NWP-based lightning prediction using flexible count data regression
<p>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 (CG) flashes – detected by the ground-based Austrian Lightning Detection & Information System (ALDIS) network – are cou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-02-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://www.adv-stat-clim-meteorol-oceanogr.net/5/1/2019/ascmo-5-1-2019.pdf |
| Summary: | <p>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 (CG) flashes – detected by the ground-based Austrian
Lightning Detection & Information System (ALDIS) network – are counted on
the <span class="inline-formula">18×18</span> km<span class="inline-formula"><sup>2</sup></span> grid of the 51-member NWP ensemble of the European
Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the
target quantity in count data regression models for the occurrence of
lightning events and flash counts of CG. The probability of lightning
occurrence is modelled by a Bernoulli distribution. The flash counts are
modelled with a hurdle approach where the Bernoulli distribution is combined
with a zero-truncated negative binomial. In the statistical models the
parameters of the distributions are described by additive predictors, which
are assembled using potentially nonlinear functions of NWP covariates.
Measures of location and spread of 100 direct and derived NWP covariates
provide a pool of candidates for the nonlinear terms. A combination of
stability selection and gradient boosting identifies the nine (three) most
influential terms for the parameters of the Bernoulli (zero-truncated
negative binomial) distribution, most of which turn out to be associated with
either convective available potential energy (CAPE) or convective
precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final
model to provide credible inference of effects, scores, and
predictions. The selection of terms and MCMC sampling 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 7 years of
data – up to a forecast horizon of 5 days. The flash count model is
calibrated and also outperforms climatology for exceedance probabilities,
quantiles, and full predictive distributions.</p> |
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| ISSN: | 2364-3579 2364-3587 |