The stochastic resonance mechanism in the Aerosol Index dynamics
S. De Martino, M. Falanga, L. Mona
Abstract
We consider Aerosol Index (AI) time-series extracted from TOMS archive for an area covering Italy (7-18o E ; 36-47o N). The missing of convergence in estimating the embedding dimension of the system and the inability of the Independent Component Analysis (ICA) in separating the fluctuations from deterministic component of the signals are evidences of an intrinsic link between the periodic behavior of AI and its fluctuations. We prove that these time series are well described by a stochastic dynamical model. Moreover, the principal peak in the power spectrum of these signals can be explained whereby a stochastic resonance, linking variable external factors, such as Sun-Earth radiation budget and local insolation, and fluctuations on smaller spatial and temporal scale due to internal weather and antrophic components.
Create a lesson
Related papers
Linking Horizontal and Vertical Submesoscale Lagrangian Dynamics South of the Ibiza Channel
Sara Cloux, Ismael Hernandez-Carrasco, Alejandro Orfila et al.
Cumulus parameterization controls simulated rainfall more than microphysics, but not at heavy thresholds: a 3 * 2 factorial WRF study over the Western Himalaya
Satyam Kumar, Akshay Sunil, Devesh Sheoran
Extreme events and statistics in an AI emulator of a global storm-resolving model
Anna Kwa, W. Andre Perkins, Troy Arcomano et al.
The hypervariational method: a general, automated technique based on innovations to tune the parameters of the Hybrid covariance matrix
Francesco Sardelli, Craig H. Bishop
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa
David Sagna, Redouane Lguensat, Samo Diatta
Forecasting carbon-risk hotspots ahead of the extreme 2026 El Niño
Xiaomeng Du, Runda Jiang, Philippe Ciais et al.