Riccardo Silini

Riccardo Silini
Universitat Politècnica de Catalunya | UPC · DONLL

Doctor of Philosophy
Software Engineer

About

17
Publications
1,074
Reads
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29
Citations
Citations since 2017
17 Research Items
25 Citations
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Introduction
Physicist. PhD PhD topic: Causal inference and forecasting methods for climate data analysis
Education
June 2019 - July 2022
Universitat Politècnica de Catalunya
Field of study
  • Computational Physics
October 2015 - July 2017
October 2011 - July 2015

Publications

Publications (17)
Article
Reliable anomaly/outlier detection algorithms have practical applications in many fields. For instance, anomaly detection allows to filter and clean the data used to train machine learning (ML) algorithms, improving their performance. However, outlier mining is challenging when the data is high-dimensional, and different approaches have been propos...
Article
Full-text available
We evaluate causal dependencies between thirteen indices that represent large-scale climate patterns (El Nino/Southern Oscillation, the North Atlantic Oscillation, the Pacific Decadal Oscillation, etc.) using a recently proposed approach based on a linear approximation of the transfer entropy. We demonstrate that this methodology identifies causal...
Article
Full-text available
The Madden–Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10 to 90 d) timescale. An improved forecast of the MJO may have important socioeconomic impacts due to the influence of MJO on both tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their c...
Preprint
Full-text available
The Madden-Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the Tropical region. The MJO affects precipitation over highly populated areas, especially around Southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact. Indices of the MJO can be derived...
Preprint
Full-text available
We evaluate causal dependencies between thirteen indices that represent large-scale climate patterns (El Nino/Southern Oscillation, the North Atlantic Oscillation, the Pacific Decadal Oscillation, etc.) using a recently proposed approach based on an approximation of the transfer entropy. We demonstrate that this methodology identifies causal relati...
Presentation
Skillful forecast of the Madden Julian Oscillation (MJO) has an important scientific interest because the MJO represents one of the most important sources of sub-seasonal predictability. Proxies of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2). The chall...
Preprint
Full-text available
The Madden-Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10- to 90-days) time scale. An improved forecast of the MJO, may have important socioeconomic impacts due to the influence of MJO on both, tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved...
Chapter
O período entre 2018 e 2022 mostrou-nos que o problema dos incêndios à escala global não está a diminuir, antes pelo contrário. Parece que as consequências das alterações climáticas já estão a afectar a ocorrência de incêndios florestais em várias partes do Mundo, de uma forma que só esperaríamos que acontecesse vários anos mais tarde. Em muitos pa...
Article
Full-text available
The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tr...
Article
Full-text available
Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success larg...
Poster
Full-text available
Poster supporting the paper "Fast and effective pseudo transfer entropy for bivariate data-driven causal inference" https://www.researchgate.net/publication/350968847_Fast_and_effective_pseudo_transfer_entropy_for_bivariate_data-driven_causal_inference/citations

Network

Cited By

Projects

Project (1)
Project
To contribute to the improvement of the sub-seasonal predictability of extreme weather events. CAFE is a Marie S. Curie Innovative-Training-Network (ITN) Project funded by the EU. Website: http://www.cafes2se-itn.eu