Citas bibligráficas
Huamán, Z., (2022). Determining advective fog variability and stratocumulus cloud vertical structure combining a thermodynamic characterization of the marine boundary layer with a ground optical fog observation system [Pontificia Universidad Católica de Chile]. https://renati.sunedu.gob.pe/handle/sunedu/3381732https://repositorio.uc.cl/xmlui/handle/11534/63639
Huamán, Z., Determining advective fog variability and stratocumulus cloud vertical structure combining a thermodynamic characterization of the marine boundary layer with a ground optical fog observation system []. CL: Pontificia Universidad Católica de Chile; 2022. https://renati.sunedu.gob.pe/handle/sunedu/3381732https://repositorio.uc.cl/xmlui/handle/11534/63639
@mastersthesis{renati/6633,
title = "Determining advective fog variability and stratocumulus cloud vertical structure combining a thermodynamic characterization of the marine boundary layer with a ground optical fog observation system",
author = "Huamán Sevilla, Zeidy Lisseth",
publisher = "Pontificia Universidad Católica de Chile",
year = "2022"
}
The stratocumulus (Sc) cloud, responsible of the formation of advective fog at the coast of the Atacama Desert, is a valuable untapped freshwater source with potential to face the water scarcity in this region. However, little is known about features such as seasonal fog variability and cloud vertical structure that are essential to assess fog harvesting water potential. To investigate these features, a thermodynamic characterization of potential temperature (θ) and specific humidity (q) in the marine boundary layer (MBL), and a Ground Optical Fog Observation System (GOFOS) that directly measures local fog spatiotemporal variability, were combined. The vertical θ and q gradients are employed to find seasonal thresholds that define the MBL regimes related to fog formation (well-mixed MBL) and dissipation (stratified MBL); the lifting condensation level (LCL) and artificial neural networks (ANNs) are used to estimate the Sc cloud base (CB) elevation; and multiple linear regression models (MLRM) and ANNs are utilized to find the elevation of the Sc cloud top (CT). Even when a seasonal variability of the θ and q gradients was observed, the thresholds that defines the MBL regimes related to fog formation are nearly constant along the year. Fog detection using the θ vertical gradients agreed 95% of the time during winter and spring, whereas fog detection using the q gradients agreed in ~69% in the same seasons. A logistic regression analysis demonstrated that fog events can be classified with a good accuracy (0.82) using only θ as predictive variable. The CB estimated with ANNs show a better agreement (r2=0.67) with GOFOS observations throughout the year than the LCL method; with errors smaller than 4%. Finally, both MLRM and ANNs show a good agreement with GOFOS observations to estimate CT (r²=0.84 and r²=0.92, respectively; with errors smaller than 4%). Our results reveal that the essential features of fog formation and its seasonal variability can be approximated through standard meteorological observations.
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