. M. Tracton, R. Wobus, and J. Irwin, 1997: A synoptic evaluation of the NCEP ensemble. Wea. Forecasting, 12, 140–153, https://doi.org/10.1175/1520-0434(1997)012<0140:ASEOTN>2.0.CO;2.

    • Crossref
    • 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, 3292–3302, https://doi.org/10.1175/1520-0493(1998)126<3292:TRBESA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. Academic Press, 676 pp.

  • Yussouf, N., and D. J. Stensrud, 2006: Prediction of near-surface variables at independent locations from a bias-corrected ensemble forecasting system. Mon. Wea. Rev., 134, 3415–3424, https://doi.org/10.1175/MWR3258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 5128 4710 1854
PDF Downloads 286 52 2