The proposed application focuses on the evaluation of hailstorms’ and thunderstorms winds’ events in the United States of America, in the period from 1996 to 2022, by a marked spatio-temporal self-exciting point process estimation. The aim of this application is the assessment and explanation of the spatio-temporal spontaneous and reproducing activity of severe hailstorms’ and thunderstorms winds’ processes. Though the spatio-temporal dynamics of the underlying spatio-temporal process are not exactly evaluable according to the self-exciting processes’ theoretical framework, the present application allows us to demonstrate how the spatio-temporal pattern is well-fitted and clearly explainable, according to the flexible semi-parametric ETAS-FLP approach.
Giada Lo Galbo, Giada Adelfio, Marcello Chiodi (2023). Severe storms events’ reproduction in the United States of America: Evaluation from the marked self-exciting point processes point of view. In Proceedings of the GRASPA 2023 Conference (pp. 211-216). Giada Adelfio; Antonino Abbruzzo.
Severe storms events’ reproduction in the United States of America: Evaluation from the marked self-exciting point processes point of view
Giada Lo Galbo
Primo
;Giada Adelfio;Marcello Chiodi
2023-07-10
Abstract
The proposed application focuses on the evaluation of hailstorms’ and thunderstorms winds’ events in the United States of America, in the period from 1996 to 2022, by a marked spatio-temporal self-exciting point process estimation. The aim of this application is the assessment and explanation of the spatio-temporal spontaneous and reproducing activity of severe hailstorms’ and thunderstorms winds’ processes. Though the spatio-temporal dynamics of the underlying spatio-temporal process are not exactly evaluable according to the self-exciting processes’ theoretical framework, the present application allows us to demonstrate how the spatio-temporal pattern is well-fitted and clearly explainable, according to the flexible semi-parametric ETAS-FLP approach.File | Dimensione | Formato | |
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