Breath of Clarity

Annotated Reference List


Ayma, V., Beltrán, C., Happ, P., Costa, G., and Feitosa, R. “Mapping Glacier Changes Using Clustering Techniques on Cloud Computing Infrastructure.” The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 42, no. 2/W16 (September 2019): 29–34. W16-29-2019.

Debbage, Neil and J. Marshall Shepherd. “The Urban Heat Island Effect and City Contiguity.” Computers, Environment and Urban Systems 54 (November 2015): 181-194. https://

Huang, Qunying, Chaowei Yang, Karl Benedict, Songqing Chen, Abdelmounaam Rezgui, and Jibo Xie. “Utilize Cloud Computing to Support Dust Storm Forecasting.” International Journal of Digital Earth 6, no. 4 (January 2013): 338–355. 10.1080/17538947.2012.749949.

Krämer, Michael and Ivo Senner. “A Modular Software Architecture for Processing of Big Geospatial Data in the Cloud.” Computers and Graphics 49 (June 2015): 69–81.

Li, Zhenlong, Chaowei Yang, Kai Liu, Fei Hu, and Baoxuan Jin.“Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data.” International Journal of Geo-Information 5, no. 173 (September 2016): 1-14. ijgi5100173.

Li, Zhenlong, Chaowei Yang, Qunying Huang, Kai Liu, Min Sun, and Jizhe Xia. “Building Model as a Service to Support Geosciences.” Computers, Environment and Urban Systems 61 (January 2017): 141-152.

Li, Zhenlong, Fei Hu, John L. Schnase, Daniel Q. Duffy, Tsengdar Lee, Michael K. Bowen, and Chaowei Yang. “A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce.” International Journal of Geographical Information Science 31, no. 1 (January 2016): 17-35. 10.1080/13658816.2015.1131830.

Lushbough, Carol M., Etinne Z. Gnimpieba, and Rion Dooley. “Life Science Data Analysis Workflow Development Using the Bioextract Server Leveraging the iPlant Collaborative Cyberinfrastructure.” Concurrency and Computation: Practice and Experience 27, no. 2 (February 2014): 408–419.

National Institute of Standards and Technology. “Software as a Service (SaaS).” Accessed January 18 2022.

Schnase, John, Daniel Duffy, Glenn Tamkin, Denis Nadeau, John Thompson, Cristina Grieg, Mark McInerney, William Webster. “MERRA Analytical Services: Meeting the Big Data Challenges of Climate Science Through Cloud-Enabled Climate Analytics-as-a- Service.” Computers, Environment and Urban Systems 61 (January 2014): 198-211.

Sebestyén Victor, Timea Czvetkó and Janos Abonyi.“The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking.” Frontiers in Environmental Science 9 (March 2021).

Yang, Chaowei, Manzhu Yu, Fei Hu, Yongyao Jiang, Yun Li. “Utilizing Cloud Computing to Address Big Geospatial Data Challenges.” Journal of Computers, Environment and Urban Systems 61 (January 2017): 120-28. 2016.10.010

Yang, Chaowei, Min Sun, Kai Lu., Qunying Huang, Zhenlong Li, Zhipeng Gui, Yunfeng Jiang, Jizhe Xia, Manzhu Yu, Chen Xu, Peter Lostritto, Nanying Zhou.“Contemporary Computing Technologies for Processing Big Spatiotemporal Data.” In Space-time integration in geography and GIScience, 327-351. Netherlands: Springer, 2015.

Yang, Chaowei, Qunying Huang, Zhenlong Li, Kai Liu, and Fei Hu. “Big Data and Cloud Computing: Innovation Opportunities and Challenges. ”International Journal of Digital Earth 10, no. 1 (January 2017): 13-53.

Yang, Chaowei, Yan Xu, and Douglass Nebert. “Redefining the Possibility of Digital Earth and Geosciences with Spatial Cloud Computing.” International Journal of Digital Earth 6, no. 4 (May 2013): 297–312.

Software as a Service (Saas) is a valuable tool for conducting necessary, broad integrative analyses given the large quantity of diverse global climate change data.

First of all, the National Institute of Standards and Technology (NIST) is a government website that provides a definition of Software as a Service (Saas). It highlights SaaS encompasses a capability provided to consumers to use the provider’s applications running on cloud infrastructure. It then goes onto paint a picture of cloud infrastructure as it explains applications are accessible from various client devices. The NIST also mentioned there can be user-specific application configuration settings. The SaaS definition is crucial to my capstone because the Center for Climate and Energy Solutions (C2ES) audience is not necessarily familiar with technology terminology and needs to be provided context to fully understand the foundational aspect of my report’s solution. The details regarding its accessibility from various client devices and user-specific application configuration settings aid in advancing my argument because the described capabilities are in demand to efficiently analyze the array of climate data.

Additionally, the article “The Applicability of Big Data in Climate Change” conveyed the interrelationship between data science and climate studies. Further, the authors emphasized there is a lack of focus on broad integrative analyses in climate science and how sustainability climate issues can be managed using specific software tools. The authors go on to mention that data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. Therefore, the source is helpful as it provides the logical connection between the problem and solution of my report as the analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

From there, the article “MERRA Analytical Services: Meeting the Big Data Challenges of Climate Science Through Cloud-Enabled Climate Analytics-as-a-Service” addressed the data challenges of climate science and provided additional support to my solution by mentioning cloud computing plays an important role in a breadth of capabilities that are essential to delivering climate analytics as a service. Crucially, the course discussed MERRA Analytic Services (MERRA/AS) as an example of cloud-enabled Climate Analytics as a Service that combines cloud computing with other capabilities. Specifically, the authors illustrated how cloud computing would conduct a broad integrative analysis of data by explaining how MERRA integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. They proceeded to list the benefits of cloud computing in handling large amounts and diverse global climate change data and concluded that cloud computing’s capacity to engage communities in the construction of new capabilities is perhaps the most important link between Cloud Computing and climate data which aligns with my thesis that cloud computing answers the demand for integrative analysis of climate data.

Then, the article “Life Science Data Analysis Workflow Development Using the Bioextract Server Leveraging the iPlant Collaborative Cyber Infrastructure” further conveyed the strengths of a web-based workflow-enabling system in fostering collaboration amongst scientists. Although the source discussed the issue in the context of the bioinformatics field, its focus on the vast quantities of data draws the crucial connection to my topic. Specifically, the authors focused on how iPlant Collaborative empowers researchers can share their data extracts, analytical tools, and workflows with collaborators. They provide iPlant AGAVE Advanced Programming Interface as a perfect example of a SaaS providing access to a collection of high performance computing and cloud resources. The article encapsulates a key talking point indirectly embedded into my thesis that enhancing analysis of a broad scientific problem requires a mechanism that makes collaboration seamless.

Finally, the article “Big Data and Cloud Computing: Innovation Opportunities and Challenges” brought a balanced perspective regarding the value of cloud computing in global climate change data analysis that focused on both the benefits and the consequences of my report’s solution. The authors highlighted advancements in climate data analysis that have come as a result of incorporating cloud computing into the field that are not mentioned in the other articles. Additionally, they recognized some of the consequences of using cloud computing include open availability of broad integrative analysis of the large, diverse data pose social challenges of geospatial significance and a weave of innovation is transforming the data into geospatial research, engineering and business values. At the end, the article provided direction for my report’s conclusion as it introduced a future research agenda for cloud computing involving both local and global digital earth science and applications.

Comment by Amanda Hughes:

Hi Mary,

Regarding page numbers, you might find this helpful. (Links to an external site.) DU writing requirements are upper right corner in header LastName – Page #

Excerpt from the link:

Page Numbering

All page numbers should be inserted in the document’s header, right justified, using your word processing program’s “Insert and then Page Numbers” function. Be sure to uncheck “Show number on first page” to eliminate the page number on your title page. Use the “Format Page Numbers” controls to insert a header containing your last name and a hyphen, to the left of the page numbers (see this document’s header and page numbers for an example), and

Choose the appropriate number format (lower case roman numerals for front matter and arabic numerals for the body of your paper).

My Comment:

Hey Amanda,

Thanks for looking into the page numbers. It is good to see the information coming from the University-College-Format-and-Style-Requirements source. I will edit accordingly.

Comment by Amanda Hughes:

Peer Review:

Hi Mary,

Are the writer’s sources likely to persuade an audience that the writer’s purpose is valid? Why or why not?

Yes, the sources are very relevant to the topic and are from reliable sources such as peer-reviewed journals.

Do the sources acknowledge and respond to the audience’s point of view? Why or why not?

Yes, in the annotations you discuss how your audience may not be familiar with the technology so you are utilizing sources to help explain what it is and its capabilities.

Are the writer’s sources relevant, reliable, credible, and valid? Why or why not?

Yes, similar to the first question, they are credible and valid because they are peer-reviewed research and articles. It is also important that the sources are from the last ten years because of how quickly we see advances in technology.

Are the references in correct author-date format? Why or why not?

Almost. There are a couple of elements missing in a few sources, and I have highlighted them in the attachment. Also of note, DU font requirement is Calibri 12 pt. I recommend updating your references list prior to pulling into your final report.

Not sure if anyone else does this, but I find it very helpful when working on drafts to have each section of my paper as its own working document, including the references list. I have a long list of potential references, and then pull a source into my “References in Final Paper” document once I actually use them in my final paper.