To perform computational experiments at greater scale and in less time, enterprises are increasingly looking to dynamically expand their computing capabilities through the temporary addition of cloud resources (aka “cloudbursting”). Computational infrastructure can be dismantled in minutes with no long-term capital investments. However, research is needed to identify which properties of an application best determine the potential benefits of cloudbursting. For example, there are certainly situations where the cost to transfer the necessary input data from the enterprise to the cloud (to execute the application in the cloud) outweighs the value of simply waiting until resources become available in-house. To better understand and quantify these general issues, we perform a concrete analysis of the value of cloudbursting for a large-scale application we have previously created to process and derive environmental results from satellite imagery. More specifically, we compare three versions of the application (an all-cloud design; a version that runs in-house on our cluster; and a hybrid cloudbursting version) on dimensions of debuggability, fault tolerance, correctness, economics, usability, and run-time speed. We find that for our application, cloudbursting is effective primarily because we were able to design the application so that its I/O behavior does not preclude remote (cloud) execution, we were able to minimize developmental cost by constructing a cloud run-time environment that is very similar our in-house environment, and we achieve good run-time performance in our cloud-based executions (for example, we describe how a representative computation that takes 2 ½ hours in-house is completed in 35 minutes via cloudbursting). By generalizing this analysis, we believe that we contribute guidance to the broader community on the value of cloudbursting for escience applications.