We live in a golden age of satellite remote sensing. The European Space Agency's Sentinel program in particular, which focuses on spaceborne Earth observation (EO), has set the standard for continuous, free and readily available satellite observations and products. The archive of EO data is vast, and continues to grow by many petabytes a year. From a scientific perspective, these vast quantities enable ever more research to be conducted, especially in the age of data-hungry artifical intelligence. Yet, without special infrastructure, the sheer magnitude of satellite data causes it to become unwieldly. Cloud-hosted services tailored to satellite data exist (noteably Google Earth Engine), but these tend to have their own shortcomings, such as limited availability of low-level (raw) satellite observations.

In our research group we study the ocean surface with spaceborne radars. Radars are uniquely capable for ocean monitoring: when mounted on a satellite they provide large coverage while being mostely unaffected by atmospheric interference (e.g. they can look through clouds), and the signals reflecting from the ocean surface provide all sorts of geophysical insights that are used in meteorology, storm tracking, swell predictions, and much more. But when looking at high resolution radar imagery of the ocean, one can identify a wealth of atmospheric information that is commonly ignored. Thus, we set about developing a methodology for extracting this information, focusing on the heat-flux exchange between the ocean and atmosphere---a critical climate variable for which no satellite products are available---from Sentinel-1's entire 10+ year data catalogue.

In this presentation we will outline the development of our methodology, which we call FluxSAR, and share preliminary scientific results. The presentation focuses on the challenges involved in working with the petabytes of high-resolution radar data, and how SURF's Spider HPC has enabled us to tackle these challenges head on.