FN Archimer Export Format
PT CHAP
TI Analysis of Sea Surface Temperature Variability Using Machine Learning
BT Chapron, B., Crisan, D., Holm, D., Mémin, E., Radomska, A. (eds) Stochastic Transport in Upper Ocean Dynamics II. STUOD 2022. Part of the Mathematics of Planet Earth book series (MPE,volume 11). Springer, Cham. Print ISBN 978-3-031-40093-3 Online ISBN 978-3-031-40094-0, https://doi.org/10.1007/978-3-031-40094-0_11. pp.247-260
AF Ouala, Said
   Chapron, Bertrand
   Collard, Fabrice
   Gaultier, Lucile
   Fablet, Ronan
AS 1:1;2:2;3:3;4:3;5:1;
FF 1:;2:PDG-ODE-LOPS-SIAM;3:;4:;5:;
C1 IMT Atlantique, Lab-STICC, Brest, France
   Ifremer, LOPS, Plouzané, France
   ODL, Locmaria-Plouzané, France
C2 IMT ATLANTIQUE, FRANCE
   IFREMER, FRANCE
   OCEANDATALAB, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
   PDG-ODE-LOPS
UM LOPS
UR https://archimer.ifremer.fr/doc/00856/96753/105300.pdf
LA English
DT Book section
AB Sea surface temperature (SST) is a critical factor in the global climate system and plays a key role in many marine processes. Understanding the variability of SST is therefore important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management. In this study, we use machine learning techniques to analyze SST anomaly (SSTA) data from the Mediterranean Sea over a period of 33 years. The objective is to best explain the temporal variability of the SSTA extremes. These extremes are revealed to be well explained through a non-linear interaction between multi-scale processes. The results contribute to better unveil factors influencing SSTA extremes, and the development of more accurate prediction models. 
PY 2024
DI 10.1007/978-3-031-40094-0_11
ID 96753
ER

EF