paper_2020.bib

@article{Hawkins_EOS_2020,
  author = {Linnia Hawkins and Jitendra Kumar and Xiangzhong Luo and Debjani Sihi and Sha Zhou},
  title = {Measuring, Monitoring, and Modeling Ecosystem Cycling},
  journal = {Eos},
  year = {2020},
  volume = {101},
  month = aug,
  doi = {10.1029/2020eo147717},
  file = {Hawkins_EOS_2020.pdf:pubs/Hawkins_EOS_2020.pdf:PDF},
  publisher = {American Geophysical Union ({AGU})},
  url = {https://doi.org/10.1029/2020eo147717},
  note = {\url{https://doi.org/10.1029/2020eo147717}}
}

@article{Konduri_RSE_2020,
  author = {Venkata Shashank Konduri and Jitendra Kumar and William W. Hargrove and Forrest M. Hoffman and Auroop R. Ganguly},
  title = {Mapping crops within the growing season across the United States},
  journal = {Remote Sensing of Environment},
  year = {2020},
  volume = {251},
  pages = {112048},
  month = dec,
  doi = {10.1016/j.rse.2020.112048},
  file = {Konduri_RSE_2020.pdf:pubs/Konduri_RSE_2020.pdf:PDF},
  publisher = {Elsevier {BV}},
  url = {https://doi.org/10.1016/j.rse.2020.112048},
  note = {\url{https://doi.org/10.1016/j.rse.2020.112048}}
}

@article{Jayasinghe_Atmosphere_2020,
  author = {Amadini Jayasinghe and Scott Elliott and Anastasia Piliouras and Jaclyn Clement Kinney and Georgina Gibson and Nicole Jeffery and Forrest Hoffman and Jitendra Kumar and Oliver Wingenter},
  title = {Modeling Functional Organic Chemistry in Arctic Rivers: An Idealized Siberian System},
  journal = {Atmosphere},
  year = {2020},
  volume = {11},
  number = {10},
  pages = {1090},
  month = oct,
  doi = {10.3390/atmos11101090},
  file = {Jayasinghe_Atmosphere_2020.pdf:pubs/Jayasinghe_Atmosphere_2020.pdf:PDF},
  publisher = {{MDPI} {AG}},
  url = {https://doi.org/10.3390/atmos11101090},
  note = {\url{https://doi.org/10.3390/atmos11101090}}
}

@inproceedings{Durden_SMC2020_20201218,
  author = {David J. Durden and Stefan Metzger and Housen Chu and Nathan Collier and Kenneth J. Davis and Ankur R. Desai and Jitendra Kumar and William R. Wieder and Min Xu and Forrest M. Hoffman},
  title = {Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere--Atmosphere Interaction},
  editor = {Jeffrey Nichols and Becky Verastegui and Arthur `Barney' Maccabe and Oscar Hernandez and Suzanne Parete-Koon and Theresa Ahearn},
  booktitle = {Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI},
  organization = {17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 (August 26--28, 2020)},
  publisher = {Springer International Publishing, Cham},
  isbn = {978-3-030-63393-6},
  pages = {204--225},
  doi = {10.1007/978-3-030-63393-6\_14},
  note = {\url{https://doi.org/10.1007/978-3-030-63393-6\_14}},
  day = 18,
  month = dec,
  year = 2020,
  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.}
}

@inproceedings{Sreepathi_Gateways2020_20201019,
  author = {Sarat Sreepathi and Min Xu and Nathan Collier and Jitendra Kumar and Jiafu Mao and Forrest M. Hoffman},
  title = {Land Model Testbed: Accelerating Development, Benchmarking and Analysis of Land Surface Models},
  booktitle = {Proceedings of the Gateways 2020 Conference},
  publisher = {Open Science Framework},
  doi = {10.17605/OSF.IO/X32A8},
  day = 19,
  month = oct,
  year = 2020,
  note = {\url{https://doi.org/10.17605/OSF.IO/X32A8}},
  abstract = {A Land Model Testbed (LMT), designed to provide a computational framework for systematically assessing model fidelity and supporting rapid development of complex multiscale models, offers a general-purpose workflow for conducting large ensemble simulations of multiple land surface models, post-processing large volumes of model output, and evaluating model results. It leverages existing tools for launching model simulations and the International Land Model Benchmarking (ILAMB) package for assessing model fidelity through comparison with best-available observational datasets. Increased complexity and proliferation of uncertain parameters in process representations in land surface models has driven the need for frequent and intensive testing and evaluating of models to quantify uncertainties and optimize parameters such that results are consistent with observations. The LMT described here meets these needs by providing tools to run thousands of ensemble simulations simultaneously and post-process their output files, by automating execution of an enhanced version of ILAMB with site-specific benchmarks and multivariate functional relationships, and by offering ensemble diagnostics and a customizable dashboard for displaying model performance metrics and associated graphics. We envision the LMT capabilities will serve as a foundational computational resource for a proposed user facility focused on terrestrial multiscale model--data integration.}
}

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