IN33A:
Big Data in the Geosciences: New Approaches to Storage, Sharing, and Analysis I


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Session ID#: 60507

Session Description:
Existing and future Earth system data are 1) increasingly large and complex – often representing high spatial/temporal/spectral resolution and dimensionality from remote sensing or model results — and are 2) growing at an unprecedented rate (e.g., NASA SWOT and NISAR missions), making such data difficult to store, download, analyze, visualize, interpret, and understand by traditional methods. Therefore, this session explores new approaches to processing, storage, exchange, and analysis of large geoscience data. Perspectives on cloud-based approaches (e.g., ready science data systems, cloud-optimized storage, cloud analytics engine, and use of cloud resources for scientific discovery), geoscientific data analytics approaches (statistical, data mining, assimilation, deep learning, etc.), parallel algorithms and software employing high performance computing resources for scalable analysis and novel applications of traditional methods on large geoscience data are strongly desired.
Primary Convener:  Cedric H David, Jet Propulsion Laboratory, Pasadena, CA, United States
Conveners:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States and Hamed Alemohammad, Radiant Earth Foundation, Washington, United States
Primary Liaison:  Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States
Chairs:  Cedric H David, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States and Soo Kyung Kim, LLNL, Livermore, United States
OSPA Liaison:  Hamed Alemohammad, Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • B - Biogeosciences
  • H - Hydrology
  • OS - Ocean Sciences
Index Terms:

1855 Remote sensing [HYDROLOGY]
1908 Cyberinfrastructure [INFORMATICS]
1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

Deep learning on the Sphere: Convolutional Neural Network on Unstructured Mesh (384153)
Chiyu Max Jiang1,2, Karthik Kashinath3, Mayur Mudigonda1,2, Ankur Mahesh4,5, Travis Allen O'Brien2, Philip S Marcus1 and Mr Prabhat2, (1)University of California Berkeley, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (3)NVIDIA Corporation, Santa Clara, CA, United States, (4)Lawrence Berkeley National Laboratory, Earth and Environmental Science, Berkeley, United States, (5)University of California Berkeley, Berkeley, United States
Lessons Learned in Creating Big Science Data Analysis Solutions for the Cloud (Invited) (354549)
Thomas Huang, NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
NASA Archives in the Cloud with Cumulus (447055)
Lauren Frederick, NASA EED-2 / Element 84, Alexandria, VA, United States
EarthInsights: Parallel Clustering of Large Earth Science Datasets on the Summit Supercomputer (Invited) (427189)
Sarat Sreepathi, Oak Ridge National Laboratory, Oak Ridge, United States, Jitendra Kumar, Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States, Dr. Forrest M Hoffman, PhD, Computational Earth Sciences Group and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, United States, Richard T Mills, Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States, Vamsi Sripathi, Intel Corporation, Hillsboro, OR, United States and William Walter Hargrove, USDA Forest Service Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Hot Springs National, AR, United States
Synthesizing Earth System Model Behavior: From Petabytes to Kilobytes (453163)
Peter J Gleckler1, Charles Doutriaux1, Jiwoo Lee1 and Paul James Durack2, (1)Lawrence Livermore National Laboratory, Livermore, CA, United States, (2)Lawrence Livermore Nat. Lab., Program for Climate Model Diagnosis and Intercomparison, Livermore, United States
Faults in the cloud: Distributed topographic template matching of fault-related landforms in Shuttle Radar Topography Mission data using a cloud-based processing framework (354793)
Robert Sare, Stanford University, Geological Sciences, Stanford, CA, United States and George E Hilley, Stanford University, Earth and Planetary Sciences, Stanford, United States
Image Super-Resolution and Uncertainty Quantification for Earth Science Data on the NASA Earth Exchange AI Platform (Invited) (436179)
Thomas Vandal1, Sangram Ganguly2, Evan Kodra3, Jennifer Dy4, Andrew Michaelis5, Ramakrishna R Nemani5 and Auroop R Ganguly6, (1)NASA Ames Research Center / BAERI, Moffett Field, CA, United States, (2)NASA Ames Research Center / BAERI, Moffet Field, CA, United States, (3)risQ, Cambridge, MA, United States, (4)Northeastern University, Boston, United States, (5)NASA Ames Research Center, Moffett Field, CA, United States, (6)Northeastern University, Civil and Environmental Engineering Department, Boston, United States

See more of: Earth and Space Science Informatics