Evaluation of Thunderstorm Predictors for Finland Using Reanalyses and Neural Networks

Peter Ukkonen, Agostino Manzato, Antti Mäkelä

Journal of Applied Meteorology and Climatology 2017 article cited by 31

Source More in Climate & Environment

importance 4/10 partially solved Meteorological Phenomena and Simulations weather forecasting stage: research

Problem

Identifying thunderstorm occurrences over Finland from reanalysis data using multiple environmental predictors.

Why it matters

Thunderstorm forecasting protects people, aviation, and property from severe convective weather.

What the AI did

Artificial neural networks trained on 14 summers of ERA-Interim reanalysis and lightning data identified thunderstorms from complex multivariate predictor combinations.

Results

Best ANN used 15 inputs, Heidke skill score 0.51 on independent test sample; substantial improvement over best single predictor.

Impact

ANN-based multivariate models substantially improved thunderstorm identification over single predictors.

Record

Venue	Journal of Applied Meteorology and Climatology
Published	2017
Work type	article
Authors	Peter Ukkonen, Agostino Manzato, Antti Mäkelä
Citations	31 (OpenAlex, at import time)
References	56
Model or method	artificial neural networks (ANN)
Organization	not stated in source
Primary source	https://doi.org/10.1175/jamc-d-16-0361.1
Open access	closed
Keywords	neural networks, thunderstorm prediction, reanalysis, Heidke skill score, convection

Importance rating

4 / 10

real but narrower advance

Improves multivariate thunderstorm identification from reanalysis data, benefiting the forecasting community; abstract gives no scale of people, aviation, or property affected.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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