Entrainment and mixing processes occur during the entire life of a cloud. These processes change the droplet size distribution, which determines rain formation and radiative properties. Since it is a microphysical process, it cannot be resolved in large scale weather forecasting models. Small scale simulations such as Direct Numerical Simulations (DNS) are required to resolve the most minute scale of these processes. The DNS of cloud dynamics are performed by integrating two mathematical models, Eulerian and Lagrangian, in a coupled way. Running DNS is a tedious task as it requires a huge amount of computational resources. In this work, we provide a projection of the required resources for running DNS in different size domains. Visualizing these large simulations presents an added challenge, as they generate petabytes of data. Visualization plays a vital role in analyzing and understanding these huge data outputs. Here, we experimented with multiple tools to conduct a visual analysis of this data. Two of these tools are well established and tested technologies: ParaView and VAPOR. The others are emergent technologies in the development phase. This data simulation and visualization, in addition to exploring DNS as mentioned above, provided an opportunity to test and improve development of several tools and methods.

Visualizations enable us to detect patterns, time-evolving features, and trends in complex datasets that might not be obvious by looking at the raw data. The visual exploration process often requires comparisons between multiple visualizations, either from the same dataset or a different one, to identify relationships and patterns. This visualization process, referred to as comparative visualization, is valuable for analyzing multivariate, multispectral, or multidimensional data. The existing tools that facilitate visual comparisons do this by three means: juxtaposition (placing visuals side by side), superposition (overlaying visuals), and explicit encoding (visualizing a derived quantity corresponding to the relationship being studied). While superposition is ideal for static, geospatial datasets, where spatialization is a key component of the data, the spatiotemporal nature of Earth science datasets presents a challenge with comparative visualizations. Visual Comparator is an interactive, cross-platform (desktops, kiosks, and web), open-source application, developed to address this shortcoming. The application is used to superimpose and compare up to three synchronized, animated visualizations, and a transition between the visualizations is provided through a slider-based interface. This form of visualization has the advantage of drawing the viewers' attention to changes between the datasets, enabling comparisons of scale, and reducing the clutter caused by having multiple variables in one visual. This article provides an overview of the project, a brief review of literature pertaining to human perception research and comparative visualizations, and a guide to accessing this application.

The process of scientific visualization often involves making design choices- colors being one one them, to effectively communicate and highlight features in the data (e.g. high/low temperatures). Using the techniques of registration and image tracking, which are widely used in Augmented Reality (AR) applications to anchor digital content to the real world, an iPad/iPhone application has been developed that visualizes hand colored earth science datasets. The application would scan a student’s hand-colored page of a rectangular image of some global dataset, obtain the colors used, and convert that to an AR interactive, 3D globe with the dataset in study, animated with the students’ colors. This exercise could also be used to educate students about different map projections and is a flexible, customizable, inexpensive tool for teachers to teach a variety of geoscience topics. This engaging interactive environment could help instill a sense of ownership of the data and encourage the student to be more engaged with the science being presented.

Abstract Mixed reality taps into intuitive human perception by merging computer-generated views of digital objects (or flow fields) with natural views. Digital objects can be positioned in 3D space and can mimic real objects in the sense that walking around the object produces smoothly changing views toward the other side. Only recently have advances in gaming graphics advanced to the point that views of moving 3D digital objects can be calculated in real time and displayed together with digital video streams. Auxiliary information can be positioned and timed to give the viewer a deeper understanding of a scene; for example, a pilot landing an aircraft might “see” zones of shear or decaying vortices from previous heavy aircraft. A rotating digital globe might be displayed on a table top to demonstrate the evolution of El Niño. In this article, the authors explore a novel mixed reality data visualization application for atmospheric science data, present the methodology using game development platforms, and demonstrate a few applications to help users quickly and intuitively understand evolving atmospheric phenomena.

High‐resolution global climate modeling holds the promise of capturing planetary‐scale climate modes and small‐scale (regional and sometimes extreme) features simultaneously, including their mutual interaction. This paper discusses a new state‐of‐the‐art high‐resolution Community Earth System Model (CESM) simulation that was performed with these goals in mind. The atmospheric component was at 0.25° grid spacing, and ocean component at 0.1°. One hundred years of “present‐day” simulation were completed. Major results were that annual mean sea surface temperature (SST) in the equatorial Pacific and El‐Niño Southern Oscillation variability were well simulated compared to standard resolution models. Tropical and southern Atlantic SST also had much reduced bias compared to previous versions of the model. In addition, the high resolution of the model enabled small‐scale features of the climate system to be represented, such as air‐sea interaction over ocean frontal zones, mesoscale systems generated by the Rockies, and Tropical Cyclones. Associated single component runs and standard resolution coupled runs are used to help attribute the strengths and weaknesses of the fully coupled run. The high‐resolution run employed 23,404 cores, costing 250 thousand processor‐hours per simulated year and made about two simulated years per day on the NCAR‐Wyoming supercomputer “Yellowstone.”