Rare, anomalous non‐transient lights associated with cumulonimbus clouds

Hale, Rodney

doi:10.1002/wea.3247

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…We can cite, among others, agriculture, economy, telecommunications, aviation safety and recreation. Indeed, they play a key role in the water and energy cycles and are often accompanied by lightning, visibility and thunderstorms (Hale, 2018(Hale, , 2012Mbane, 2009Mbane, , 2015. They are sometimes accompanied by severe weather conditions causing turbulence, structural damage and safety problems for air navigation (Daïka et al, 2021).…”

Section: I-introductionmentioning

confidence: 99%

Quantification and Forecasting of Cumulonimbus (Cb) Clouds Direction, Nebulosity and Occurrence With Autoregression Using 2018-2020 Dataset From Yaounde-nsimalen

Ringnwi

Augustin

Rodrigue

et al. 2022

Preprint

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This works reports the quantification and forecasting of Cumulonimbus (Cb) clouds direction, nebulosity and occurrence with auto regression using 2018-2020 dataset from Yaoundé –Nsimalen of Cameroon. Data collected for October 2018-2020 consisted of 2232 hourly observations. Codes were written to automatically align, multi-find and replace data points in excel to facilitate treating big datasets. The approach included quantification of direction generating time series from data and determination of model orders using the correlogram. The coefficients of the SARIMA model were determined using Yule-Walker equations in matrix form, the Augmented Dickey Fuller test (ADF) was used to check for stationarity assumption, Portmanteau test to check for white noise in residuals and Shapiro-Wilk test to check normality assumptions. After writing several algorithms to test different models, an Autoregressive Neural Network (ANN) was fitted and used to predict the parameters over window of 2 weeks. Autocorrelation Function (ACF) shows no correlation between residuals, with p ≤ 0.05, fitting the stationarity assumption. Average performance is 80%. A stationary wavelike occurrence of the direction has been observed, with East as the most frequented sector. Forecast of Cb parameters is important in effective air traffic management, creating situational awareness and could serve as reference for future research. The method of decomposition could be made applicable in future research to quantify/forecast cloud directions.

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Section: I-introductionmentioning

confidence: 99%