Earth sciences graphics software

Abstract: Traditionally, climate is defined as the statistical collective of the weather conditions of a specified area during a specified interval of time, usually several decades. This definition is currently undergoing a change to place more emphasis on the exchange of energy, momentum, and mass between the different compartments of the Earth System. Although weather is experienced as a pure atmospheric phenomenon with high temporal variability, the long-term changes of mean weather conditions are driven by the dynamics of slowly changing components of the climate system: e.g. the ocean, sea and land ice, and the biosphere.

Abstract: Recent growth in the scale and variety of Earth science data has provided unprecedented opportunities to big data analytics research for understanding the Earth's physical processes. An upsurge of Earth science datasets in the past few decades are being continually collected using various modes of acquisition, at different scales of observation, and in diverse data types and formats. Earth science datasets, however, exhibit some unique characteristics (such as adherence to physical properties and spatiotemporal constraints) that present challenges to traditional data-centric approaches. In this article, the authors briefly introduce the different categories of Earth science datasets and further describe some of the major data-centric challenges in analyzing Earth science data.