Atmospheric science and geospatial visualization have advanced rapidly over the last ten years. Although one may think that the atmospheric sciences community should naturally use GIS, traditionally this was not the case because data models used by atmospheric sciences are often multi-dimensional, including 4D or 5D data, while also being dynamic or having elements of time in them that make their use in GIS complex. Thus, atmospheric visualization has largely developed in its own path through tools such as VisAD. This has changed in recent year, with a variety of data formats now utilized by open source and proprietary GIS. The advent of semi-structured data (SSD) has allowed data models and structure to be more flexible and mutable. More applications are now common and apply scientific visualization along with data analysis.
The use of new data models has allowed more traditional GIS data models to be integrated with those used in atmospheric science. One such tool is MeteoInfo, a tool that combines data visualization with spatial analysis. The tool itself has some basic GIS functions and its main benefit is visualizing a variety of data formats that support atmospheric data, including NOAA ISH data, NetCDF grid atmospheric data, binary GrADS data, and ASCII data.
Recent technologies incorporate Geospatial Data Abstraction Library (GDAL) with atmospheric data, allowing common formats such as HDF data to be more easily incorporated into GIS. Data integration can still be challenging. Metadata, image display, interpretation, and spatial referencing functions may need to be applied for corrections of data or adding missing data that allow rendering of HDF data using GDAL. One common problem is some atmospheric data have nine bands in raster form, while most GIS tools utilize three bands for color display. An interpretation function can be used to combine bands and make the data visualized in a three-band GIS tool such as QGIS or ArcGIS. However, data integration is still often complicated by the nature of atmospheric multidimensionality, making integration a continuing problem. Therefore, we are likely to continue see more development in this area, where atmospheric data can preserve its historic character of complex, multidimensionality and be operable within standard GIS products.
 For more on data formats between atmospheric data and GIS, see: Xie, H., Zhou, X., Vivoni, E.R., Hendrickx, J.M.H., et al. (2005) GIS-based NEXRAD Stage III precipitation database: automated approaches for data processing and visualization. Computers & Geosciences. [Online] 31 (1), 65–76. Available from: doi:10.1016/j.cageo.2004.09.009.
 For a recent treatment and tool development using GDAL to address atmospheric data, see: Jiang, Y., Sun, M. & Yang, C. (2016) A Generic Framework for Using Multi-Dimensional Earth Observation Data in GIS. Remote Sensing. [Online] 8 (5), 382.