Open Climate Workbench to support efficient and innovative analysis of NASA's high-resolution observations and modeling datasets

We propose to develop an Analytic Collaborative Framework (ACF) that can power the processing flow of large and complex Earth science datasets and advance the scientific analysis of those datasets. In the proposed ACF development, we aim to address one of the current, fundamental challenges faced by the climate science community: bringing together vast amounts of both model and satellite observation data at different spatial and temporal resolutions in a high-performance, service-based cyberinfrastructure that can support scalable Earth science analytics. The Regional Climate Model Evaluation System (RCMES) developed by the Jet Propulsion Laboratory in association with the University of California, Los Angeles has undertaken systematic evaluation of climate models for many years with NASA's ongoing investments to advance infrastructure for the U.S. National Climate Assessment (NCA). RCMES is powered by the Open Climate Workbench (OCW; with a current public version of v1.3), an open-source Python library, that handles many of the common evaluation tasks for Earth science data such as rebinning, metrics computation, and visualization. Over the next three years, we propose to significantly advance the use and analysis of large and complex Earth science datasets by improving and extending the capabilities of OCW to version 2.0, and OCW v2.0 will be the ACF. The primary goal of developing OCW v2.0 is to improve and extend the capabilities of OCW for characterizing, compressing, analyzing, and visualizing observational and model datasets with high spatial and temporal resolutions. As an open-source ACF for climate scientists, OCW v2.0 will run on AWS Cloud with special emphasis on developing two use cases: air quality impacts due to wildfires and elevation-dependent warming. Our four specific objectives are to: O1. Migrate the RCMES database (RCMED) to AWS and provide observational datasets for the upcoming fifth NCA O2. Optimize the scientific workflows for common operations, applying data compression techniques and autonomic runtime system O3. Integrate cross-disciplinary algorithms for analyzing spatial patterns O4. Provide a comprehensive web service and supporting documentation for end-users The primary outcome of our proposed work will be a powerful ACF with enhanced capabilities for utilizing state-of-art observations and novel methodologies to perform comprehensive evaluation of climate models. By infusing recent advances in data management and processing technology, OCW v2.0 will be able to optimize scientific workflows when users analyze high-resolution datasets from RCMED, CMIP6 S3, and NASA's Distributed Active Archive Centers (DAACs). All of the OCW v2.0's capabilities will be made available from both command-line scripts and Jupyter Lab notebooks, which capture the end-to-end analysis workflow from collaborating climate scientists for reuse and modification. The proposed ACF development will meet one of the three AIST program's main objectives by "fully utilizing the large amount of diverse observations using advanced analytic tools, visualizations, and computing environments." Our data compression and runtime system will "address the Big Data challenge associated with observing systems and facilitate access to large amounts of disparate datasets" by compressing the datasets while assessing the trade-offs between lowering spatial resolution and accuracy. In addition, the ACF will "make unique topological data analysis (TDA) tools accessible and useful to the climate science community." By testing two use cases with our ACF, we will enable our long-term vision of "moving from custom-built ACF systems to reusable frameworks" for supporting various research activities in climate science, and broadening NASA's footprint in earth-science analytics in a way that increases the utility of current data products and cultivates demand for future ones.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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