NG42A:
Applications of Machine Learning, Deep Learning, and Novel Statistical Approaches to the Study of Weather and Climate Using Large Data Sets I

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Session ID#: 31522

Session Description:
The increasing volume of climate data from observations, analysis products, and climate model output presents the climate science community with unprecedented data analysis challenges and opportunities. This challenge becomes greater when targeting extreme events as standard data reduction techniques like multi-model ensemble averaging reduce the magnitude of extremes. To address this, sophisticated statistical and machine learning algorithms have been developed. Applications areas include: extreme events, remote sensing, sensitivity analysis, trend prediction, weather prediction, process emulation, data assimilation, and uncertainty quantification, amongst others. This session invites speakers that develop and/or apply machine learning, deep learning, and related novel statistical techniques to mine climate data for the study of climate projection, climate/weather extreme events, and more.
Primary Convener:  Jiali Wang, Argonne National Laboratory, Argonne, United States
Conveners:  David Matthew Hall, University of Colorado at Boulder, Boulder, CO, United States, Matthew R Norman, Oak Ridge National Lab, Oak Ridge, TN, United States and Won Chang, University of Chicago, Chicago, IL, United States
Chairs:  David Matthew Hall, University of Colorado at Boulder, Boulder, CO, United States and Jiali Wang, Argonne National Laboratory, Argonne, United States
OSPA Liaison:  Won Chang, University of Chicago, Chicago, IL, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • GC - Global Environmental Change
  • IN - Earth and Space Science Informatics
  • OS - Ocean Sciences
Index Terms:

1630 Impacts of global change [GLOBAL CHANGE]
1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]
3270 Time series analysis [MATHEMATICAL GEOPHYSICS]

Abstracts Submitted to this Session:

Learning About Climate and Atmospheric Models Through Machine Learning (Invited) (206533)
Donald D Lucas, Lawrence Livermore National Laboratory, Livermore, CA, United States
Deep Learning @15 Petaflops/second: Semi-supervised pattern detection for 15 Terabytes of climate data (Invited) (235096)
William Drew Collins1, Michael F Wehner2, Mr Prabhat2, Thorsten Kurth3, Nadathur Satish4, Ioannis Mitliagkas5, Jian Zhang6, Evan Racah2, Mostofa Patwary4, Narayanan Sundaram4 and Pradeep Dubey4, (1)Lawrence Berkeley National Laboratory and UC Berkeley, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (3)NVIDIA Corporation, Zurich, Switzerland, (4)Intel, Santa Clara, CA, United States, (5)Stanford University, Stanford, CA, United States, (6)Zhejiang University, Department of Atmospheric Sciences, School of Earth Sciences, Hangzhou, China
Evaluating the fidelity of CMIP5 models in producing large-scale meteorological patterns over the Northwestern United States (263785)
Benjamin R Lintner, Rutgers University New Brunswick, New Brunswick, NJ, United States, Paul C Loikith, Portland State University, Geography, Portland, OR, United States, Max Pike, Rutgers University New Brunswick, New Brunswick, United States and Christina Aragon, Oregon State University, Water Resources Engineering, Corvallis, OR, United States
Applying machine-learning techniques to Twitter data for automatic hazard-event classification. (250689)
Rosa Filgueira1, Emma J Bee2, Diego Diaz-Doce2, Jacob Poole Sr3 and Anubha Singh3, (1)British Geological Survey Lyell Centre - BGS, Informatics, Edinburgh, United Kingdom, (2)British Geological Survey Keyworth, Nottinghamshire, United Kingdom, (3)British Geological Survey, Keyworth, United Kingdom
Precipitation event tracking reveals that precipitation characteristics respond differently under seasonal, interannual, and anthropogenic forcing (296487)
Chen Chen1, Won Chang2, Wenwen Kong3, Jiali Wang4, Veerabhadra Rao Kotamarthi4, Michael Stein5 and Elisabeth J Moyer6, (1)University of Chicago, Chicago, IL, United States, (2)University of Cincinnati, Department of Mathematical Sciences, Cincinnati, United States, (3)University of California Berkeley, Department of Geography, Berkeley, CA, United States, (4)Argonne National Laboratory, Argonne, United States, (5)University of Chicago, Department of Statistics, Chicago, IL, United States, (6)University of Chicago, Department of the Geophysical Sciences, Chicago, United States
Characterizing Temperature Variability and Associated Large Scale Meteorological Patterns Across South America (258962)
Judah Detzer, Portland State University, Portland, OR, United States, Paul C Loikith, Portland State University, Geography, Portland, OR, United States, Carlos R Mechoso, UCLA, Los Angeles, CA, United States, Armineh Barkhordarian, Jet Propulsion Laboratory, Pasadena, CA, United States and Huikyo Lee, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States

See more of: Nonlinear Geophysics