ANN-based empirical model for ENSO forecast
Scientific organizationInstitute of Applied Physics of the Russian Academy of Sciences
Academic degreeMaster of physics
PositionJunior research scientist
Scientific disciplineEarth Sciences, Ecology & Environmental Management
TopicANN-based empirical model for ENSO forecast
The important task of modern science is construction of mathematical models of complex natural systems. There are two general approaches to natural systems modeling: (i) first-principle modeling, when mathematical model is based on physical laws, and (ii) empirical modeling, based on construction of model directly from data. This report is devoted to empirical modeling El-Nin'o (ENSO) from sea surface temperature anomalies data. We constructed model of evolution operator in form of artificial neural network and made forecast of the ENSO variability from several time points.
KeywordsNatural systems, empirical modeling, evolution operator, ENSO, artificial neural networks