Addressing Scientific Data Challenges using the ArcGIS Platform

The availability and scale of scientific data is increasing exponentially. Efficient use of these datasets is key to better understand many of today's pressing global challenges, such as climate change. Fortunately, the ArcGIS platform provides extensive functionality for reading, managing, analyzing, visualizing and sharing scientific data stored in the three formats widely used in the scientific community—netCDF, HDF, and GRIB. Recent developments in ArcGIS can be leveraged to unleash the full potential of these scientific datasets by providing the information to everybody, whenever and wherever they need it. This paper discusses the capabilities of ArcGIS and some best practices using satellite- and model-derived earth science data. It also demonstrates how to extend the data management and analytical capabilities of multidimensional data in ArcGIS using third-party Python packages.