PARVIS: Parallel Analysis Tools and New Visualization Techniques for Ultra-Large Climate Data Sets
The goal of this project is to develop a new Parallel Gridded Analysis Library (ParGAL) that will vastly improve the speed of climate data analysis compared to the current serial tools.
The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations.
New and faster methods for visualization will be needed in order to get the most knowledge out of the new generation of high-resolution climate models. Climate visualizations are typically two-dimensional (surface temperature, zonally averaged wind) because the climate system has a very small aspect ratio: it is thousands of kilometers wide but only a few kilometers deep. The bottleneck to producing these images is not the drawing of the image on the screen but rather the calculations that must be performed on the climate model output before an image can be made.
We will speed up this post-processing, or analysis, phase of climate visualization by developing a new Parallel Gridded Analysis Library (ParGAL) that will vastly improve the speed of climate data analysis compared to the current serial tools. ParGAL will build on currently available software technology that will also permit calculations to be performed in parallel on many different numerical grids. We will interface ParGAL with a popular climate modeling tool: the NCAR Command Language (NCL). We will further enhance the current visualization paradigm by adding parallel scripting abilities using Swift.