The Physical Oceanography Distributed Active Archive Center (PO.DAAC) has traditionally hosted NASA’s Earth Observing System oceanography datasets, but is expanding its archive to include hydrology datasets from satellites like the upcoming Surface Water and Ocean Topography (SWOT) mission. The SWOT mission, expected to launch later this year (2022), will deliver approximately 20 TB of data per day! Though hydrologic and water resources applications will be enabled at a greater scale than ever before, an increase in data volume requires more efficient and scalable data management technologies. Cloud computing tools and services can help pave the way toward efficiency. By June 2022, PO.DAAC will have enabled all its data to be accessed in the NASA Earthdata Cloud hosted in Amazon Web Services (AWS). Other NASA DAACs are also in the process of migrating their Earth observations to the Earthdata Cloud, which will support seamless access across DAACs and disciplines. PO.DAAC desires to make data access, pre-processing, and analysis as seamless as possible for data users, supporting science and applications users alike with relevant tools and resources. In this presentation, after introducing the PO.DAAC, we highlight a new SWOT-specific data search mechanism (searching via the SWOT River Database (SWORD) pre-defined river reaches) and showcase a cloud computing workflow in the context of hydrologic applications by accessing and analyzing a proxy SWOT dataset, Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) river heights. This cloud workflow can be easily adapted to other PO.DAAC datasets, or further developed with other DAAC data, offering effective guidance and support for a variety of science use cases and applications.

Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist’s personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.