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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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h5-index value: 14
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Volume 14
Adv. Sci. Res., 14, 187–194, 2017
https://doi.org/10.5194/asr-14-187-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 14, 187–194, 2017
https://doi.org/10.5194/asr-14-187-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Jun 2017

30 Jun 2017

Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

Stefano Federico, Marco Petracca, Giulia Panegrossi, Claudio Transerici, and Stefano Dietrich Stefano Federico et al.
  • ISAC-CNR, UOS of Rome, via del Fosso del Cavaliere 100, 00133-Rome, Italy

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy.

Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1.

Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

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This study investigates the impact of using lightning data on the precipitation forecast at different forecast ranges (3–24 h). Twenty case studies, occurred over Italy in fall 2012, are selected to show the impact. Results show the important and positive impact of using lightning data to improve the precipitation forecast. The time range, however, is very important because the performance decreases steadily and substantially with forecasting time.
This study investigates the impact of using lightning data on the precipitation forecast at...
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