Copyright © 2016 Lenin Del Rio Amador
Lenin Del Rio Amador
During my PhD, my research work has focused on data processing, time series analysis and stochastic modelling of the atmospheric dynamics in order to make predictions based on the statistical properties of time series with structures spanning large ranges of scales (scaling, fractals, multifractals). The main results have led to the theoretical model SLIMM and the developing and operational set-up of the Stochastic Seasonal to Interannual Prediction System and website (StocSIPS) for long-range forecast of atmospheric fields.
http://www.physics.mcgill.ca/StocSIPS/
Over the past ten years, a key advance in our understanding of atmospheric variability is the discovery that between the weather and climate regime lies an intermediate “macroweather” regime, spanning the range of scales from ≈10 days to ≈30 years. Macroweather statistics are characterized by two fundamental symmetries: scaling (turbulence-like laws) and the factorization of the joint space-time statistics.
These statistical properties are fundamental for macroweather forecasting. For example:
- The temporal scaling implies that the system has a long range memory that can be exploited for forecasting.
- The low temporal intermittency implies that mathematically well-established (Gaussian) forecasting techniques can be used.
- The statistical factorization property implies that although spatial correlations are large, they are not necessarily useful in making regional forecasts.
These statistical features represent our hypothesis for building the Stochastic Linear Macroweather Model (SLIMM) and can be directly exploited by the Stochastic Seasonal to Interannual Prediction System (StocSIPS). StocSIPS is a straightforward, highly efficient forecasting system that makes global, monthly, seasonal and interannual forecasts. Using hindcasts, we compare StocSIPS with other Global Circulation Models, in particular the Environment Canada’s CanSIPS model, finding that over most of the earth, for horizons beyond about one month, StocSIPS is significantly more accurate.
StocSIPS’ advantages include:
Convergence to the real – not model - climate.
Over one million less computing time.
No need for data assimilation.
No ad hoc post processing.
No need for downscaling.
Contact Info:
Office: ERP 207, McGill University
Phone: +1 514 398 3916
Email: [email protected]
Field: Non Linear Dynamics
Links
During my MSc., I worked in the Superconductivity and Complex Phenomena groups in the Physics Faculty, University of Havana. My main focus was on experimental research and phenomenological modelling of transport properties of high-TC superconductors. Particularly on BSCCO and YBCO composite tapes, the most used materials in power applications of Superconductivity. We used an approach that combined laser cutting, microimaging, transport measurements and theoretical modelling to explain the anisotropy of the critical current and I-V characteristics of these materials.
I also had other contributions in the area of Soft Matter Physics for the study of dispersion and migration of bacteria under flow in tortuous and confined structures such as porous or fractured materials. In particular, I developed a theoretical model for explaining the behaviour of flow-controlled densification and anomalous dispersion of E. coli through a constriction.
Recent Publications
Conference Presentations
Public Presentations
2018.- Harnessing Butterflies: Theory and Practice of the Stochastic Seasonal to Interannual
Chapter in Nonlinear Advances in Geosciences, A.A. Tsonis ed. Springer Nature.
