NG33A:
Applications of Machine Learning, Deep Learning, and Novel Statistical Approaches to the Study of Weather and Climate Using Large Data Sets II Posters

Submit an Abstract to this Session



Session ID#: 31499

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:

Pattern Matching for Volcano Status Assessment: what monitoring data alone can say about Mt. Etna activity (220799)
Flavio Cannavo, National Institute of Geophysics and Volcanology, Rome, Italy, Andrea Cannata, INGV National Institute of Geophysics and Volcanology, Osservatorio Etneo, Catania, Italy and Carmelo Cassisi, National Institute of Geophysics and Volcanology, Catania, Italy
Application of Deep Learning and Supervised Learning Methods to Recognize Nonlinear Hidden Pattern in Water Stress Levels from Spatiotemporal Datasets across Rural and Urban US Counties (224532)
Tristan Eisenhart, Columbia University of New York, New York, NY, United States, Laureline Josset, Columbia University, New York, United States, James A Rising, University of Delaware, Newark, DE, United States, Naresh Devineni, Department of Civil Engineering, The City College of the City University of New York, and CUNY-CREST, Department of Civil Engineering, New York, United States and Upmanu Lall, Columbia University, Earth and Environmental Engineering, New York, United States
Semi-supervised tracking of extreme weather events in global spatio-temporal climate datasets (225274)
Soo Kyung Kim1, Mr Prabhat2 and Dean Norman Williams1, (1)Lawrence Livermore National Laboratory, Livermore, CA, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Improving Satellite Observation Utilization for Model Initialization with Machine Learning: An Introduction and Tackling the “Labeled Dataset” Challenge for Cyclones Around the World (225894)
Jebb Stewart, NOAA Global Systems Laboratory, Boulder, United States, Christina Kumler, NOAA Global Systems Laboratory and Cooperative Institute for Research in Environmental Sciences, Boulder, United States, Yu-Ju Lee, NOAA Earth System Research Laboratory and Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Mark Govett, NOAA, Global Systems Laboratory, Boulder, CO, United States, Lidia Trailovic, Mexico Df, Mexico and Brian Etherton, NOAA Earth System Research Laboratory, Boulder, CO, United States
Development of anomaly detection models for deep subsurface monitoring (226445)
Alexander Y Sun, University of Texas at Austin, Bureau of Economic Geology, Austin, United States
Using Deep Learning for Targeted Data Selection, Improving Satellite Observation Utilization for Model Initialization (226696)
Yu-Ju Lee1,2, Christina Kumler3, Lidia Trailovic2, Brian Etherton4, Mark Govett5 and Jebb Stewart6, (1)University of Colorado Boulder, Department of Computer Science, Boulder, CO, United States, (2)NOAA Earth System Research Laboratory and Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (3)NOAA Global Systems Laboratory and Cooperative Institute for Research in Environmental Sciences, Boulder, United States, (4)NOAA Earth System Research Laboratory, Boulder, CO, United States, (5)NOAA, Global Systems Laboratory, Boulder, CO, United States, (6)NOAA Global Systems Laboratory, Boulder, United States
Improving the Accuracy of Cloud Detection Using Machine Learning (230117)
Mary Ellen Craddock, Randall J Alliss and Michael Mason, Northrop Grumman Corporation, Mission Systems, McLean, VA, United States
Evaluation of Deep Learning Representations of Spatial Storm Data (245771)
David John Gagne II, NCAR/MMM, Boulder, United States, Sue Ellen Haupt, National Center for Atmospheric Research, Boulder, CO, United States and Douglas W Nychka, Colorado School of Mines, Applied Mathematics and Statistics, Golden, United States
Applications of Machine Learning to Downscaling and Verification (254170)
Rachel Prudden, Met Office, Informatics lab, Exeter, United Kingdom; University of Exeter, Exeter, United Kingdom
Emulating RRTMG Radiation with Deep Neural Networks for the Accelerated Model for Climate and Energy (274881)
Anikesh Pal, Oak Ridge National Laboratory, Oak Ridge, TN, United States and Matthew R Norman, Oak Ridge National Lab, Oak Ridge, TN, United States
Application of Clustering Algorithms to Observed and Simulated Daily Precipitation over the Tropical and Southern Pacific Ocean (296889)
Max Pike, Rutgers University New Brunswick, New Brunswick, United States and Benjamin R Lintner, Rutgers, New Brunswick, United States
Inferring Ice Thickness from a Glacier Dynamics Model and Multiple Surface Datasets. (298951)
Yawen Guan, The Statistical and Applied Mathematical Sciences Institute, Cary, NC, United States, Murali Haran, Pennsylvania State University, University Park, PA, United States and David Pollard, Pennsylvania State University, Earth and Environmental Systems Institute, University Park, PA, United States
An SVM model with hybrid kernels for hydrological time series (299086)
Cheng Wang1, Hongrui Wang2, Xinyu Zhao3 and Qing Xie3, (1)Argonne National Laboratory, Argonne, IL, United States, (2)Beijing Normal University, College of Water Science, Beijing, China, (3)Beijing Normal University, Beijing, China
Deep Neural Network Emulation of a High-Order, WENO-Limited, Space-Time Reconstruction (229079)
Matthew R Norman, Oak Ridge National Laboratory, Oak Ridge, TN, United States and David Matthew Hall, University of Colorado at Boulder, Boulder, CO, United States
Multi-objective optimization for generating a weighted multi-model ensemble (245995)
Hugo Lee, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States

See more of: Nonlinear Geophysics