• Applequist, S., Gahrs G. E. , Pfeffer R. L. , and Niu X-F. , 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17 , 783799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blanchard, D. O., 1998: Assessing the vertical distribution of convective available potential energy. Wea. Forecasting, 13 , 870877.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boyden, C. J., 1963: A simple instability index for use as a synoptic parameter. Meteor. Mag., 92 , 198210.

  • Bradbury, T. A. M., 1977: The use of wet-bulb potential temperature charts. Meteor. Mag., 106 , 233251.

  • Brelsford, W. M., and Jones R. H. , 1967: Estimating probabilities. Mon. Wea. Rev., 95 , 570576.

  • Charba, J. P., 1979: Two to six hour severe local storm probabilities: An operational forecast system. Mon. Wea. Rev., 107 , 12631274.

    • Search Google Scholar
    • Export Citation
  • Craven, J. P., Jewell R. E. , and Brooks H. E. , 2002: Comparison between observed convective cloud-base heights and lifting condensation level for two different lifted parcels. Wea. Forecasting, 17 , 885890.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glahn, H. R., and Lowry D. A. , 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11 , 12031211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Golding, B. W., 2000: Quantitative precipitation forecasting in the UK. J. Hydrol., 239 , 286305.

  • Haklander, A. J., and Van Delden A. , 2003: Thunderstorm predictors and their forecast skill for the Netherlands. Atmos. Res., 67–68 , 273299.

    • Search Google Scholar
    • Export Citation
  • Holleman, I., 2007: Bias adjustment and long-term verification of radar-based precipitation estimates. Meteor. Appl., 14 , 195203.

  • Hsu, W-R., and Murphy A. H. , 1986: The attributes diagram: A geometrical framework for assessing the quality of probability forecasts. Int. J. Forecasting, 2 , 285293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jefferson, G. J., 1963a: A further development of the instability index. Meteor. Mag., 92 , 313316.

  • Jefferson, G. J., 1963b: A modified instability index. Meteor. Mag., 92 , 9296.

  • Jefferson, G. J., 1966: Letter to the editor. Meteor. Mag., 95 , 381382.

  • Kain, J. S., Baldwin M. E. , and Weiss S. J. , 2003: Parameterized updraft mass flux as a predictor of convective intensity. Wea. Forecasting, 18 , 106116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., Weiss S. J. , Levit J. J. , Baldwin M. E. , and Bright D. R. , 2006: Examination of convection-allowing configurations of the WRF model for the prediction of severe convective weather: The SPC/NSSL Spring Program 2004. Wea. Forecasting, 21 , 167181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kitzmiller, D. H., Samplatsky F. G. , Mello C. , and Dai J. , 2002: Probabilistic forecasts of severe local storms in the 0–3 hour timeframe from an advective-statistical technique. Preprints, 21st Conf. on Severe Local Storms/19th Conf. on Weather Analysis and Forecasting/15th Conf. on Numerical Weather Prediction/, San Antonio, TX, Amer. Meteor. Soc., JP1.10. [Available online at http://ams.confex.com/ams/pdfpapers/46765.pdf.].

  • Kruizinga, S., 1979: Objective classification of daily 500 mbar patterns. Preprints, Sixth Conf. on Probability and Statistics in the Atmospheric Sciences, Banff, AB, Canada, Amer. Meteor. Soc., 126–129.

  • Mason, S. J., 2004: On using “climatology” as a reference strategy in the Brier and ranked probability skill scores. Mon. Wea. Rev., 132 , 18911895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCalla, C., and Kalnay E. , 1988: Short and medium range forecast skill and the agreement between operational models. Preprints, Eighth Conf. on Numerical Weather Prediction, Baltimore, MD, Amer. Meteor. Soc., 634–640.

  • Miller, R. C., 1967: Notes on analysis and severe storm forecasting procedures of the Military Weather Warning Center. AWS Tech. Rep. 200, U.S. Air Force, Scott AFB, IL, 94 pp. [Available from Headquarters, AWS, Scott AFB, IL 62225.].

  • Molteni, F., Buizza R. , Palmer T. N. , and Petroliagis T. , 1996: The new ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122 , 73119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moncrieff, M. W., and Miller M. J. , 1976: The dynamics and simulation of tropical cumulonimbus and squall lines. Quart. J. Roy. Meteor. Soc., 102 , 373394.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noteboom, S., 2006: Processing, validatie, en analyse van bliksemdata uit het SAFIR/FLITS systeem (in Dutch). KNMI Internal Rep. IR-2006-01, 71 pp. [Available from KNMI, P.O. Box 201, 3730 AE De Bilt, Netherlands.].

  • Schmeits, M. J., Kok C. J. , and Vogelezang D. H. P. , 2005: Probabilistic forecasting of (severe) thunderstorms in the Netherlands using model output statistics. Wea. Forecasting, 20 , 134148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmeits, M. J., Kok C. J. , Vogelezang D. H. P. , and van Westrhenen R. M. , 2007: Kansverwachtingen voor onweer ten behoeve van uitgifte weeralarm (KOUW). KNMI Internal Rep. IR-2007-03, 22 pp. [Available online at http://www.knmi.nl/publications/fulltexts/kouw_ir.pdf.].

  • Showalter, A. K., 1953: A stability index for thunderstorm forecasting. Bull. Amer. Meteor. Soc., 34 , 250252.

  • Thompson, P. D., 1977: How to improve accuracy by combining independent forecasts. Mon. Wea. Rev., 105 , 228229.

  • Undén, P., and Coauthors, 2002: HIRLAM-5 Scientific Documentation. SMHI, Norrköping, Sweden, 144 pp.

  • Wessels, H. R. A., 1998: Evaluation of a radio interferometry lightning positioning system. KNMI Scientific Rep. WR-98–04, De Bilt, Netherlands, 26 pp.

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

  • Williams, E., and Coauthors, 1999: The behavior of total lightning activity in severe Florida thunderstorms. Atmos. Res., 51 , 245265.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2 2 2
PDF Downloads 2 2 2

Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands

View More View Less
  • 1 Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
Restricted access

Abstract

The development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands. The system consists of logistic regression equations for both the probability of thunderstorms and the conditional probability of severe thunderstorms in the warm half-year (from mid-April to mid-October). These equations have been derived for 12 regions of about 90 km × 80 km each and for projections out to 12 h in advance (with 6-h periods). As a source for the predictands, reprocessed total lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset not only consisted of the combined postprocessed output from two numerical weather prediction (NWP) models, as in previous work by the first three authors, but it also contained an ensemble of advected radar and lightning data for the 0–6-h projections. The NWP model output dataset contained 17 traditional thunderstorm indices, computed from a reforecasting experiment with the High-Resolution Limited-Area Model (HIRLAM) and postprocessed output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Brier skill scores and attributes diagrams show that the skill of the MOS thunderstorm forecast system is good and that the severe thunderstorm forecast system generally is also skillful, compared to the 2000–04 climatology, and therefore, the preoperational system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in 2008.

Corresponding author address: Dr. M. J. Schmeits, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE De Bilt, Netherlands. Email: schmeits@knmi.nl

Abstract

The development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands. The system consists of logistic regression equations for both the probability of thunderstorms and the conditional probability of severe thunderstorms in the warm half-year (from mid-April to mid-October). These equations have been derived for 12 regions of about 90 km × 80 km each and for projections out to 12 h in advance (with 6-h periods). As a source for the predictands, reprocessed total lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset not only consisted of the combined postprocessed output from two numerical weather prediction (NWP) models, as in previous work by the first three authors, but it also contained an ensemble of advected radar and lightning data for the 0–6-h projections. The NWP model output dataset contained 17 traditional thunderstorm indices, computed from a reforecasting experiment with the High-Resolution Limited-Area Model (HIRLAM) and postprocessed output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Brier skill scores and attributes diagrams show that the skill of the MOS thunderstorm forecast system is good and that the severe thunderstorm forecast system generally is also skillful, compared to the 2000–04 climatology, and therefore, the preoperational system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in 2008.

Corresponding author address: Dr. M. J. Schmeits, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE De Bilt, Netherlands. Email: schmeits@knmi.nl

Save