Differences among the Data Models Used by the Geographic Information Systems and Atmospheric Science Communities

In the Earth science disciplines, both the observational instrumentation and numerical forecasting technology used to generate data are improving so rapidly that the techniques available to manage and use the resultant datasets are struggling to keep pace. A notable example is represented by the atmospheric science discipline. As observational and model output datasets in the atmospheric science community increase in resolution, there is an increasing demand to cross the boundaries between the GIS (Geographic Information Systems) and ASIS (Atmospheric Science Information Systems) communities. For example hydrologists, who traditionally use GIS, are interested in incorporating radar information into their GIS analysis and modeling systems. On the other hand, researchers and educators in the atmospheric science are interested in integrated views of terrain, infrastructure, and demographic data (typically in GIS data systems) with atmospheric data from forecast models, satellites, and radar data. Differences in the way the two communities think about their data can give rise to difficulties in integrated analysis and display of datasets from the two disciplines. For example, the atmosphere inherently has three spatial independent variables while the GIS community focuses on two. Furthermore, the atmosphere changes on time scales much shorter than those usually considered within the GIS community. Consequently, the atmospheric scientist thinks in a 4dimensional space and requires a 4-dimensional data model. The paper presents a general, abstract view of differences between the data models of the two communities as well as a schematic description of where the data systems (traditional GIS, evolving systems based on Open GIS specifications, and traditional ASIS) overlap and where they are distinct from each other. Examples in each category are described. Finally, even for the datasets which seem to lie in the area of overlap, some of the difficulties inherent in the integration process are discussed along with solutions where they have been developed. GIS and ASIS abstract data models In the Earth sciences, there are many conceptual models for the datasets in each subdiscipline. For the purposes of this discussion, the focus will be on atmospheric science because, in many cases, the data models differ dramatically from those in the GIS community. In order to understand the ability of GIS data models to represent Atmospheric Science (AS) datasets, it is useful to consider the following questions: 1. How important is the geographic aspect for AS data? 2. How well is time modeled? 3. How much of AS semantics is captured?