Visual scalability of spatial ensemble uncertainty

Weather Research and Forecasting (WRF) models simulate weather conditions by generating 2D numerical weather prediction ensemble members either through perturbing initial conditions or by changing different parameterization schemes, e.g., cumulus and microphysics schemes. These simulations are often used by weather analysts to analyze the nature of uncertainty attributed by these simulations to forecast weather conditions with good accuracy. The number of simulations used for forecasting is growing with the advent of increase in computing power. Hence, there is a need for providing better visual insights of uncertainty with growing number of ensemble members. We propose a geo visual analytical framework that uses visual analytics approach to resolve visual scalability of these ensemble members. Our approach naturally fits with the workflow of an analyst analyzing ensemble spatial uncertainty. Meteorologists evaluated our framework qualitatively and found it to be effective in acquiring insights of spatial uncertainty associated with multiple ensemble runs that are simulated using multiple parameterization schemes.