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Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm

Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm. Maurice Schmeits, Kees Kok, Daan Vogelezang and Rudolf van Westrhenen KNMI. Outline. Introduction : Weather alarm for severe thunderstorms Method : Model output statistics (MOS)

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Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm

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  1. Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm Maurice Schmeits, Kees Kok, Daan Vogelezang and Rudolf van Westrhenen KNMI

  2. Outline • Introduction: Weather alarm for severe thunderstorms • Method: Model output statistics (MOS) • Data used inMOS system for (severe) thunderstorms • Illustration of statistical method • Definitions of predictands • Case (10 June 2007) • Verification results • Conclusions and outlook IUGG 2007

  3. Weather alarm for severe thunderstorms (I) • Weather alarm: if probability of ≥ 500 discharges/5 min./(50x50 km2) ≥ 90% in next 12 hours • One of the least predictable phenomena • History (note: other criterion): many misses, only a few hits and no false alarms • Goal: decrease number of misses and increase number of hits, while keeping number of false alarms low • Means: new objective probabilistic forecasting system IUGG 2007

  4. Model output statistics (MOS) • Aim: • Features: To determine a statistical relationship (mostly via regression) between a predictand (i.e. the occurrence of a thunderstorm in this case) and predictors from NWP model forecasts (and possibly from observations) • forecasts possible for predictands that are not available from direct model output • (reliable) probabilistic forecasts possible, even while using output from a single model run • separate regression equation for each forecast projection (correction of systematic model errors) IUGG 2007

  5. MOS system for (severe) thunderstorms Ensemble of advected radar data (0 to +6 h) (Ensemble of advected) lightning data (0 to +6 h) NWP model forecasts (0 to +12 h) Logistic regression (LR) model Probability of thunderstorms (0 to +6 h/ +6 to +12 h) • Archive: • 2/3 part for development • 1/3 part for verification In developing the LR model you need a 3/2-year long data archive IUGG 2007

  6. Example of logistic regression equation using only the first predictor (region M-MS; period: 15-21 UTC) binary predictand logistic curve Probability of thunderstorms Fraction of ensemble with no. of flashes ≥ 4 [SAFIR 1440 +0620]

  7. Weather alarm for severe thunderstorms (II) • Weather alarm: if probability of ≥ 500 discharges/5 min./(50x50 km2) ≥ 90% in next 12 hours • 2000-2005 ‘climatology’ on the basis of this criterion: only twice a year (between 30 April and 15 September) • Statistical methods are not capable of handling such rare events. • Therefore, other predictand definitions have been used. IUGG 2007

  8. Predictand definitions Predictand for thunderstorms: Probability of > 1 lightning discharge in a 6h period (00-06, 03-09, 06-12, 09-15, 12-18, 15-21, 18-00 or 21-03 UTC) in a 90x80 km2 region. Predictands for severe thunderstorms: Conditional probability of ≥ X, ≥Y or ≥Z discharges/ 5 min. in a 6h period in a 90x80 km2 region with condition > 1 discharge in the same 6h period in the same region. Here X =50 (all 6-h periods); Y = 100 and Z =200 (12-18, 15-21 and 18-00 UTC). IUGG 2007

  9. 09 UTC run (based on H 1006 and EC 0912) Case 10 June 2007 (15-21 UTC; +6 to +12 h) Probability of thunderstorms Maximum 5-min. lightning intensity Cond. prob. of severe thunderstorms (≥ 50 discharges/ 5 min.) (≥200 discharges/ 5 min.) IUGG 2007 ‘Clim.’ prob. of thunderstorms: 5-19 % ‘Clim.’ cond. prob. of severe thunderstorms (≥ 200 discharges/5 min.): 5 % (abs. prob.: < 1 %)

  10. Verification results 2006 (Probability of > 1 discharge) 0 to +6 h +6 to +12 h Brier skill score (%) Brier skill score (%) Brier skill score (%) Time (UTC) Time (UTC) IUGG 2007

  11. Reliability diagrams (’05-’06;15-21 UTC; 0 to +6h) ≥ 50 discharges/ 5 min. ≥ 100 discharges/ 5 min. Observed frequency Observed frequency Forecast probability Forecast probability IUGG 2007

  12. Conclusions and outlook • Probabilistic forecasts for thunderstorms (> 1 discharge) are skilful with respect to the 2000-2004 climatology. • Probabilistic forecasts for severe thunderstorms (≥ 50/ ≥ 100 discharges per 5 min.) are reasonably skilful with respect to the 2000-2004 climatology. • The system has been pre-operational at KNMI since Spring of 2006 and will be fully operational later this year. • It is expected that this system will help the forecasters to decide whether a weather alarm for severe thunderstorms should be issued. IUGG 2007

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