Evaluate WRF-based Lightning Potential Index (LPI) lightning parameterization over Sri Lanka during second inter-monsoon in 2018
DOI:
https://doi.org/10.34117/bjdv10n1-005Keywords:
lightning prediction, LPI, Sri Lanka, WRF modelAbstract
It is important to develop accurate and reliable lightning prediction system that can be contributed towards the safety of life, both concerning forecasting for the public safety and safety of aviation and electrical power. This study aims to evaluate WRF-based Lightning Potential Index(LPI) lightning parameterization and its applicability for predicting lightning over Sri Lanka during the second inter-monsoon. The WRF-ARW model 3.9.1 was used to produce predictions for three lightning events with various physical parameterization schemes with two nested domains with a resolution of 12km and 4km respectively. The model simulated LPI values were evaluated using the Earth Networks Global Lightning (ENGLN) dataset. Results show corresponding lightning simulations were produced with spatial distribution aligned with ground-based lightning data. Results consistently show a high correlation of the LPI index with an hourly CG flash rate over the three cases. Moreover, the WRF model was able to capture the lightning using LPI in Sri Lanka, suggesting that it can be used operationally to predict lightning potential region.
References
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Elmore, K. L., Stensrud, D. J., & Crawford, K. C. (2002). Explicit cloud-scale models for operational forecasts: A note of caution. Weather and Forecasting, 17(4), 873–884. https://doi.org/10.1175/1520-0434(2002)017<0873:ECSMFO>2.0.CO;2
Fiori, E., Lagasio, M., Parodi, A., Procopio, R., Smorgonskiy, A., Rachidi, F., & Diendorfer, G. (2016). Implementation and Performance analysis of the lightning potential index as a forecasting tool. 2016 33rd International Conference on Lightning Protection, ICLP 2016, 1–6. https://doi.org/10.1109/ICLP.2016.7791514
Perler, D., & Marchand, O. (2009). A study in weather model output postprocessing: Using the boosting method for thunderstorm detection. Weather and Forecasting, 24(1), 211–222. https://doi.org/10.1175/2008WAF2007047.1
Skamarock, W. C., Klemp, J. B., Dudhi, J., Gill, D. O., Barker, D. M., Duda, M. G., … Powers, J. G. (2008). A Description of the Advanced Research WRF Version 3. Technical Report, (June), 113. https://doi.org/10.5065/D6DZ069T
Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., … Del Carmen Llasat, M. (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 Atmospheres, 115(4), 1–13. https://doi.org/10.1029/2008JD010868
Elmore, K. L., Stensrud, D. J., & Crawford, K. C. (2002). Explicit cloud-scale models for operational forecasts: A note of caution. Weather and Forecasting, 17(4), 873–884. https://doi.org/10.1175/1520-0434(2002)017<0873:ECSMFO>2.0.CO;2
Fiori, E., Lagasio, M., Parodi, A., Procopio, R., Smorgonskiy, A., Rachidi, F., & Diendorfer, G. (2016). Implementation and Performance analysis of the lightning potential index as a forecasting tool. 2016 33rd International Conference on Lightning Protection, ICLP 2016, 1–6. https://doi.org/10.1109/ICLP.2016.7791514
Perler, D., & Marchand, O. (2009). A study in weather model output postprocessing: Using the boosting method for thunderstorm detection. Weather and Forecasting, 24(1), 211–222. https://doi.org/10.1175/2008WAF2007047.1
Skamarock, W. C., Klemp, J. B., Dudhi, J., Gill, D. O., Barker, D. M., Duda, M. G., … Powers, J. G. (2008). A Description of the Advanced Research WRF Version 3. Technical Report, (June), 113. https://doi.org/10.5065/D6DZ069T
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