ABSTRACT The use of artificial intelligence (AI) and machine learning (ML) methods in the geosciences can be categorized into three types, those that: (1) accelerate computationally expensive Earth system models; (2) fill the vacuum where numerical and physics-based models struggle; and (3) enable and enlighten data-driven discoveries. To achieve these tasks, many cyberinfrastructure (CI) systems are required. This chapter reviews the cutting-edge CI aiding the implementation of AI in the geosciences. Each technique presented is evaluated to assist geoscientists in determining how appropriate it is. Use cases in the subdomains of seismology, hydrology, and climatology are introduced to help readers understand the workflows. Challenges and future opportunities for CI development center on big data, provenance, interoperability, and heterogeneity due to the scale and complexity that future AI models in the geosciences will require.

ABSTRACT For science to reliably support new discoveries, its results must be reproducible. Assessing reproducibility is a challenge in many fields—including the geosciences—that rely on computational methods to support these discoveries. Reproducibility in these studies is particularly difficult; the researchers conducting studies must agree to openly share research artifacts, provide documentation of underlying hardware and software dependencies, ensure that computational procedures executed by the original researcher are portable and execute in different environments, and, finally, verify if the results produced are consistent. Often these tasks prove to be tedious and challenging for researchers. Sciunit ( https://sciunit.run ) is a system for easily containerizing, sharing, and tracking deterministic computational applications across environments. Geoscience applications in the fields of hydrology, solid Earth, and space science have actively used Sciunit to encapsulate, port, and repeat workflows across computational environments. In this chapter, we provide a comprehensive survey of geoscience applications that have used Sciunit to improve sharing and reproducibility. We classify the applications based on their reproducibility requirements and show how Sciunit accommodates relevant interfaces and architectural components to support reproducibility requirements within each application. We aim to provide these applications as a Sciunit compendium of use cases for replicability, benchmarking, and improving the conduct of reproducible science in other fields.

ABSTRACT This chapter provides an in-depth discussion of a scalable solution for running solar energy production ensemble simulations. Generating a forecast ensemble is computationally expensive. But with the help of Analog Ensemble, forecast ensembles can be generated with a single deterministic run of a weather forecast model. Weather ensembles are then used to simulate 11 10 KW photovoltaic solar power systems to study the simulation uncertainty under a wide range of panel configurations and weather conditions. This workflow has been developed and tested at scale on the National Center for Atmospheric Research supercomputer, Cheyenne, with more than 7000 concurrent cores. Results show that spring and summer are typically associated with greater simulation uncertainty. Optimizing the panel configuration based on the individual performance of simulations under changing weather conditions can improve the accuracy of simulations by more than 12%. This work also shows how panel configuration can be optimized based on geographic locations.