• Alhamed, A., and Lakshmivarahan S. , 2002: Cluster analysis of a multimodel ensemble data from SAMEX. Mon. Wea. Rev., 130 , 226256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anthes, R. A., 1986: The general question of predictability. Mesoscale Meteorology and Forecasting, P. S. Ray, Ed., Amer. Meteor. Soc., 636–656.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., and Miller M. J. , 1986: A new convective adjustment scheme. Part I: Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112 , 677692.

    • Search Google Scholar
    • Export Citation
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78 , 13.

  • Bright, D. R., and Mullen S. L. , 2002: The sensitivity of the numerical simulation of the southwest monsoon boundary layer to the choice of PBL turbulence parameterization in MM5. Wea. Forecasting, 17 , 99114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colle, B. A., Westrick K. J. , and Mass C. F. , 1999: Evaluation of MM5 and Eta-10 precipitation forecasts over the Pacific Northwest during the cool season. Wea. Forecasting, 14 , 137154.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colle, B. A., Olson J. B. , and Tongue J. S. , 2003a: Multiseason verification of the MM5. Part I: Comparison with the Eta Model over the central and eastern United States and impact of MM5 resolution. Wea. Forecasting, 18 , 431457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colle, B. A., Olson J. B. , and Tongue J. S. , 2003b: Multiseason verification of the MM5. Part II: Evaluation of high-resolution precipitation forecasts over the northeastern United States. Wea. Forecasting, 18 , 458479.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., Mullen S. L. , and Sanders F. , 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125 , 24272459.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., DiMego G. , Tracton M. S. , and Zhou B. , 2003: NCEP short-range ensemble forecasting (SREF) system: Multi-IC, multi-model and multi-physics approach. Research Activities in Atmospheric and Oceanic Modeling, J. Cote, Ed., CAS/JSC Working Group Numerical Experimentation Rep. 23, WMO/TD 1161, 5.09–5.10.

    • Search Google Scholar
    • Export Citation
  • Eckel, F. A., and Mass C. F. , 2005: Aspects of effective short-range ensemble forecasting. Wea. Forecasting, 20 , 328350.

  • Frank, W. M., 1983: The cumulus parameterization problem. Mon. Wea. Rev., 111 , 18591871.

  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121 , 764787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grell, G. A., Kuo Y. , and Pasch R. J. , 1991: Semiprognostic tests of cumulus parameterization schemes in the middle latitudes. Mon. Wea. Rev., 119 , 531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., 2004: Probabilistic mesoscale forecast error prediction using short-range ensembles. Ph.D. dissertation, University of Washington, 146 pp. [Available from Dept. of Atmospheric Sciences, University of Washington, Seattle, WA 98195.].

  • Grimit, E. P., and Mass C. F. , 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17 , 192205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129 , 550560.

  • Hong, S-Y., and Pan H-L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124 , 23222339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., Lefaivre L. , Derome J. , Ritchie H. , and Mitchell H. L. , 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124 , 12251242.

    • 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 , 927945.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, M. S., 2004: Evaluation of a mesoscale short-range ensemble forecasting system over the northeast U.S. M.S. thesis, Marine Sciences Research Center, Stony Brook University, 135 pp. [Available from MSRC, Stony Brook University/SUNY, Stony Brook, NY 11794-5000.].

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43 , 170181.

  • Kain, J. S., and Fritsch J. M. , 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47 , 27842802.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20 , 130141.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richardson, D. S., 2001: Ensembles using multiple models and analyses. Quart. J. Roy. Meteor. Soc., 127 , 18471864.

  • Stanski, H. R., Wilson L. J. , and Burrows W. R. , 1989: Survey of common verification methods in meteorology. Environment Canada Research Rep. 89-5, 114 pp. [Available from Forecast Research Division, Atmospheric Environment Service, 4905 Dufferin St., Downsview, ON M3H 5T4, Canada.].

  • Stensrud, D. J., and Yussouf N. , 2003: Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131 , 25102524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., Bao J. , and Warner T. T. , 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128 , 20772107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., and Kalnay E. , 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74 , 23172330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., and Kalnay E. , 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125 , 32973319.

  • Wandashin, M. S., Mullen S. L. , Stensrud D. J. , and Brooks H. E. , 2001: Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129 , 729747.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., and Seaman N. L. , 1997: A comparison study of convective parameterization schemes in a mesoscale model. Mon. Wea. Rev., 125 , 252278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in Atmospheric Science: An Introduction. Academic Press, 467 pp.

  • Yussouf, N., Stensrud D. , and Lakshmivarahan S. , 2004: Cluster analysis of multimodel ensemble data over New England. Mon. Wea. Rev., 132 , 24522462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, D-L., and Anthes R. , 1982: A high-resolution model of the planetary boundary layer—Sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor., 21 , 15941609.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, D-L., and Zheng W-Z. , 2004: Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. J. Appl. Meteor., 43 , 157169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., Snyder C. , and Rotunno R. , 2002: Mesoscale predictability of the “surprise” snowstorm of 24–25 January 2000. Mon. Wea. Rev., 130 , 16171632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., and Kuo Y-H. , 1996: Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model. Mon. Wea. Rev., 124 , 28592882.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States

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  • 1 Institute for Terrestrial and Planetary Atmospheres, Stony Brook University, Stony Brook, New York
  • | 2 NOAA/National Weather Service, Upton, New York
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Abstract

A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

Corresponding author address: Dr. Brian A. Colle, Marine Sciences Research Center, Stony Brook University, Stony Brook, NY 11794-5000. Email: brian.colle@stonybrook.edu

Abstract

A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

Corresponding author address: Dr. Brian A. Colle, Marine Sciences Research Center, Stony Brook University, Stony Brook, NY 11794-5000. Email: brian.colle@stonybrook.edu

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