PODPAC: open-source Python software for enabling harmonized, plug-and-play processing of disparate earth observation data sets and seamless transition onto the serverless cloud by earth scientists

• Published in: Earth science informatics Vol. 13; no. 4; pp. 1507 - 1521
• Main Authors: Ueckermann, Mattheus P., Bieszczad, Jerry, Entekhabi, Dara, Shapiro, Marc L., Callendar, David R., Sullivan, David, Milloy, Jeffrey
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Cloud computing; Data processing; Data sources; Datasets; Drought; Drought monitoring; Earth; Earth and Environmental Science; Earth science; Earth Sciences; Earth System Sciences; Electronic devices; Encapsulation; Environmental monitoring; Information Systems Applications (incl.Internet); Machine learning; Nodes; Ontology; Open data; Open source software; Parallel processing; Plug & play; Python; Reproducibility; Simulation and Modeling; Software; Software Article; Soil moisture; Source code; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Spatial data; Web services; Plug-and-play algorithms; Reproducibility; Data harmonization; JupyterLab; Serverless cloud computing; Python; SMAP
• ISSN: 1865-0473; 1865-0481; 1865-0481
• DOI: 10.1007/s12145-020-00506-0

In this paper, we present the Pipeline for Observational Data Processing, Analysis, and Collaboration (PODPAC) software. PODPAC is an open-source Python library designed to enable widespread exploitation of NASA earth science data by enabling multi-scale and multi-windowed access, exploration, and integration of available earth science datasets to support analysis and analytics; automatic accounting for geospatial data formats, projections, and resolutions; simplified implementation and parallelization of geospatial data processing routines; standardized sharing of data and algorithms; and seamless transition of algorithms and data products from local development to distributed, serverless processing on commercial cloud computing environments. We describe the key elements of PODPAC’s architecture, including Nodes for unified encapsulation of disparate scientific data sources; Algorithms for plug-and-play processing and harmonization of multiple data source Nodes ; and Lambda functions for serverless execution and sharing of new data products via the cloud. We provide an overview of our open-source code implementation and testing process for development and deployment of PODPAC. We describe our interactive, JupyterLab-based end-user documentation including quick-start examples and detailed use case studies. We conclude with examples of PODPAC’s application to: encapsulate data sources available on Amazon Web Services (AWS) Open Data repository; harmonize processing of multiple earth science data sets for downscaling of NASA Soil Moisture Active Passive (SMAP) soil moisture data; and deploy a serverless SMAP-based drought monitoring application for use access from mobile devices. We postulate that PODPAC will also be an effective tool for wrangling and standardizing massive earth science data sets for use in model training for machine learning applications.