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Lightning Prediction Using Model Output Statistics

Author

Listed:
  • Thorsten Simon
  • Georg J. Mayr
  • Nikolaus Umlauf
  • Achim Zeileis

Abstract

A method to predict lightning by postprocessing numerical weather prediction (NWP) output is developed for the region of the European Eastern Alps. Cloud-to-ground flashes-detected by the ground-based ALDIS network-are counted on the 18x18 km² grid of the 51-member NWP ensemble of the European Centre of Medium-Range Weather Forecasts (ECMWF). These counts serve as target quantity in count data regression models for the occurrence and the intensity of lightning events. The probability whether lightning occurs or not is modelled by a binomial distribution. For the intensity a hurdle approach is employed, for which the binomial distribution is combined with a zero-truncated negative binomial to model the counts within a grid cell. In both statistical models the parameters of the distributions are described by additive predictors, which are assembled by potentially nonlinear terms of NWP covariates. Measures of location and spread of approx. 100 direct and derived NWP covariates provide a pool of candidates for the nonlinear terms. A combination of stability selection and gradient boosting selects influential terms. Markov chain Monte Carlo (MCMC) simulation estimates the final model to provide credible inference of effects, scores and predictions. The selection of terms and MCMC simulation are applied for data of the year 2016, and out-of-sample performance is evaluated for 2017. The occurrence model outperforms a reference climatology-based on seven years of data-up to a forecast horizon of 5 days. The intensity model is calibrated and also outperforms climatology for exceedance probabilities, quantiles, and full predictive distributions.

Suggested Citation

  • Thorsten Simon & Georg J. Mayr & Nikolaus Umlauf & Achim Zeileis, 2018. "Lightning Prediction Using Model Output Statistics," Working Papers 2018-14, Faculty of Economics and Statistics, Universität Innsbruck.
    • Handle: RePEc:inn:wpaper:2018-14
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    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, January.
    2. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    3. Nadja Klein & Thomas Kneib & Stefan Lang, 2015. "Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 405-419, March.
    4. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    5. Nikolaus Umlauf & Nadja Klein & Achim Zeileis, 2017. "BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond)," Working Papers 2017-05, Faculty of Economics and Statistics, Universität Innsbruck.
    6. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    lightning detection data; distributional regression; count data model; gradient boosting; MCMC;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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