Abstract
The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.
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Acknowledgements
The National Ecological Observatory Network is a project sponsored by the National Science Foundation and managed under cooperative agreement by Battelle. Some of this material is based upon work supported by the National Science Foundation (Grant DBI-0752017). We acknowledge contributions for some of this material from participants of NSF-sponsored joint NCAR and NEON workshop, Predicting life in the Earth system – linking the geosciences and ecology, and the continued efforts in post-workshop working groups. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Portions of this research were sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory and used resources of the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE–AC05–00OR22725. Additional support was provided by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) activity of the Earth & Environmental System Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science. Some contributions to this research were supported by NASA’s Earth Science Division (Grant NNX15AG76G). We acknowledge contributions for some of this material from all participants in the RUBISCO-AmeriFlux Working Group, which is supported by the RUBISCO SFA.
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Durden, D.J. et al. (2020). Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_14
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