Acquisition and Analysis of Times Series of Satellite Data in the Cloud – Lessons from the Field

Series: HPC Best Practices Webinars

Satellite data has grown and matured to levels that allow powerful and relevant analysis in climate science, which requires time series spanning decades. Acquiring such data has been a technical and coding challenge given the historical formats in which data is stored, and analyzing the data has required high levels of coding expertise. With technological advances, like the coding language Python and new storage and process capabilities available in the cloud, there is great potential to increase the use of satellite data in new and diverse research areas. This requires, however, expanding the user base by building capacity in groups with limited coding or technological expertise. A challenge is the steep learning curve for these new technological advances, which can be intimidating and discouraging. To provide a taste of the new technologies and opportunities they provide, the presenter has developed a tutorial to teach potential new users how to acquire, synthesize and analyze satellite and satellite-based time series of data, while learning and using Python and cloud advances in the process. In this webinar, the speaker will share the lessons learned in making and teaching the tutorial, which can be found at https://github.com/marisolgr/python_sat_tutorials.


Presenter Bio

Marisol Garcia-Reyes has a background in physics, atmospheric science and computer sciences, but she’s an oceanographer at heart. Her research focuses on how climate change and variability impact marine environments and ecosystems, and on climate extreme events. For this, she analyzes large amounts of diverse data, including satellite, in situ and model data, using it in her own research and in collaborative research where Earth data and her data expertise is needed. This has motivated her to learn and share her experiences and expertise on new data and coding advances. She is also a mentor, committed to increasing access, equity, and diversity in education and in sciences.