Green house gas (GHG) satellites such as Orbiting Carbon Observatory 2 (OCO2), Sentinel 5P (TROPOMI), GHGSat offer global coverage of measuring column averaged carbon-di-oxide (XCO2) and methane (XCH4). Though GHG satellites are a scalable method of measuring GHG emissions, they are limited by coarse spatial and/or temporal resolutions and missing data. Based on the statistical interpolation technique, fixed rank kriging (FRK), in this work, we have developed a novel nested kriging approach, N-FRK to address the above challenges of GHG satellite data, in particular of OCO2 satellite. Compared to the spatiotemporal resolution of 1.2×2.2km2 and 16 days of OCO2 satellite, we have increased the temporal resolution to 1 day and spatial resolution to 1.11×1.11km2using N-FRK. The daily spatial maps at 111, 11.1, 1.11 km resolutions have been generated using FRK and N-FRK techniques and validated across 13 or subset of the 13 sensor sites of total carbon column observing network (TCCON) for the year 2019. As part of validation, we present the R2, root mean square error (RMSE), and bias metrics and we see good agreement between the estimated data and sensor data.

Visualization is required for the effective utilization of data from a weather simulation. Appropriate mapping of user goals to the design of pictorial content has been useful in the development of interactive applications with sufficient bandwidth for timely access to the model data. When remote access to the model visualizations is required the limited bandwidth becomes the primary bottleneck. To help address these problems, visualizations are presented on a Web page as a meta-representation of the model output and serve as an index to simplify finding other visualizations of relevance. To provide consistency with extant interactive products and to leverage their cost of development, the aforementioned applications are adapted to automatically populate a Web site with images and interactions for an operational weather forecasting system.

Non-traditional applications of scientific data challenge the typical approaches to visualization. In particular popular scientific visualization strategies fail when the expertise of the data consumer is in a different field than the one that generated the data and data from the user's domain must be utilized as well. This problem occurs when predictive weather simulations are used for a number of weather-sensitive applications. A data fusion approach is adopted for visualization design and utilized for specific example problems.

Scaling of simulations challenges the effectiveness of conventional visualization methods. This problem becomes two-fold for mesoscale weather models that operate in near-real-time at cloud-scale resolution. For example, typical approaches to vector field visualization (e.g., wind) are based upon global methods, which may not illustrate detailed structure. In addition, such computations employ multi-resolution meshes to capture small-scale phenomena, which are not properly reflected in both vector and scalar realizations. To address the former critical point analysis and simple bandpass filtering of wind fields is employed for better seed point identification of streamline calculations. For the latter, an encapsulation of nested computational meshes is developed for general realization. It is then combined with the seed point calculation for an improved vector visualization of multi-resolution weather forecasting data.

Efforts to create highly generic visualizations, both content and interface, often when applied to non research oriented or operational activities are composed of several goals. Although these goals may appear to be related, they are often composed of distinct tasks. Generic solutions, even if domain-specific, may lack sufficient focus to be effective for such purposes. The design of different visualization tools matched to a set of tasks but built on top of a common framework with a similar approach to content is a promising alternative. This hypothesis is tested in detail by application to a demanding problem-operational weather forecasting.

Modular visualization environments utilizing a data-flow execution model have become quite popular in recent years, especially those that incorporate visual programming tools. However, simplistic implementations of such an execution model are quite limited when applied to problems of realistic complexity, which negate the intuitive advantage of data-flow systems. This situation can be resolved by extending the execution model to incorporate a more complete and efficient programming infrastructure while still preserving the virtues of pure "data-flow". This approach has been used for the implementation of a general-purpose software package, IBM Visualization Data Explorer.

The paper presents an interactive approach for guiding the user's select of colormaps in visualization. PRAVDAColor, implemented as a module in the IBM Visualization Data Explorer, provides the user a selection of appropriate colormaps given the data type and spatial frequency, the user's task, and properties of the human perceptual system.

The ordinarily arid climate of coastal Peru is disturbed every few years by a phenomenon called El Nino, characterized by a warming in the Pacific Ocean. Severe rainstorms are one of the consequences of El Nino, which cause great damage. An examination of daily data from 66 rainfall stations in the Chiura-Piura region of northwestern Peru from late 1982 through mid-1983 (associated with an El Nino episode) yields information on the mesoscale structure of these storms. These observational data are typical of a class that are scattered at irregular locations in two dimensions. The use of continuous realization techniques for qualitative visualization (e.g., surface deformation or contouring) requires an intermediate step to define a topological relationship between the locations of data to form a mesh structure. Several common methods are considered, and the results of their application to the study of the rainfall events are analyzed.>

No abstract available.

In Rogowitz and Treinish (1993), we introduced an architecture for incorporating perceptual rules into the visualization process. In this architecture, higher-level descriptors of the data, metadata, flow to perceptual rules, which constrain visualization operations. In this paper, we develop a deeper analysis of the rules, the prerequisite metadata, and the system for enabling their operation.>

Direct analysis of spacecraft observations of stratospheric ozone yields information about the morphology of annual austral depletion. Visual correlation of ozone with other atmospheric data illustrates the diurnal dynamics of the polar vortex and contributions from the upper troposphere, including the formation and breakup of the depletion region each spring. These data require care in their presentation to minimize the introduction of visualization artifacts that are erroneously interpreted as data features. Non-geographically registered data of differing mesh structures can be visually correlated via cartographic warping of underlying geometries without interpolation. Since this approach is independent of realization technique, it provides a framework for experimenting with different visualization strategies. This methodology preserves the fidelity of the original data sets in a coordinate system suitable for three-dimensional, dynamic examination of upper atmospheric phenomena.>

Reports from five research centers involved with atmospheric and environmental visualization issues are presented in this case study. Visualization with heterogeneous computer architectures is highlighted in the US EPA Scientific Visualization Center discussion. The NASA Marshall Space Flight Center effort to develop the multidimensional analysis of sensor systems (MASS) environment is presented. Florida State University's building of a new scientific visualization package, Sci An, is reported. This is followed by a discussion of the design and implementation of VIS-AD, an experimental laboratory for developing scientific algorithms, at the University of Wisconsin-Madison. The visualization of global atmospheric data at IBM Thomas J. Watson Research Center is highlighted.>