Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)
This addresses the challenge of assessing climate change impacts on precipitation for environmental and urban planning, though it is incremental as it builds on existing DTW methods.
The study tackled the problem of evaluating precipitation variability evolution under climate change by developing a novel shape-based variant of Dynamic Time Warping for clustering rainfall time series, finding that precipitation variability increased over the years in the Paris area.
Studying the impact of climate change on precipitation is constrained by finding a way to evaluate the evolution of precipitation variability over time. Classical approaches (feature-based) have shown their limitations for this issue due to the intermittent and irregular nature of precipitation. In this study, we present a novel variant of the Dynamic time warping method quantifying the dissimilarity between two rainfall time series based on shapes comparisons, for clustering annual time series recorded at daily scale. This shape based approach considers the whole information (variability, trends and intermittency). We further labeled each cluster using a feature-based approach. While testing the proposed approach on the time series of Paris Montsouris, we found that the precipitation variability increased over the years in Paris area.