CLARITY is a Marie Skłodowska-Curie Individual Fellowship project, financed under the H2020-MSCA-IF-2015 call. It is a multidisciplinary collaboration at the Ca’ Foscari Department of Environmental Sciences, Informatics and Statistics (DAIS) deliberated as an attempt to incorporate physical perspectives of complex systems into climate modeling strategies, so as to help advance our understanding of climate variability and improve our ability to simulate climate evolution.
Worldwide, the city of Venice is of particular importance in terms of urban sustainability and resilience. As a part of this project we are applying our collaborative knowledge to develop more accurate models to assess the impact of climate change on future temperature and ultraviolet radiation (UVR) exposure of city’s populations. We aim to achieve the results that can be useful to plan the future of the city and, if successful, could be replicated in other communities in Europe.
Thus, CLARITY is designed around three specific goals:
- try to deepen the understanding of fluctuations inherent in climate data,
- to incorporate obtained and already existent information on data dynamics into climate modelling, and
- to apply such created analysis and modeling strategies to obtain more accurate models of the microclimatology of the city of Venice.
CLARITY uses scaling analysis methods of detrended fluctuation analysis – DFA and wavelet transform (WT) spectral analysis, to investigate climate data.
WT provides clear functional estimates of spectral properties of the fluctuating data; it represents an independent statistical confirmation of the results of detrending methods, and a tool to additionally examine cycles and cyclical consistency of our records.
CLARITY relies upon the Bayesian hierarchical modelling strategy currently under development at Ca’Foscari DAIS.
It employs a wavelet-based space-time model to capture variability in individual simulations of different data series of the same parameter, which will allow for a description of the space-time dependences of errors and their spatial and temporal propagation pathways.
This is followed by the development of a non-parametric clustering model of the Bayesian hierarchical structure, using the parameters estimated in the first stage.
A non-parametric approach is generally adopted to construct clusters of consistent models.
Classification of time series of temperature variations from climatically homogeneous regions based on long‐term persistence (2021) Sarvan D, Tošić M, Borovinić M, Blesić S. International Journal of Climatology 2021; 1– 19.
Applications of statistical physics to study climate phenomena and contribute to overall adaptation efforts (2020) Blesić S. EPL 132, 20004.
Comparison of Solar Ultraviolet Radiation Exposure in Urban Canyons in Venice, Italy and Johannesburg, South Africa (2020) Wright CY, du Preez DJ, Martincigh BS, Allen MW, Millar DA, Wernecke B, Blesić S, Photochemistry and Photobiology.
Characterization of personal solar ultraviolet radiation exposure using detrended fluctuation analysis (2020) Blesić S, du Preez DJ, Stratimirović Dj, Ajtić J, Ramotsehoa MC, Allen M, Wright C, Environmental Research 182, 108976.
Heterogeneity of scaling of the observed global temperature data (2019) Blesić S, Zanchettin D, Rubino A, Journal of Climate 32, 349-367.
Analysis of cyclical behavior in time series of stock market returns (2018) Stratimirović Dj, Sarvan D, Miljković V, Blesić S, Communications in Nonlinear Science and Numerical Simulation 54, 21-33.
Dynamics of beryllium-7 specific activity in relation to meteorological variables, tropopause height, teleconnection indices and sunspot number (2017) Sarvan D, Stratimirović Dj, Blesić S, Djurdjevic V, Miljković V, Ajtić J, Physica A 469, 813-823.
Novel approach to analysing large data sets of personal sun exposure measurements (2016) Blesić S, Stratimirović Dj, Ajtić J, Wright C, Allen M, Journal of Exposure Science & Environmental Epidemiology 26, 613-620.
Conference abstracts and talks
Researcher's Night (Veneto Night) 2017 -: Radio Ca'Foscari podcast.