Statistical–dynamical modeling of the cloud-to-ground lightning activity in Portugal

• Autor(a) principal: Sousa, J.F.
• Data de Publicação: 2013
• Outros Autores: Fragoso, Marcelo, Mendes, S., Corte-Real, J., Santos, J.A.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10451/28381
• topic: Cloud-to-ground discharge; Statistical–dynamical modeling; Lightning regime; Logistic modeling; Lightning forecasting; Portugal

The present study employs a dataset of cloud-to-ground discharges over Portugal, collected by the Portuguese lightning detection network in the period of 2003–2009, to identify dynamically coherent lightning regimes in Portugal and to implement a statistical–dynamical modeling of the daily discharges over the country. For this purpose, the high-resolution MERRA reanalysis is used. Three lightning regimes are then identified for Portugal: WREG, WREM and SREG. WREG is a typical cold-core cut-off low. WREM is connected to strong frontal systems driven by remote low pressure systems at higher latitudes over the North Atlantic. SREG is a combination of an inverted trough and a mid-tropospheric cold-core nearby Portugal. The statistical–dynamical modeling is based on logistic regressions (statistical component) developed for each regime separately (dynamical component). It is shown that the strength of the lightning activity (either strong or weak) for each regime is consistently modeled by a set of suitable dynamical predictors (65–70% of efficiency). The difference of the equivalent potential temperature in the 700–500 hPa layer is the best predictor for the three regimes, while the best 4-layer lifted index is still important for all regimes, but with much weaker significance. Six other predictors are more suitable for a specific regime. For the purpose of validating the modeling approach, a regional-scale climate model simulation is carried out under a very intense lightning episode.