Operational forecasters and weather researchers need accurate visualization of atmospheric data from both computational models and observed data. Although these two applications share some requirements, they have different needs and goals. We've developed a visualization tool for atmospheric science researchers and research weather forecasters that allows the 3D visualization of measured radar data and rendered numerical model data to show the 3D structures as well as how the weather event would look when observed in the field. Our system lets us load the original data directly onto the graphics hardware, with the grid mapping from the rendering space to the grid space programmed on the GPU. This method is flexible enough to handle the grids important in meteorological research and enables the application of advanced visualization methods available in texture-based slicing systems. The visually accurate rendering of weather data can be useful for training weather spotters, evaluating forecasting models, training forecasters to interpret radar data, and comparing sensor data to observed weather events.

Rendering of atmospheric bodies involves modeling the complex interaction of light throughout the highly scattering medium of water and air particles. Scattering by these particles creates many well-known atmospheric optical phenomena including rainbows, halos, the corona, and the glory. Unfortunately, most radiative transport approximations in computer graphics are ill-suited to render complex angularly dependent effects in the presence of multiple scattering at reasonable frame rates. Therefore, this paper introduces a multiple-model lighting system that efficiently captures these essential atmospheric effects. We have solved the rendering of fine angularly dependent effects in the presence of multiple scattering by designing a lighting approximation based upon multiple scattering phase functions. This model captures gradual blurring of chromatic atmospheric optical phenomena by handling the gradual angular spreading of the sunlight as it experiences multiple scattering events with anisotropic scattering particles. It has been designed to take advantage of modern graphics hardware; thus, it is capable of rendering these effects at near interactive frame rates.

Weather visualization has traditionally been restricted to surface models and 2-D representations. We present a visually accurate method for rendering volumetric multi-field weather data that includes cloud water, ice, rain, snow, and graupel hydrometeors. This representation better communicates the complex 3-D nature of weather, by rendering it according to physically based lighting and scattering characteristics of hydrometeor particles. The rendering system works with the new Weather Research and Forecasting (WRF) NWP model as well as cumulus cloud dynamics models. We have successfully rendered a simulated WRF supercell storm at interactive rates with high visual accuracy. Therefore, improved NWP model evaluation can be achieved through this new visually accurate system. This software has potential uses in, not only weather forecasting and research, but in the education of weather spotters and the general public.

Weather visualization is a difficult problem because it comprises volumetric multi-field data and traditional surface-based approaches obscure details of the complex three-dimensional structure of cloud dynamics. Therefore, visually accurate volumetric multi-field visualization of storm scale and cloud scale data is needed to effectively and efficiently communicate vital information to weather forecasters, improving storm forecasting, atmospheric dynamics models, and weather spotter training. We have developed a new approach to multi-field visualization that uses field specific, physically-based opacity, transmission, and lighting calculations per-field for the accurate visualization of storm and cloud scale weather data. Our approach extends traditional transfer function approaches to multi-field data and to volumetric illumination and scattering.

perational forecasters and weather researchers need accurate visualization of atmospheric data from both computational models and observed data. Although these two applications share some requirements, they have different needs and goals. Atmospheric phenomena are visualized at various scales and on a wide variety of grids, so weather visualization packages must have flexible sampling systems. Tools that can handle a wide variety of grids let researchers apply many powerful techniques within atmospheric research, such as simulation and measurement data comparisons and multiple model analysis. Researchers often choose meteorological grid structures based on efficient computation or limitations of the measurement system, complicating the mapping from the Carte-sian rendering space to the data's computational space. Although uniform grids form the basis for a wide variety of applications and are comparatively easy to sample, many applications, including fluid dynamics simulations, weather models, ocean models, and Doppler measurement, use nonuni-form, structured grids that provide higher sampling densities in areas of greater variation, interest, and accuracy. Unfortunately, these nonuniform grid structures are more difficult to manage in texture-based volume-rendering applications (see the " Previous Work in Visualizing Nonuniform Grids " sidebar for other work in this area). Although we can resample the data to a uniform grid, this method often introduces grid artifacts not present in the original data, and can require significantly more storage. The grid should also be as small as possible so the relevant data fields can fit into the graphics processing unit (GPU) memory. We've developed a visualization tool for atmospheric science researchers and research weather forecasters that allows the 3D visualization of measured radar data and rendered numerical model data to show the 3D structures as well as how the weather event would look when observed in the field. Our system lets us load the original data directly onto the graphics hardware, with the grid mapping from the rendering space to the grid space programmed on the GPU. This method is flexible enough to handle the grids important in meteorological research and enables the application of advanced visualization methods available in texture-based slicing systems. The visually accurate rendering of weather data can be useful for training weather spotters, evaluating forecasting models, training forecasters to interpret radar data, and comparing sensor data to observed weather events. Since volume rendering's introduction in 1988, it has been an essential 3D visualization tool. Applying modern graphics hardware to the volume-rendering problem has allowed for complex …

New active pharmaceutical ingredients (APIs) continue to be discovered from the natural world. These compounds can be further developed into a range of useful products such as drugs, supplements, or cosmeceuticals. To determine the biological target of the API, the new compounds are evaluated for their biological activity through a combination of in silico, in vitro, and in vivo analyses. These results will determine where the molecular target tissue is located for the API. However, identification of the target is only the beginning of pharmaceutical and cosmeceutical development. A common roadblock in the development of products is the evaluation and manipulation of the permeability of an API through biologically relevant membranes.