Feasibility study of the forecast of electric shocks using the Lightning Potential Index

Main Article Content

Leydi Laura Salazar Domínguez
Adrián Luis Ferrer Hernández
Lourdes Álvarez-Escudero

Abstract

The Lightning Potential Index (LPI) is a measure of the potential for generation and separation of charges that lead to lightning in convective clouds. It is calculated within the cloud separation region between  y, taking into account the speed of updrafts and microphysical parameters of the clouds. The general objective of our research is to evaluate the feasibility of the LPI to forecast electric shocks on the cuban territory, by analyzing three case studies with significant information regarding the occurrence of storms. In this study, WRF outputs are obtained with the LPI parameterization at 00:00 and 12:00 UTC for a first experiment every 1 hour and a second experiment every 1 minute, both with a resolution of 3km, which are validated with the data from the Earth Networks Total Lightning Network (ENTLN) sensor network. The results show that the LPI makes an accurate temporal forecast but overestimates the area of occurrence of electric discharges although this index is a sample of the convective activity in general. So, the LPI is a feasible tool in the short-term forecast of the electrical and convective activity.

Downloads

Download data is not yet available.

Article Details

How to Cite
Salazar DomínguezL. L., Ferrer HernándezA. L., & Álvarez-EscuderoL. (1). Feasibility study of the forecast of electric shocks using the Lightning Potential Index. Revista Cubana De Meteorología, 27(1). Retrieved from http://rcm.insmet.cu/index.php/rcm/article/view/554
Section
Original Articles

References

Davydova Belitskaya, V., Cruz, R., &, R.-L. (2016). Un modelo de verificación de pronósticos de precipitación. Aaaa model of precipitation forecast verification. Ingeniería Revista Académica, 20:24–33.
Dementyeva, S., V. Il’in, N., & A. Mareev, E. (2015). Calculation of the lightning potential index and electric field in numerical weather prediction models. Atmos. Ocean. Phys. 51: 186. https://doi.org/10.1134/S0001433815010028, pages 210–217.
Earth Networks Total Lightning Network (ENTLN) Datafeed versión 3.0. Interface Control Document (2017). EN.PM.UM 20170323.
Lecha, L. B., Paz, L. R., & Lapinel, B. (eds) (1994). El Clima de Cuba. Editorial Academia, La Habana. ISBN: 959-02-006-0.
Lynn, B. & Yair, Y. (2010). Prediction of lightning flash density with the WRF model. Advances in Geosciences, 23, 11-16.
MMM (2017). Mesoescale & Micrroscale Meteorology Division. ARW Version 3.8 Modeling System User’s Guide. Complementary to the ARW Tech Note, 411pp. Boulder, Colorado, USA.
Price, C. & Rind, D. (1992). A Simple Lightning Parameterization for Calculating Global Lightning Distributions. Journal of Geophysical Research, VOL. 97, NO. D9. NASA Goddard Institute for Space Studies, New York Columbia University, New York, pages 9919–9933.
Restrepo B, L. & Gonzales L, J. (2007). De pearson a spearman. Revista Colombiana de Ciencias Pecuarias, vol. 20, núm. 2, abril-junio. Universidad de Antioquia. Medellín, Colombia., pages 183–192.
Rubiera, J. M., Gonzáles, C., Llanes, M. T., & Bermúdez, Y. (2017). Manual de Procedimientos Operacionales Ordinarios. Sistema Nacional de pronósticos. La Habana, INSMET.
Sierra, M., Ferrer, M., Hernández, R., Gonzáles, Y., Borrajero, L., & Rodríguez, R. (2014). Sistema automático de predicción a mesoescala de cuatro ciclos diarios. Informe del resultado No.1 del Proyecto: Sistema de Predicción a muy corto plazo basado en el Acoplamiento de Modelos de Alta Resolución y Asimilación de Datos. Instituto de Meteorología de Cuba.
Sierra Lorenzo, M., Ferrer Hernández, A. L., Valdés, R., G. Mayor, Y., Carlos Cruz Rodríguez, R., Borrajero Montejo, I., Rodríguez Genó, C. F., Rodríguez, N., & Roque, A. (2015). Sistema automático de predicción a mesoescala de cuatro ciclos diarios. doi:10.13140/RG.2.1.2888.1127.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., … Powers, J. G. (2008). A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR). University Corporation for Atmospheric Research. doi:10.5065/D68S4MVH.
Wang, Y., Yang, Y., & Jin, S. (2018). Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods. Atmosphere.doi:10.3390/atmos9030099.
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., & Llasat, M. d. C. (2010). Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (wrf) model dynamic and microphysical fields. Journal of geophysical research, vol.115, D04205, doi: 10.1029/2008JD010868.
Álvarez, L. & Borrajero, I. (2015). Estudio de las marchas anual y diaria de fenómenos meteorológicos clasificados según el código de tiempo presente. Proyecto: Estudio de la distribución espacial de fenómenos meteorológicos en Cuba a partir del código de tiempo presente II (Código Programa: P211LH007-002. PNCT: ”Meteorología y Desarrollo Sostenible del País. Centro de Física de la Atmósfera. Instituto de Meteorología).
Álvarez, L. & Borrajero, I. (2018). Distribución espacial de fenómenos meteorológicos en Cuba clasificados a partir del tiempo presente I y II. Revista Cubana de Meteorología. Vol. 24, No. 1, pages 95–110 and 111–127.

Most read articles by the same author(s)