The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding
Abstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.06.031 · Cited by 14,719
High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.66 · Cited by 6,362
High-resolution mapping of global surface water and its long-term changes
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016) · Nature · doi:10.1038/nature20584 · Cited by 5,513
The NCEP Climate Forecast System Reanalysis
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al. (2010) · Bulletin of the American Meteorological Society · doi:10.1175/2010bams3001.1 · Cited by 5,136
Cloud computing: state-of-the-art and research challenges
Zhang, Q., Cheng, L., & Boutaba, R. (2010) · Journal of Internet Services and Applications · doi:10.1007/s13174-010-0007-6 · Cited by 3,515
Impact of anthropogenic climate change on wildfire across western US forests
Abatzoglou, J. T., & Williams, A. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1607171113 · Cited by 3,073
Self-citationA Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain
Daly, C., Neilson, R. P., & Phillips, D. L. (1994) · Journal of Applied Meteorology · doi:10.1175/1520-0450(1994)033<0140:astmfm>2.0.co;2 · Cited by 2,782
Landsat-8: Science and product vision for terrestrial global change research
Roy, D. P., Wulder, M. A., Loveland, T. R., C.E., W., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., et al. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.02.001 · Cited by 2,659
Self-citationDevelopment of gridded surface meteorological data for ecological applications and modelling
Abatzoglou, J. T. (2013) · International Journal of Climatology · doi:10.1002/joc.3413 · Cited by 1,916
Self-citation
Cited by
Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat‐Capdevila, A., & Verbist, K. (2018) · Water Resources Research · doi:10.1029/2017wr022437 · Cited by 561
Bastos, L. M., Carciochi, W., Lollato, R. P., Jaenisch, B. R., Rezende, C. R., Schwalbert, R., Vara Prasad, P. V., Zhang, G., Fritz, A. K., Foster, C., et al. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00054 · Cited by 164
Agbehadji, I. E., Mabhaudhi, T., Botai, J., & Masinde, M. (2023) · Climate · doi:10.3390/cli11090188 · Cited by 48
Page, W. G., Freeborn, P. H., Butler, B. W., & Jolly, W. M. (2019) · International Journal of Wildland Fire · doi:10.1071/wf19022 · Cited by 34
Novoa, V., Rojas, C., Rojas, O., Ahumada-Rudolph, R., & Moreno-Santoyo, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-56407-5 · Cited by 20
Metadata
- Authors
- Huntington, J. L., Hegewisch, K. C., Daudert, B., Morton, C. G., Abatzoglou, J. T., McEvoy, D. J., & Erickson, T.
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2017-11
- Volume
- 98
- Issue
- 11
- Pages
- 2397-2410
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W2605456139
- Cited by
- 363
- References
- 32
- FWCI
- 25.13
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding. https://app.cochat.ai/papers/10.1175/bams-d-15-00324.1
- The paper
- Huntington, J. L., Hegewisch, K. C., Daudert, B., Morton, C. G., Abatzoglou, J. T., McEvoy, D. J., & Erickson, T. (2017). Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding. Bulletin of the American Meteorological Society. https://doi.org/10.1175/bams-d-15-00324.1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
