Google Research

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Enhanced Natural Resource Monitoring and Process Understanding

  • Britta Daudert
  • Charles Morton
  • Daniel McEvoy
  • John Abatzoglou
  • Justin Huntington
  • Katherine Hegewisch
  • Tyler Allen Erickson
Bulletin of the American Meteorological Society (2017), 2397–2410


The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early warning systems for local-to-regional climate impacts. However, these data remain largely inaccessible for a broader segment of potential users given the computational demands of big data. Climate Engine is a web-based application that overcomes many barriers that researchers and practitioners face when accessing these large gridded datasets. Climate Engine uses Google's parallel cloud computing platform, Google Earth Engine, to enable users to process, visualize, download, and share various climate and remote sensing datasets in real-time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Secondly, several examples of analyses enabled by Climate Engine are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated through a web browser with an internet connection. The ability to access climate and remote sensing data archives with on demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding. The Climate Engine application can be found at, along with URL links to make case study example results highlighted in this article.

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