Automated integration of continental-scale observations in near-real time for simulation and analysis of biosphere-atmosphere interactions

David J. Durden, Stefan Metzger, Housen Chu, Nathan Collier, Kenneth J. Davis, Ankur R. Desai, Jitendra Kumar, William R. Wieder, Min Xu, Forrest M. Hoffman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationDriving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers
EditorsJeffrey Nichols, Arthur ‘Barney’ Maccabe, Suzanne Parete-Koon, Becky Verastegui, Oscar Hernandez, Theresa Ahearn
PublisherSpringer Science and Business Media Deutschland GmbH
Pages204-225
Number of pages22
ISBN (Print)9783030633929
DOIs
StatePublished - 2021
Event17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 - Virtual, Online
Duration: Aug 26 2020Aug 28 2020

Publication series

NameCommunications in Computer and Information Science
Volume1315 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020
CityVirtual, Online
Period8/26/208/28/20

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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