TY - GEN
T1 - Unraveling Ocean Dynamics: Exploring Temporal Variability and Climate Correlations Along the Northern Coast of Colombia
AU - Ojeda-Caicedo, V.V.
AU - Solano-Correa, Y.T.
PY - 2024
Y1 - 2024
N2 - Ocean dynamics are crucial due to their influence on the development of marine ecosystems and oceanic phenomena, such as coastal upwelling, which affect biological productivity and environmental conditions in coastal regions. Evaluating the temporal variability of oceanographic variables, including current fields, temperature, and salinity, can support decision making for effective marine ecosystem conservation. Understanding the effects of oceanographic variables influenced by climate phenomena, such as the El Nino-Southern Oscillation (ENSO), can help mitigate the impacts of climate change on the oceans. In this article, two study areas parallel to the northern coast of Colombia were chosen. Thirty years of velocity, salinity, and temperature data were acquired. Using this data, time series were obtained for each variable and each area to describe the behavior of oceanographic variables and their relationship with the ENSO climatic phenomenon. A time series model based on machine learning techniques was proposed for the 30 years (1993–2022) of monthly data for analyzing these variables.
AB - Ocean dynamics are crucial due to their influence on the development of marine ecosystems and oceanic phenomena, such as coastal upwelling, which affect biological productivity and environmental conditions in coastal regions. Evaluating the temporal variability of oceanographic variables, including current fields, temperature, and salinity, can support decision making for effective marine ecosystem conservation. Understanding the effects of oceanographic variables influenced by climate phenomena, such as the El Nino-Southern Oscillation (ENSO), can help mitigate the impacts of climate change on the oceans. In this article, two study areas parallel to the northern coast of Colombia were chosen. Thirty years of velocity, salinity, and temperature data were acquired. Using this data, time series were obtained for each variable and each area to describe the behavior of oceanographic variables and their relationship with the ENSO climatic phenomenon. A time series model based on machine learning techniques was proposed for the 30 years (1993–2022) of monthly data for analyzing these variables.
UR - https://www.scopus.com/pages/publications/85212876387
U2 - 10.1109/ENO-CANCOA61307.2024.10751054
DO - 10.1109/ENO-CANCOA61307.2024.10751054
M3 - Conference contribution
SN - 9798350387858
BT - 2024 18th National Meeting on Optics and the 9th Andean and Caribbean Conference on Optics and its Applications, ENO-CANCOA 2024 - Conference Proceedings
ER -