Horizontal grid spacing comparison among Random Forest algorithms to nowcast Cloud-to-Ground lightning occurrence

Fata, Alice La; Amato, Federico; Bernardi, M.; D’Andrea, Mirko; Procopio, Renato; Fiori, Elisabetta

doi:10.1007/s00477-022-02222-1

Cited by 7 publications

(2 citation statements)

References 68 publications

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“…According to [2], the presence of lightning is a reliable indicator of intense convective activity typical of storm cloud formation environments. Several studies related to the detection of deep convective clouds include lightning data [3,[22][23][24]64,75,76].…”

Section: Post-processing Lighting Filtermentioning

confidence: 99%

Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models

Dávila-Ortiz,

Carbajal-Pérez,

Velázquez-Zapata

et al. 2024

Remote Sensing

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The presence of deep convective clouds is directly related to potential convective hazards, such as lightning strikes, hail, severe storms, flash floods, and tornadoes. On the other hand, Mexico has a limited and heterogeneous network of instruments that allow for efficient and reliable monitoring and forecasting of such events. In this study, a quasi-real-time framework for deep convective cloud identification and modeling based on machine learning (ML) models was developed. Eight different ML models and model assembly approaches were fed with Interest Fields estimated from Advanced Baseline Imager (ABI) sensor data on the Geostationary Operational Environmental Satellite-R Series (GOES-R) for one region in central Mexico and another in northeastern Mexico, both selected for their intense convective activity and high levels of vulnerability to severe weather. The results indicate that a simple approach such as Logistic Regression (LR) or Random Forest (RF) can be a good alternative for the identification and simulation of deep convective clouds in both study areas, with a probability of detection of (POD) ≈ 0.84 for Los Mochis and POD of ≈ 0.72 for Mexico City. Similarly, the false alarm ratio (FAR) ≈ 0.2 and FAR ≈ 0.4 values were obtained for Los Mochis and Mexico City, respectively. Finally, a post-processing filter based on lightning incidence (Lightning Filter) was applied with data from the Geostationary Lightning Mapper (GLM) of the GOES-16 satellite, showed great potential to improve the probability of detection (POD) of the ML models. This work sets a precedent for the implementation of an early-warning system for hazards associated with intense convective activity in Mexico.

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Section: Post-processing Lighting Filtermentioning

confidence: 99%

Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models

Dávila-Ortiz,

Carbajal-Pérez,

Velázquez-Zapata

et al. 2024

Remote Sensing

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“…A nowcasting model for predicting the occurrence of cloud-to-ground lightning strikes was developed using a RF classifier and the application of available meteorological parameters. The ML model is a useful tool for short-term lightning prediction and enhancing measures for lightning protection [16].…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia

Rufus,

Ahmad,

Abdullah

et al. 2023

2023 International Symposium on Lightning Protection (XVII SIPDA)

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Thunderstorms are one of the most destructive natural phenomena on the planet, as they are predominantly associated with lightning and heavy rainfall that result in human deaths, urban flooding, and agricultural damage. Thus, accurate thunderstorm prediction is essential for planning and managing agriculture, flood control, and air traffic control. This study utilized historical lightning and meteorological data from 2011 to 2018 in the southern regions of Peninsular Malaysia to predict thunderstorm occurrences. The lightning dataset is classified into three class ranges, where the high range of lightning rarely occurs in this region compared to the low and medium ranges of lightning because of the non-linear and complex characteristics of the thunderstorm and lightning itself, leading to an imbalanced dataset. The k-fold and stratified cross-validation (CV) methods and a resampling technique called SMOTE are introduced to overcome the imbalance in the training dataset. Then the dataset is trained and tested using five Machine Learning (ML) algorithms, including Decision Trees (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB). The results haveshown that the GB ML model using stratified k-fold CV and SMOTE is the best algorithm for thunderstorm prediction for this region, with accuracy ranging from 74% to 95%, recall ranging from 72% to 93%, precision ranging from 76% to 97%, and F1-Score ranging from 74% to 95%. Future thunderstorm predictions based on lightning patterns and meteorological datasets are expected to establish an early strategy to address the presence of thunderstorms by notifying the relevant authorities, to prevent any damage that may be caused by the thunderstorms.

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Spatiotemporal data science: theoretical advances and applications

Amato

Lombardo

Tonini

et al. 2022

Stoch Environ Res Risk Assess

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Horizontal grid spacing comparison among Random Forest algorithms to nowcast Cloud-to-Ground lightning occurrence

Cited by 7 publications

References 68 publications

Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models

Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models

A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia

Spatiotemporal data science: theoretical advances and applications

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