Ensemble simulations are one of the primary sources of uncertain data sets in scientific studies. While modeling and measuring a real-world phenomenon via simulations, the lack of knowledge regarding the ground-truth compels the scientists to use multiple initial conditions and/or different input parameters to get an estimate of the possible outcomes. The resulting ensemble data sets are used for decision making in real world and thus, are of prime importance to the weather and the geo-scientists. At GRAVITY lab, we have proposed various tools and techniques to analyze and visualize such ensemble datasets. Using information theoretic measures we quantify and visualize the uncertainty of ensemble features like isosurfaces and streamlines. We also develop effective visual analytic solutions to study the effect of input parameters and initial conditions on the ensemble results by performing various types of sensitivity analysis.