Weather scientists are looking to better understand the atmospheric conditions. We propose a new tool to detect the most significant association between variables in the multidimensional multivariate time-varying climate datasets. In this case, we represent the correlation between variables, the uncertainty between different members within ensembles, and several clustering methods. 77w climate dataset is collected in different time steps and locations. One of the most important research questions for weather scientists is the relationship between various variables in different time steps, or dissimilar spatial locations. In this paper; we present a set of techniques to evaluate the correlation and association between different variables within a time step and spatial location. In another way, we perform static analysis on a single point in space-time, then extending that analysis either in the temporal or spatial dimensiorz(s), followed by an aggregation of the individual results to get an "overall" correlation. We created a tool that not only can he used to visualize the correlation and uncertainty between two time series of all ensembles, but also spatial locations. Mini-batch-K-Means clustering is applied to these datasets to identify the most substantial patterns within them. We study the Pearson correlation and integrate glyphs and color mapping into our design to demonstrate the trend of changing the correlation values of a single, pair: or triple of variables. Statistical calculations are applied to derive an accurate interpretation of the time-varying correlations between members within all of the ensembles as well as the uncertainty of the correlation values. The uncertainty visualizations provide insight toward the effects of parameter perturbation, sensitivity to initial conditions, and inconsistencies in model outputs. To evaluate the tool, we apply this technique to a climatology dataset.
Najmeh Abedzadeh, "An interactive tool for Analyzing the Correlation, Uncertainty, and Clustering (ACUC) over ensembles in climate dataset" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis, 2017, pp 5 - 11, https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-384