Extended-Range Probability Forecasts Based on Dynamical Model Output

Jianfu Pan Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, Maryland

Search for other papers by Jianfu Pan in
Current site
Google Scholar
PubMed
Close
and
Huug van den Dool Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, Maryland

Search for other papers by Huug van den Dool in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A probability forecast has advantages over a deterministic forecast as the former offers information about the probabilities of various possible future states of the atmosphere. As physics-based numerical models find their success in modern weather forecasting, an important task is to convert a model forecast, usually deterministic, into a probability forecast. This study explores methods to do such a conversion for NCEP’s operational 500-mb-height forecast and the discussion is extended to ensemble forecasting. Compared with traditional model-based statistical forecast methods such as Model Output Statistics, in which a probability forecast is made from statistical relationships derived from single model-predicted fields and observations, probability forecasts discussed in this study are focused on probability information directly provided by multiple runs of a dynamical model—eleven 0000 UTC runs at T62 resolution.

To convert a single model forecast into a strawman probability forecast (single forecast probability or SFP), a contingency table is derived from historical forecast–verification data. Given a forecast for one of three classes (below, normal, and above the climatological mean), the SFP probabilities are simply the conditional (or relative) frequencies at which each of three categories are observed over a period of time. These probabilities have good reliability (perfect for dependent data) as long as the model is not changed and maintains the same performance level as before. SFP, however, does not discriminate individual cases and cannot make use of information particular to individual cases. For ensemble forecasts, ensemble probabilities (EP) are calculated as the percentages of the number of members in each category based on the given ensemble samples. This probability specification method fully uses probability information provided by the ensemble. Because of the limited ensemble size, model deficiencies, and because the samples may be unrepresentative, EP probabilities are not reliable and appear to be too confident, particularly at forecast leads beyond day 6. The authors have attempted to combine EP with SFP to improve the EP probability (referred to as modified forecast probability). Results show that a simple combination (plain average) can considerably improve upon both the EP and SFP.

Corresponding author address: Dr. Huug van den Dool, Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, MD 20746.

Abstract

A probability forecast has advantages over a deterministic forecast as the former offers information about the probabilities of various possible future states of the atmosphere. As physics-based numerical models find their success in modern weather forecasting, an important task is to convert a model forecast, usually deterministic, into a probability forecast. This study explores methods to do such a conversion for NCEP’s operational 500-mb-height forecast and the discussion is extended to ensemble forecasting. Compared with traditional model-based statistical forecast methods such as Model Output Statistics, in which a probability forecast is made from statistical relationships derived from single model-predicted fields and observations, probability forecasts discussed in this study are focused on probability information directly provided by multiple runs of a dynamical model—eleven 0000 UTC runs at T62 resolution.

To convert a single model forecast into a strawman probability forecast (single forecast probability or SFP), a contingency table is derived from historical forecast–verification data. Given a forecast for one of three classes (below, normal, and above the climatological mean), the SFP probabilities are simply the conditional (or relative) frequencies at which each of three categories are observed over a period of time. These probabilities have good reliability (perfect for dependent data) as long as the model is not changed and maintains the same performance level as before. SFP, however, does not discriminate individual cases and cannot make use of information particular to individual cases. For ensemble forecasts, ensemble probabilities (EP) are calculated as the percentages of the number of members in each category based on the given ensemble samples. This probability specification method fully uses probability information provided by the ensemble. Because of the limited ensemble size, model deficiencies, and because the samples may be unrepresentative, EP probabilities are not reliable and appear to be too confident, particularly at forecast leads beyond day 6. The authors have attempted to combine EP with SFP to improve the EP probability (referred to as modified forecast probability). Results show that a simple combination (plain average) can considerably improve upon both the EP and SFP.

Corresponding author address: Dr. Huug van den Dool, Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, MD 20746.

Save
  • Akesson, O., 1996: Comparative verification of precipitation probabilities from the ECMWF ensemble prediction system and from the operational T213 forecast. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., J31–J34.

  • Anderson, J. L., 1996a: Selection of initial conditions for ensemble forecasts in a simple perfect model framework. J. Atmos. Sci.,53, 22–36.

    • Crossref
    • Export Citation
  • ——, 1996b: A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate,9, 1518–1530.

    • Crossref
    • Export Citation
  • Branstator, G. A., W. Mai, and D. Baumhefner, 1993: Identification of highly predictable flow elements for spatial filtering of medium and extended range numerical forecasts. Mon. Wea. Rev.,121, 1786–1802.

    • Crossref
    • Export Citation
  • Chen, W. Y., and H. M. van den Dool, 1995a: Low-frequency anomalies in the NMC model and reality. J. Climate,8, 1369–1385.

  • ——, and ——, 1995b: Forecast skill and low-frequency variability in the NMC DERF90 experiments. Mon. Wea. Rev.,123, 2491–2514.

    • Crossref
    • Export Citation
  • Deque, M., 1997: Ensemble size for numerical seasonal forecasts. Tellus,49A, 74–86.

    • Crossref
    • Export Citation
  • Gilman, D. L., 1986: Expressing uncertainty in long range forecasts. Namias Symposium, J. O. Roads, Ed., Scripps Institution of Oceanography Reference Series 86-17, 174–187.

  • Glahn, H. R., and D. A. Lowry, 1972: The use of model output statistics forecasting through model consensus. Bull. Amer. Meteor. Soc.,76, 1157–1164.

    • Crossref
    • Export Citation
  • Hamill, T., and S. Colucci, 1996: Eta/RSM ensemble usefulness for short-range forecasting. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., J43–J45.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis project. Bull. Amer. Meteor. Soc.,77, 437–471.

    • Crossref
    • Export Citation
  • Klinker, E., and M. Capaldo, 1986: Systematic errors in the baroclinic waves of the ECMWF model. Tellus,38A, 215–235.

    • Crossref
    • Export Citation
  • Molteni, F., and S. Tibaldi, 1990: Regimes in the wintertime circulation over northern extratropics. II: Consequences for dynamical predictability. Quart. J. Roy. Meteor. Soc.,116, 1263–1288.

  • Mureau, R., F. Molteni, and T. N. Palmer, 1993: Ensemble prediction using dynamically conditioned perturbations. Quart. J. Roy. Meteor. Soc.,119, 299–324.

    • Crossref
    • Export Citation
  • O’Lenic, E., and Coauthors, 1996: Format, methods and estimated skill of proposed operational forecasts for week two (days 8–14). Proc. 21st Annual Climate Diagnostics Workshop, Huntsville, AL, NOAA/NWS/NCEP/CPC, 162–165.

  • Pan, J., and H. van den Dool, 1995: On the geographical distribution of forecast skill in 500 hPa height in the 6–10 day range for NH winter. Proc. 20th Annual Climate Diagnostics Workshop, Seattle, WA, NOAA/NWS/NCEP/CPC, 109–112.

  • Schemm, J. E., H. M. van den Dool, and S. Saha, 1996: Application of a multi-year DERF experiment towards week 2 and monthly climate prediction. Proc. 21st Climate Diagnostics and Prediction Workshop, Huntsville, AL, NOAA/NWS/NCEP/CPC, 166–169.

  • Toth, Z., and E. K. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc.,74, 2317–2330.

    • Crossref
    • Export Citation
  • Tracton, M. S., 1990: Predictability and its relationship to scale interaction processes in blocking. Mon. Wea. Rev.,118, 1666–1695.

    • Crossref
    • Export Citation
  • ——, and E. K. Kalnay, 1993: Operational ensemble prediction at the National Meteorological Center: Practical aspects. Wea. Forecasting,8, 379–398.

  • ——, K. C. Mo, W. Chen, E. Kalnay, R. Kistler, and G. White, 1989:Dynamical extended range forecasting (DERF) at the National Meteorological Center. Mon. Wea. Rev.,117, 1604–1635.

    • Crossref
    • Export Citation
  • van den Dool, H. M., and Z. Toth, 1991: Why do forecasts for near-normal fail to succeed? Wea. Forecasting,6, 76–85.

    • Crossref
    • Export Citation
  • ——, and L. Rukhovets, 1994: On the weights for an ensemble-averaged 6–10-day forecast. Wea. Forecasting,9, 457–465.

    • Crossref
    • Export Citation
  • Vislocky, R. L., and J. M. Fritsch, 1995: Improved model output statistics forecasts through model concensus. Bull. Amer. Meteor. Soc.,76, 1157–1164.

    • Crossref
    • Export Citation
  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev.,109, 784–812.

    • Crossref
    • Export Citation
  • Wilks, D. S., 1995: Statistical methods in the atmospheric sciences. International Geophysics Sciences, R. Dmowska and J. R. Holton, Eds., Vol. 59, Academic Press, 199–283.

  • Zhu, Y., G. Lyengar, Z. Toth, S. Tracton, and T. Marchok, 1996: Objective evaluation of the NCEP gloabl ensemble forecasting system. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., J79–J82.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 287 76 14
PDF Downloads 115 41 5