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The knowledge and technologies that move our society forward and preserve our international competitive advantage rely upon a highly skilled workforce that is adept at conducting complex scientific and technical research—and in translating its outcome into useful products and services. “Use-inspired” research is driven by specific needs and interests and naturally focuses on socioeconomically advantageous application, whereas academic research tends to be driven by an intrinsic quest for new knowledge. Each has its role in overall technological development, however, the skills and knowledge crucial for success in these domains can differ significantly. To integrate these two approaches in doctoral training in STEM fields, a national workshop of ~100 leaders of industry, academia, funding agencies and non-profits was held with the goal of developing a robust understanding of the current status of the pipeline from graduate degree programs in STEM into professional research environments. At the conclusion, the Workshop participants identified gaps in the present training of STEM doctorates. Then they endorsed the Pasteur Partners PhD (P3) track recently established at Lehigh University as a new model for student-centered workforce training based on use-inspired research in partnership with industry. Here, we present the key outcomes of the workshop and describe the four distinctive features of the P3 program: 1. Pre-program summer internship; 2. Co-advising of students by a university faculty member and an industry researcher; 3. Instructions for developing essential professional skills; 4. Industry Residency (as in medical school). In this context, ‘Industry’ is defined broadly to include private corporations, national labs, defense organizations, healthcare institutes, etc., which hire PhDs. Collectively, we consider this as a model for the much needed redesigning of the US STEM doctoral education to create a national workforce of technical leaders. Finally, challenges to the implementation of the P3 track are identified. Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. https://peer.asee.org/44062 

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society. 

ABSTRACT Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.