Lightning Forecast Using Data Mining Techniques On Hourly Evolution Of The Convective Available Potential Energy

Abstract

This study presents a method developed for lightning forecasting in eastern Amazonia, based on the estimates of the hourly evolution of the convective available potential energy (CAPE). The CAPE is a computed index of the air stability situation over a given area of the Earth. This parameter is determined from vertical profiles of temperature and humidity of the atmosphere, obtained through radiosondes. The CAPE values may also be estimated during the period between soundings, by using the meteorological variables observed continuously at surface weather stations. Two data mining techniques were used for the forecasts: k-Nearest Neighbor and Decision Tree. For the calculation of the CAPE and its estimated hourly evolution, we used radio soundings data made available by a site of the University of Wyoming, in addition to surface temperature data provided by the METAR code, both collected at the Belém-Brazil airport, during 2009. The CAPE index levels, indicative of strong convection in the area were compared to data of actual lightning activity, provided by the STARNET detection system, in a circular area of 100 km radius, centered at that airport. The angular coefficient of the adjusted line equation to the hourly evolution values of the CAPE and the average value of the CAPE were the predicting attributes, while the number of lightning flashes detected by the STARNET was the classification attribute. The results indicated that it is possible to predict the lightning class of occurrences with an accuracy of the 70%, in this research area.

Classification of Rice Varieties Using Artificial Intelligence Methods

“…8. DT general structure [34] In DT, the root node represents the attribute. The inner node indicates the test or evaluation of a property.…”

Meta-analysis of vaterite secondary data revealed the synthesis conditions for polymorphic control

“…Besides their limitations, DTs are able to solve a wide array of classification problems. For instance, among their applications can be cited citation networks [16], pharmaceutical manufacturing process [17], modelling building energy demand [18], weather forecast [19], diagnosis of diseases [8], [9], detection of forest fires [20], agriculture [21], finance [22], computer vision and many more [23].…”

Analyzing and Forecasting Lightning Flashes and the Related Wind Gusts at a Wind Energy Power Plant in a Hilly Region of Western Greece

“…with LFC the Level of Free Convection, EL the Equilibrium Level, θe the equivalent potential temperature of the air parcel and θes the saturated equivalent potential temperature of the atmospheric environment. CAPE stands for positive differences between θe and θes meaning that the pseudo-adiabatic of the displaced air parcel is warmer than the environment resulting to instability situations (Sá et al 2011;Das 2017). In other words, it is the amount of energy an airparcel would have in case it were vertically lifted to a certain height in the atmosphere.…”

“…In the literature there is a great number of works based on the artificial intelligence and data mining techniques (e.g. Sá et al 2011;Bates et al 2018;Schön et al 2019;Mostajabi et al 2019;Shrestha et al 2019). An extensive survey of several research papers can be found in Bala et al 2017. In this work we examine the occurrence of extreme wind gusts around a wind power plant, located in a hilly region of Western Greece, based on the presence of cumulonimbus clouds and lightning activity.…”