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

Huntington, Justin L.; Hegewisch, Katherine C.; Daudert, Britta; Morton, Charles G.; Abatzoglou, John T.; McEvoy, Daniel J.; Erickson, Tyler

doi:10.1175/BAMS-D-15-00324.1

i

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

2017
By Huntington, Justin L. ; Hegewisch, Katherine C. ; Daudert, Britta ; ...

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Journal Title:

Bulletin of the American Meteorological Society
Personal Author:

Huntington, Justin L. ; Hegewisch, Katherine C. ; Daudert, Britta ; Morton, Charles G. ; Abatzoglou, John T. ; McEvoy, Daniel J. ; Erickson, Tyler
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office)
Description:

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. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share 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. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. 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.
Keywords:

Climatology Remote Sensing Remote Sensing
Source:

Bulletin of the American Meteorological Society, 98(11), 2397-2410
DOI:

https://doi.org/10.1175/BAMS-D-15-00324.1
ISSN:

0003-0007 ; 1520-0477
Format:

PDF
Publisher:

American Meteorological Society
Document Type:

Journal Article
Funding:

Grant no. AB-133E-16-cQ-0022
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:c7ebb86ece210d6577953a4c8e3f8058e1fd9a624d848b2b69d76b05084b216b8b03b5879f70307cf59f8eb4f24daa129062ea149062d3baf7897b1e989fc5a2
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https://repository.library.noaa.gov/view/noaa/70947/noaa_70947_DS1.pdf
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