Session 11A 19th Conference on Artificial Intelligence for Environmental Science

Program Chairs: David John Gagne II , Univ. of Oklahoma ; Carlos Gaitan , University of Oklahoma ; Amy McGovern , Univ. of Oklahoma ; Philippe Tissot , Texas A&M Univ. - Corpus Christi

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Monday, 13 January 2020

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8:30 AM-10:00 AM: Monday, 13 January 2020

Recording files available
1
The Enterprise: Worth More than You Think
Location: 210AB (Boston Convention and Exhibition Center)
Hosts: (Joint between the Presidential Forum Sessions; the 19th Conference on Artificial Intelligence for Environmental Science; the Eighth Symposium on Building a Weather-Ready Nation: Enhancing Our Nation's Readiness, Responsiveness, and Resilience to High Impact Weather Events; the 15th Symposium on Societal Applications: Policy, Research and Practice; and the Eighth Symposium on the Weather, Water, and Climate Enterprise )
Moderator: William Hooke, AMS Associate Executive Director
8:30 AM
PF1.1
Extreme Weather, Artificial Intelligence, and the Enterprise: Google’s Flood Forecasting Initiative in India
Jason Hickey, Google, Mountain View, CA
Recorded Presentation

9:00 AM
PF1.2
Scientific Knowledge, Individual Behavior, and Social Value
Scott Barrett, Columbia School of International and Public Affairs
Recorded Presentation

9:30 AM
Q & A

10:00 AM-10:30 AM: Monday, 13 January 2020


AM Coffee Break (Monday)
Location: Boston Convention and Exhibition Center

10:30 AM-12:00 PM: Monday, 13 January 2020

Recording files available
Joint Session 2
How Artificial Intelligence at Scale Will Link Weather and Climate Data to Society
Location: 157AB (Boston Convention and Exhibition Center)
Hosts: (Joint between the 10th Symposium on Advances in Modeling and Analysis Using Python; the 19th Conference on Artificial Intelligence for Environmental Science; the 36th Conference on Environmental Information Processing Technologies; and the Sixth Symposium on High Performance Computing for Weather, Water, and Climate )
Cochairs: David John Gagne II, Univ. of Oklahoma; Scott Collis, Argonne National Laboratory
10:30 AM
J2.1
How Python Can Help Us to Create the Physical Data Scientists of the Future (Core Science Keynote)
Amy McGovern, Univ. of Oklahoma, Norman, OK
Recorded Presentation

11:00 AM
J2.2
Cloud Nowcasting on Satellite Images: A Novel Dataset and Experimental Comparisons
Andreas Holm Nielsen, Aarhus Univ., Aarhus, Denmark; and A. Wagner, A. Iosifidis, and H. Karstoft
Recorded Presentation

11:15 AM
J2.4
Geocaching with Geohashing—Scaling Weather APIs with Python and Spark for Big Data Machine Learning
Alexander Kalmikov, QuantumBlack, a McKinsey Company, Cambridge, MA; and Y. Zhu, L. Zhang, and J. Annor
Recorded Presentation
Recorded Presentation
Recorded Presentation Manuscript (13.3 MB)

Handout (13.3 MB)

11:30 AM
J2.5
Frameworks for Gaining Insight and Machine Learning on Large Climate and Weather Datasets
Robert Jackson, Argonne National Laboratory, Argonne, IL; and S. Collis, I. Foster, B. Blaiszik, and S. Fiore
Recorded Presentation

J2.3
A Deep Neural Network Deriving Cloud Properties from Satellite Remote Sensing

11:00 AM-12:00 PM: Monday, 13 January 2020

Recording files available
Session 1A
AI for Environmental Science. Part I
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Carlos F. Gaitan, Arable Labs, Inc.; Zhonghua Zheng, University of Illinois at Urbana−Champaign
11:00 AM
1A.1
Lessons from Downscaling Precipitation in Tasmania, Australia, and the Northeast United States Using Machine Learning Approaches
Timothy Lynar, Univ. of New South Wales, Campbell, Australia; and C. D. Watson
Recorded Presentation

11:15 AM
1A.2
Climate Change Impacts on Global Ecology
Kate Duffy, Northeastern Univ., Boston, MA; and T. Gouhier and A. Ganguly

11:30 AM
1A.3
Creating Bias-Corrected Global Radiation Datasets from Climate Reanalysis Products Using Supervised Learning
Tirthankar Chakraborty, Yale Univ., New Haven, CT; and X. Lee
Recorded Presentation

11:45 AM
1A.4
Causal Inference: A Pathway for System Identification Using Observational Datasets
Mohammed Ombadi, Univ. of California, Irvine, Irvine, CA; and P. Nguyen, S. Sorooshian, and K. Hsu
Recorded Presentation

Recording files available
Session 1B
AI for Environmental Science. Part II
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Chair: Auroop R. Ganguly, Northeastern Univ.
11:00 AM
1B.1
Convection Forecast Enhanced by the Deep Learning of Radar Observations and Numerical Prediction
Leiming Ma, Shanghai Central Meteorological Observatory, Shanghai, China
Recorded Presentation

11:15 AM
1B.2
Smartphone Pressure Analysis with Machine Learning and Kriging
Conor McNicholas, Univ. of Washington, Seattle, WA
Recorded Presentation

11:30 AM
1B.3
Cloud-Based Machine Learning Capabilities to Improve Weather Event Predictions
Rich Baker, Peraton, Greenbelt, MD; and P. MacHarrie, L. Koye, H. Phung, J. Hansford, S. Causey, R. Niemann, and D. M. Beall
Recorded Presentation

11:45 AM
1B.4
Developing an Automated System to Predict Tornadoes in Simulated Nonclassical Convective Storms
Dylan J. Steinkruger, The Pennsylvania State Univ., State College, PA; and P. Markowski and G. S. Young
Recorded Presentation

2:00 PM-4:00 PM: Monday, 13 January 2020

Recording files available
Session 2A
Applications of Machine Learning in Earth System Modeling
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Christiane Jablonowski, University of Michigan; Christoph A. Keller, USRA/GESTAR NASA/GMAO
2:00 PM
2A.1
Discovering Novel Eddy Parameterizations with Machine Learning
Laure Zanna, Univ. of Oxford, Oxford, United Kingdom; and T. Bolton
Recorded Presentation

2:15 PM
2A.2
A Pure Deep Learning Approach to Precipitation Nowcasting
Jason Hickey, Google, Mountain View, CA; and C. Gazen, S. Agrawal, C. Bromberg, L. Barrington, V. Lakshmanan, and J. Burge
Recorded Presentation

2:30 PM
2A.3
Toward Physics-Informed Deep Learning for Turbulent Flows
Rui Wang, Northeastern Univ., Boston, MA; and A. Albert, K. Kashinath, M. Mustafa, and R. Yu
Recorded Presentation Manuscript (35.6 kB)

2:45 PM
2A.4
Deep Learning for Weather Prediction: Forecasting Globally Gridded 500-hPa Geopotential Heights on Short- to Medium-Range Time Scales
Jonathan A. Weyn, Univ. of Washington, Seattle, WA; and D. R. Durran and R. Caruana
Recorded Presentation

3:00 PM
2A.5
Nonlinear Averaging of Global NCEP Wave Ensemble Using NNs
Vladimir Krasnopolsky, NOAA, College Park, MD
Recorded Presentation

3:15 PM
2A.6
Machine Learning for Parameterization of Moist Processes in the Atmosphere
Janni Yuval, MIT, Cambridge, MA; and P. A. O'Gorman
Recorded Presentation

3:30 PM
2A.7
Developing the Snow Cover Fraction Schemes for Land Surface Models Using a Machine Learning Approach
Yuan-Heng Wang, The Univ. of Arizona, Tucson, AZ; and H. V. Gupta, P. D. Broxton, Y. Fang, A. Behrangi, X. Zeng, and G. Y. Niu
Recorded Presentation

2A.8
A Machine Learning–Based Parameterization of OH

Recording files available
Session 2B
Deep Learning Applications for Environmental Science. Part I
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Tianle Yuan, JCET; Sarvesh Garimella, ACME AtronOmatic, LLC
2:00 PM
2B.1
Classifying Global Low-Cloud Morphology with a Deep Learning Model: Results and Potential Use
Tianle Yuan, JCET, Baltimore, MD; and J. Mohrmann, H. Song, R. Wood, K. Meyer, and L. Oreopoulos
Recorded Presentation

2:15 PM
2B.2
A Deep Learning Approach for Intelligent Compression of Satellite Data
Sarvesh Garimella, ACME AtronOmatic, LLC, Portland, OR
Recorded Presentation

2:30 PM
2B.3
Artificial Intelligence (AI) Techniques to Enhance Satellite Data Use for Nowcasting and NWP/Data Assimilation
S. A. Boukabara, NOAA/NESDIS/STAR, College Park, MD; and E. Maddy, N. Shahroudi, R. N. Hoffman, T. Connor, S. Upton, J. E. Ten Hoeve III, V. Krasnopolsky, and K. Garrett
Recorded Presentation

2:45 PM
2B.4
Convective Storm Nowcasting Using a Deep Learning Approach
Lei Han, Ocean Univ. of China, Qingao, China; and W. Zhang and J. Sun
Recorded Presentation

3:00 PM
2B.5
Using Deep Learning Algorithms in Forecasting the Severe Haze Events in Southeast Asia
Chien Wang, CNRS/UPS, Toulouse, France
Recorded Presentation

3:15 PM
2B.6
Learning and Inference of Advective Fluid Transport in Geophysical Environments
Chinmay S. Kulkarni, MIT, Cambridge, MA; and P. F. J. Lermusiaux
Recorded Presentation

3:30 PM
2B.7
Downscaling Numerical Weather Models with GANs
Alok Singh, Terrafuse, Berkeley, CA; and B. White, A. Albert, and K. Kashinath
Manuscript (8.0 MB)

3:45 PM
2B.8
Finescale Surface Climate Data with Deep Learning
Thomas C. M. Martin, Univ. of São Paulo, São Paulo, Brazil; and H. R. Rocha, K. Brauman, M. Flörke, G. M. P. Perez, R. L. N. Wanderley, L. M. Domingues, and R. C. Abreu
Recorded Presentation

4:00 PM-6:00 PM: Monday, 13 January 2020


Formal Poster Viewing Reception (Mon)
Location: Hall B (Boston Convention and Exhibition Center)

Poster Session 1
AI for Environmental Science Poster Session I
Location: Hall B (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: John K. Williams, The Weather Company, An IBM Business; Zhonghua Zheng, University of Illinois at Urbana−Champaign
356A
A feature extraction and sequence prediction framework for N-Dimensional data structures: an application for subseasonal rainfall and streamflow forecast.
Thomas C. M. Martin, Univ. of São Paulo, São Paulo, Brazil; and G. M. P. Perez and H. R. Rocha

356
U.S. Water Prices: A Machine Learning Approach
Quinn McColly, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. Tissot and D. Yoskowitz

357
Gradient-Based Optimization to Reduce Uncertainty in Radar Rainfall Estimates Using Deep Learning Techniques and In Situ Measurements from Disdrometers
Haonan Chen, Colorado State University and NOAA Physical Sciences Laboratory, Boulder, CO; and R. Cifelli and V. Chandrasekar

358
A Volume-to-Point Approach of Radar-Based QPE
Ting-Shuo Yo, National Taiwan Univ., Taipei City, Taiwan; National Taiwan Univ., Taipei, Taiwan; and S. H. Su, C. C. Wu, C. W. Chang, and H. C. Kuo

359
Reconstruction of Severe Storms Observed by Weather Radars Using Recurrent Neural Networks
Cesar Beneti, SIMEPAR-Parana Meteorological System, Curitiba, Brazil; and C. Oliveira, S. Scheer, and L. Calvetti

Handout (2.5 MB)

Available air channel capacity prediction by weather-capacity Graphical Neural Network(wcGNN)

360
Automated Detection of the Above-Anvil Cirrus Plume Severe Storm Signature with Deep Learning
Charles Liles, NASA, Hampton, VA; and K. M. Bedka, T. D. Smith, Y. X. Huang, R. Biswas, E. Xia, C. Dolan, and A. Hosseini Jafari
Manuscript (1.6 MB)

Handout (1.9 MB)

361
Exploring the Application of Machine Learning to Identification of Storm Objects
Patrick A. Campbell, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and K. L. Ortega, S. S. Williams, and T. M. Smith

362
MRMS-based Hail Sizing and Classification Using Different, Large Databases
Jose Efraim Aguilar Escamilla, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and S. S. Williams and K. L. Ortega

Handout (1.0 MB)

363
Developing a Hail Probability Product for the Probabilistic Hazards Information Framework
Kiel L. Ortega, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and S. S. Williams

Handout (1.2 MB)

364
A New Machine Learning–Based Tornado Detection Algorithm for the WSR-88D Network
Thea Sandmael, CIMMS/Univ. of Oklahoma and NOAA/OAR/NSSL, Norman, OK; and K. L. Elmore and B. R. Smith

365
Comparison of Shallow and Deep Neural Network Water Temperature Predictions for Resource Management during Cold Stunning Events
Jensen DeGrande, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. Tissot, J. Wiliams, H. Kamangir, N. Durham, and S. Bates

366
Implementation of an Artificial Neural Network to Forecast Storm Surge Time Series
Alexandra N. Ramos-Valle, Rutgers Univ., New Brunswick, NJ; and E. N. Curchitser and C. L. Bruyère

367
Seasonal Hurricane Forecasting Using Machine Learning
Timothy Hall, Walkersville, MD; and K. Hall

Handout (1.2 MB)

368
Single-Station Forecasting from Deep Learning Methods
Nathaneal Beveridge, Air Force Institute of Technology, Wright-Patterson AFB, OK; and A. Geyer and R. C. Tournay

368A
Relative Importance of Thermodynamic Variables to the Worldwide Variability of Thunderstorms
Chuntao Liu, Texas A&M—Corpus Christi, Corpus Christi, TX; and N. Liu and P. Tissot

Tuesday, 14 January 2020

Monday | Tuesday | Wednesday | Thursday | top

8:30 AM-10:00 AM: Tuesday, 14 January 2020

Recording files available
Session 3A
AI Applied to Airborne or Spaceborne Earth Observation Datasets
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: James M. Kurdzo, MIT Lincoln Laboratory; Vladimir Krasnopolsky, NOAA
8:30 AM
3A.1
NN Technique for Producing Consistent Ocean Color Data for Assimilation in Ocean Models
Vladimir Krasnopolsky, NOAA, College Park, MD
Recorded Presentation

8:45 AM
3A.2
Machine Learning for inpainting QuikSCAT winds in Hawaii's Lee Region
William Chapman, SIO, La Jolla, CA; SIO, La Jolla, CA; SIO, La Jolla, CA; and T. Kilpatrick
Recorded Presentation

9:00 AM
3A.3
Using Deep Learning to Extract Regions of Interest (ROI) in Real Time from Geostationary Satellite Data
Christina Kumler, NOAA, Boulder, CO; and J. Stewart, D. Hall, and M. Govett
Recorded Presentation

9:15 AM
3A.4
The Optimal Single-Scattering Properties for Retrieving Ice Cloud Properties Based on Machine Learning Techniques
Yi Wang, Texas A&M Univ., College Station, TX; and P. Yang and Y. Huang

9:30 AM
3A.5
Neural Network Techniques for Hyperspectral IR Profiling of Cloudy Atmospheres
Adam B. Milstein, MIT Lincoln Laboratory, Lexington, MA; and W. J. Blackwell

9:45 AM
3A.6
Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data
Thomas Vandal, NASA/BAERI, Mountain View, CA; and R. Nemani
Recorded Presentation

Recording files available
Session 3B
High-Impact Weather Prediction with AI
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Montgomery L. Flora, University of Oklahoma, CIMMS, NSSL/NOAA; Stephan R. Sain, Jupiter Intelligence
8:30 AM
3B.1
Generating Ensemble-Derived Next-Day Probabilistic Severe Weather Forecasts with Machine Learning
Eric D. Loken, CIMMS/Univ. of Oklahoma, Norman, OK; and A. J. Clark
Recorded Presentation

8:45 AM
3B.2
Regional High-Impact Hail Forecasting Using Random Forests
Amanda Burke, CAPS/Univ. of Oklahoma, Norman, OK; and N. Snook and A. McGovern
Recorded Presentation

9:00 AM
3B.3
Using Machine Learning to Advance Next-Day Probabilistic Convective Hazard Prediction with Convection-Allowing Models
Ryan A. Sobash, NCAR, Boulder, CO; and D. J. Gagne II, C. S. Schwartz, and D. A. Ahijevych
Recorded Presentation

9:15 AM
3B.4
Using Machine Learning to Improve Storm-Scale 1-h Probabilistic Forecasts of Severe Weather
Montgomery L. Flora, Univ. of Oklahoma, CIMMS, NSSL/NOAA, Norman, OK; and C. Potvin, P. Skinner, and A. McGovern
Recorded Presentation

9:30 AM
3B.5
Using Deep Neuron Network to improve the Performance of NUCAPS Profiles in Lower Atmosphere
Zheng Ma, CIMSS/Univ. of Wisconsin, Madison, WI; and Z. Li, J. Li, and J. Sun
Recorded Presentation

9:45 AM
3B.6
Multiprior LSTM (mpLSTM): Predicting Visibility with Uncertainties from Complex Background States
Yao Xiao, Shanghai Em-Data Technology Co., Ltd, Shanghai, China; and Y. Meng, F. Qi, H. Zuo, X. Guo, Z. Yan, and C. Lu

10:00 AM-10:30 AM: Tuesday, 14 January 2020


AM Coffee Break (Tuesday)
Location: Boston Convention and Exhibition Center

10:30 AM-12:00 PM: Tuesday, 14 January 2020

Recording files available
Session 4
AI Applications for the Detection of Earth Science Phenomena
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Christina Kumler, University of Colorado; Aaron Kaulfus, Univ. of Alabama in Huntsville; Vladimir Krasnopolsky, NOAA
10:30 AM
4.1
Detecting Cloud Cover in Webcam Images Using Neural Networks: A Nowcasting Application
Thomas Nipen, Norwegian Meteorological Institute, Oslo, Norway; and E. Myrland, M. Pejcoch, C. Lussana, and I. A. Seierstad
Recorded Presentation

10:45 AM
4.2
Rapid Hailstone Characterization: A 3D Computer Vision Shape Analysis Model
Stan Biryukov, Understory Weather, Madison, WI; and K. Jero, A. Kubicek, E. Hewitt, and J. Leonard
Recorded Presentation

11:00 AM
4.3
Topological Data Analysis and Machine Learning Methods for Pattern Detection in Spatiotemporal Climate Data
Karthik Kashinath, LBNL, Berkeley, CA; and G. Muszynski, M. F. Wehner, V. Kurlin, M. Prabhat, and J. Balewski

11:15 AM
4.4
Analysis and Application of Mesoscale Radar Scenes during Severe Weather Events
Alex M. Haberlie, Louisiana State Univ., Baton Rouge, LA; and W. S. Ashley, V. A. Gensini, and M. Karpinski
Recorded Presentation

11:30 AM
4.5
Deep Learning Approach for the Detection of Areas Likely for Convection Initiation
Jebb Q. Stewart, NOAA, Boulder, CO; and C. Kumler, D. Hall, and M. W. Govett
Recorded Presentation

11:45 AM
4.6
Using Deep Learning to Create a Long-Term Climatology of Warm and Cold Fronts
Ryan A. Lagerquist, CIMMS, Norman, OK; and J. T. Allen and A. McGovern
Recorded Presentation

Recording files available
Joint Session 17
AI and Climate: Impact and Opportunities
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 33rd Conference on Climate Variability and Change; the 26th Conference on Probability and Statistics; and the Events )
Cochairs: Auroop Ganguly, Northeastern University; Karthik Kashinath, LBNL
10:30 AM
J17.1
Viewing Climate Signals through an AI Lens (Core Science Keynote)
Elizabeth A. Barnes, Colorado State Univ., Fort Collins, CO; and I. Ebert-Uphoff, J. Hurrell, C. W. Anderson, and D. Anderson
Recorded Presentation

11:00 AM
J17.2
Evaluation of Data-Driven Causality Discovery Methods among Dominant Climate Modes
Steve R. Hussung, Indiana Univ., Bloomington, Bloomington, IN; and S. Mahmud, A. Sampath, M. Wu, P. Guo, and J. Wang

11:15 AM
J17.3
Deep Learning Semantic Segmentation for Climate Change Precipitation Analysis
Andrew Lou, LBNL, Berkeley, CA; Univ. of California Berkeley, Berkeley, CA; and E. Chandran, M. Prabhat, J. Biard, K. Kunkel, M. F. Wehner, and K. Kashinath

11:30 AM
J17.4
The Future of Severe Thunderstorms in the United States—Insights from Combining Deep Learning and High-Resolution Modeling
Maria J. Molina, NCAR, Boulder, CO; and D. J. Gagne II and A. F. Prein
Recorded Presentation

11:45 AM
J17.5
Downscaling Climate Model Data for Energy and Crop Modelling Using Self-Organizing Maps
Andrew Polasky, The Pennsylvania State Univ., Univ. Park, PA; and J. L. Evans and J. Fuentes
Recorded Presentation

12:00 PM-1:30 PM: Tuesday, 14 January 2020


Lunch Break (Tuesday)

1:30 PM-2:30 PM: Tuesday, 14 January 2020

Recording files available
Session 5A
AI for Environmental Science. Part III
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
CoChair: Carlos F. Gaitan, University of Oklahoma
1:30 PM
5A.1
A Hybrid Empirical–Bayesian Artificial Neural Network Model of Salinity in the San Francisco Bay–Delta Estuary
Christine S. Lew, Tetra Tech, Lafayette, CA
Recorded Presentation

Handout (1.4 MB)

1:45 PM
5A.2
Utilizing Multimedia Modeling and Machine Learning to Assess Dissolved Oxygen as a Proxy for Hypoxia in Lake Erie
Christina Feng Chang, Univ. of Connecticut, Storrs, CT; and M. Astitha, V. Garcia, C. Tang, P. Vlahos, D. Wanik, and J. Yan
Recorded Presentation

2:00 PM
5A.3
Using Convolutional Neural Networks for the prediction of groundwater levels
Maximilian Nölscher, German Federal Institute for Geosciences and Natural Resources, Berlin, Germany; and S. Broda, H. Häntze, L. Jäger, P. Prasse, and S. Makowski
Recorded Presentation

2:15 PM
5A.4
Using Machine Learning to Predict Complete Winter Ice Cover of a Freshwater Lake
Campbell D. Watson, Thomas J. Watson Research Center, IBM, Yorktown Heights, NY; and G. Auger, M. Tewari, and L. A. Treinish
Recorded Presentation

Recording files available
Session 5B
Environet
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Karthik Kashinath, LBNL; Karthik mukkavilli, LBNL
1:30 PM
5B.1
Environet: A Project Update
Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms, Montreal, Canada; McGill Univ., Montreal, Canada

1:45 PM
5B.2
ClimateNet: Bringing the Power of Deep Learning to Weather and Climate Sciences via Open Datasets and Architectures
Karthik Kashinath, LBNL, Berkeley, CA; and M. Mudigonda, K. Yang, J. Chen, A. Greiner, and M. Prabhat

2:00 PM
5B.3
Community Earth System Science Datasets from NCAR
David John Gagne II, NCAR, Boulder, CO; and R. D. Loft and N. Flyer
Recorded Presentation

2:15 PM
5B.4
IceNet: A Large-Scale Dataset for Tracking Ice Flow Using Unsupervised Learning with Adversarial Networks
Yimeng Min, Montreal Institute for Learning Algorithms, Montreal, Canada; and S. K. Mukkavilli and Y. Bengio

Recording files available
Joint Session 22
Hybrid Machine Learning and Statistical Approaches
Location: 260 (Boston Convention and Exhibition Center)
Hosts: (Joint between the 26th Conference on Probability and Statistics; and the 19th Conference on Artificial Intelligence for Environmental Science )
Cochairs: Stephan R. Sain, Jupiter Intelligence; Dorit Hammerling, Jupiter Technology
1:30 PM
J22.1
Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California
Michael Scheuerer, CIRES, Boulder, CO; and M. B. Switanek, T. M. Hamill, and R. Worsnop
Recorded Presentation

1:45 PM
J22.3
The Long-Term Frontal System Variation for Future Climate Projections with Machine Learning Weather Classifier
Shih-Hao Su, Chinese Culture Univ., Taipei, Taiwan; and T. S. Yo, C. W. Chang, Y. C. Yu, and J. L. Chu

J22.2
Machine Learning calibration of CAM ensemble probabilistic forecasts of convective mode with conditional probabilities of severe weather hazards using the OU MAP ensemble from HWT SFEs.

2:15 PM
J22.4
Statistical–Physical Microphysics Parameterization Schemes: A Proposed Framework for Physically Based Microphysics Schemes That Learn from Observations
Marcus van Lier-Walqui, Columbia Univ. and NASA GISS, New York, NY; and H. Morrison, M. R. Kumjian, K. J. Reimel, O. P. Prat, S. Lunderman, and M. Morzfeld
Recorded Presentation

2:30 PM-3:00 PM: Tuesday, 14 January 2020


PM Coffee Break (Tuesday)
Location: Boston Convention and Exhibition Center

3:00 PM-4:00 PM: Tuesday, 14 January 2020

Recording files available
Session 6
History of AI in Environmental Science (Centennial)
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Philippe Tissot, Texas A&M University−Corpus Christi; Sue Ellen Haupt, NCAR
3:00 PM
6.1
The History of AI in the Environmental Sciences (Core Science Keynote)
Sue Ellen Haupt, NCAR, Boulder, CO
Recorded Presentation

3:30 PM
6.2
AI Applications to the Earth Sciences: 35 Years through the Lens of the AMS Artificial Intelligence Committee
Philippe E. Tissot, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX
Recorded Presentation

3:45 PM
Panel Discussion

Recording files available
Joint Session 30
Transitioning Artificial Intelligence (AI) Prediction Systems to Operations
Location: 251 (Boston Convention and Exhibition Center)
Hosts: (Joint between the 10th Conference on Transition of Research to Operations; and the 19th Conference on Artificial Intelligence for Environmental Science )
Cochairs: John K. Williams, The Weather Company, An IBM Business; Daniel Rothenberg, ClimaCell
3:00 PM
J30.1
AI for Earth: Building an Extensible, Distributed API Pipeline Platform for Artificial Intelligence
Patrick Flickinger, Microsoft, Redmond, WA
Recorded Presentation

3:15 PM
J30.2
Lightning Prediction for Space Launch Using Machine Learning Based on Electric Field Mills and Lighting Detection and Ranging Data
Anson Cheng, Air Force Institute of Technology, Wright-Patterson AFB, OH; and A. J. Geyer

3:30 PM
J30.3
Predicting Weather Conditions Utilizing Artificial Neural Networks for C-17 Mission Planning
Garrett A Alarcon, Air Force Institute of Technology, Wright-Patterson AFB, OH; and A. J. Geyer
Recorded Presentation

3:45 PM
J30.4
Artificial Intelligence–Based Ensemble Modeling for Correction of GPM IMERG Precipitation Product over the Brahmaputra River Basin
MD Abul Ehsan Bhuiyan, Univ. of Connecticut, STORRS, CT; and N. K. Biswas, R. Raihan Sayeed Khan, S. J. Ilham, and C. witharana

4:00 PM-6:00 PM: Tuesday, 14 January 2020


Formal Poster Viewing Reception (Tues)
Location: Hall B (Boston Convention and Exhibition Center)

Wednesday, 15 January 2020

Monday | Tuesday | Wednesday | Thursday | top

8:30 AM-10:00 AM: Wednesday, 15 January 2020

Recording files available
Session 7A
AI in Radar Observations
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Sarvesh Garimella, ACME AtronOmatic, LLC; Alex M. Haberlie, Louisiana State Univ.
8:30 AM
7A.1
An AI Approach for Generating Instantaneous Rain Rates from Volumetric Radar Scans
Sarvesh Garimella, ACME AtronOmatic, LLC, Portland, OR
Recorded Presentation

8:45 AM
7A.2
Radar Quantitative Precipitation Estimate Results Using a Convolution Neural Network
Micheal Simpson, NOAA/NSSL, Norman, OK; and J. Zhang and K. W. Howard
Recorded Presentation

9:00 AM
7A.3
Machine Learning Techniques for Radar-Based Hail Size Prediction
Skylar S. Williams, OU/CIMMS and NOAA/OAR/NSSL, Norman, OK; and K. L. Ortega
Recorded Presentation

9:15 AM
7A.4
An Investigation of Two Machine Learning Radar-Based Hail Discrimination Algorithms
Kimberly L. Elmore, CIMMS/Univ. of Oklahoma and NOAA/OAR/NSSL, Norman, OK; and K. L. Ortega and J. C. Snyder
Recorded Presentation

9:30 AM
7A.5
Assessment of Two Techniques Used to Identify ZDR Arcs Automatically in Radar Observations
Allison T. LaFleur, Purdue Univ., West Lafayette, IN; and R. Tanamachi and R. E. Nelson
Recorded Presentation

9:45 AM
7A.6
Locating Bird Roosts Using NEXRAD Radar Data and Image Segmentation
Katherine Avery, Univ. of Oklahoma, Norman, OK; and A. McGovern, E. Bridge, and J. F. Kelly
Recorded Presentation

Recording files available
Session 7B
Deep Learning Applications for Environmental Science. Part II
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Chair: Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila)
8:30 AM
7B.1
Multisource Data Integration under a Deep Learning Framework to Improve Streamflow Forecast Ability
Dapeng Feng, The Pennsylvania State Univ., Univ. Park, PA; and C. Shen and K. Fang
Recorded Presentation

8:45 AM
7B.2
Using Deep Learning to Detect Atmospheric Rivers across Climate Datasets and Resolutions
Ankur Mahesh, Lawrence Berkeley National Lab, Berkeley, CA; ClimateAi, San Francisco, CA; and T. A. O'Brien, K. Kashinath, M. Mudigonda, M. Prabhat, C. A. Shields, J. J. Rutz, L. R. Leung, A. E. Payne, F. M. Ralph, M. Wehner, and W. D. Collins

9:00 AM
7B.3
A Comparison of Deep Learning, Shallow Neural Networks, and Principal Component Analysis Based Approaches to Thunderstorm Prediction
Hamid Kamangir, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. E. Tissot, W. G. Collins, and S. A. King
Recorded Presentation

9:15 AM
7B.4
Detecting and Classifying Tornado Damage Utilizing Deep Neural Networks and UAS-Based Imagery
Melissa A. Wagner, Arizona State Univ., Tempe, AZ; and Z. Chen, J. Das, R. K. Doe, and R. S. Cerveny
Recorded Presentation

9:30 AM
7B.5
Using Deep Learning to Predict Error Growth in Model Forecasts
Christopher P. Rattray, Univ. of Oklahoma, Norman, OK; and D. B. Parsons

Recording files available
Joint Session 37
Physical Interpretability in Machine Learning
Location: 260 (Boston Convention and Exhibition Center)
Hosts: (Joint between the 26th Conference on Probability and Statistics; the 19th Conference on Artificial Intelligence for Environmental Science; and the 30th Conference on Weather Analysis and Forecasting (WAF)/26th Conference on Numerical Weather Prediction (NWP) )
Cochairs: Elizabeth Satterfield, NRL; Philippe Tissot, Texas A&M Univ. - Corpus Christi
8:30 AM
J37.1
Multiresolution Cluster Analysis—Addressing Trust in Climate Classification
Derek DeSantis, LANL, Los Alamos, NM; and P. Wolfram and B. Alexandrov
Recorded Presentation

8:45 AM
J37.2
Understanding What Deep Learning Has Learned about Tornadoes
Ryan A. Lagerquist, CIMMS, Norman, OK; and A. McGovern, D. J. Gagne II, C. R. Homeyer, and T. M. Smith
Recorded Presentation

9:00 AM
J37.3
Selected Methods from Explainable AI to Improve Understanding of Neural Network Reasoning for Environmental Science Applications
Imme Ebert-Uphoff, CIRA–Colorado State Univ., Fort Collins, CO; and K. Hilburn, B. A. Toms, and E. A. Barnes
Recorded Presentation

9:15 AM
J37.4
Emulation of Bin Microphysical Processes with Machine Learning
David John Gagne II, NCAR, Boulder, CO; and C. C. Chen and A. Gettelman
Recorded Presentation

9:30 AM
J37.5
Using Physically Interpretable Neural Networks to Discover Modes of Climate and Weather Variability
Benjamin A. Toms, Colorado State Univ., Fort Collins, CO; and E. A. Barnes and I. Ebert-Uphoff
Recorded Presentation

9:45 AM
J37.6
Lessons Learned Using ML for Knowledge Discovery in the Atmospheric Sciences
Amy McGovern, Univ. of Oklahoma, Norman, OK
Recorded Presentation

10:00 AM-10:30 AM: Wednesday, 15 January 2020


AM Coffee Break (Wednesday)
Location: Boston Convention and Exhibition Center

10:30 AM-12:00 PM: Wednesday, 15 January 2020

Recording files available
Session 8
AI for Environmental Science. Part IV
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
CoChair: Auroop R. Ganguly, Northeastern Univ.
10:30 AM
8.1
Predicting Storm Prediction Center Watch Likelihood Using Machine Learning
David Harrison, CIMMS/Univ. of Oklahoma and NOAA/NWS/Storm Prediction Center, Norman, OK; and A. McGovern and C. D. Karstens
Recorded Presentation

10:45 AM
8.2
EnSOMble Forecasting: Analyzing Simulated Supercell Environments from Convection-Allowing Models Using Self-Organizing Maps
Burkely T. Gallo, CIMMS/Univ. of Oklahoma and NOAA/NWS/SPC, Norman, OK; and A. K. Anderson-Frey and M. L. Flora
Recorded Presentation

11:00 AM
8.3
Wind Variability Analysis for the Kuwait Region Using Self-Organizing Maps
Steven M. Naegele, The Pennsylvania State Univ., Univ. Park, PA; NCAR, Boulder, CO; and T. C. McCandless, S. E. Haupt, G. S. Young, and S. J. Greybush

11:15 AM
8.4
Evaluation of a Hybrid Modeling Approach to Predict the Atmospheric State by Blending Numerical Modeling and Machine Learning
Troy J. Arcomano, Texas A&M Univ., College Station, TX; and I. Szunyogh, B. Hunt, and E. Ott
Recorded Presentation

11:30 AM
8.5
A Short-Term Hail Prediction System Based on Numerical Weather Modeling and Machine Learning
Chandrasekar Radhakrishnan, Colorado State Univ., Fort Collins, CO; and V. Chandrasekar, A. Kubicek, J. krzak, and E. Hewitt

11:45 AM
8.6
Development of a Radar-Identified Storm Cell and Track Dataset for Storm Motion Distributions and Machine Learning Applications
Dianna M. Francisco, Univ. of Oklahoma/CIMMS and NOAA/NSSL, Norman, OK; and T. M. Smith, K. M. Calhoun, and P. A. Campbell
Recorded Presentation

Recording files available
Joint Session 43
Tropical Cyclone Analysis and Prediction with Machine Learning I
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the Tropical Meteorology and Tropical Cyclones Symposium; and the Events )
Cochairs: Jebb Stewart, NOAA/ESRL, Boulder and CIRA/Colorado State Univ.; Eric D. Loken, CIMMS/University of Oklahoma
10:30 AM
J43.1
Using Geostationary Imagery to Peer through the Clouds Revealing Hurricane Structure
C. J. Slocum, CIRA, Fort Collins, CO; and J. Knaff
Recorded Presentation

10:45 AM
J43.2
Probabilistic Rapid Intensification Prediction with Convolutional Neural Networks and HWRF
David John Gagne II, NCAR, Boulder, CO; and C. M. Rozoff and J. L. Vigh
Recorded Presentation

Handout (2.7 MB)

11:00 AM
J43.3
A Review of Support Vector Machine Performance on Tropical Cyclone Intensity Prediction with Imbalanced Datasets
Mu-Chieh Ko, NOAA/AOML/HRD, Miami, FL; and M. Kubat, S. G. Gopalakrisnan, and F. D. Marks
Recorded Presentation

11:15 AM
J43.4
Combining Artificial Intelligence and Physics-Based Modeling Techniques to Improve Hurricane Track and Intensity Forecasting
Narges Shahroudi, Riverside Technology, Inc., and NOAA/NESDIS/STAR, College Park, MD; and E. Maddy, S. A. Boukabara, V. M. Krasnopolsky, and R. N. Hoffman
Recorded Presentation

11:30 AM
J43.5
Using Evolutionary Programming to Generate Improved Tropical Cyclone Intensity Forecasts
Jesse Schaffer, Univ. of Wisconsin−Milwaukee, Milwaukee, WI; and P. Roebber and C. Evans
Recorded Presentation

11:45 AM
J43.6
An Updated Atlantic Basin Tropical Cyclone Rapid Intensification Scheme Using Machine Learning and Operational Forecast Data
Andrew Mercer, Mississippi State Univ., Mississippi State, MS; and A. D. Grimes and K. M. Wood
Recorded Presentation

12:00 PM-1:30 PM: Wednesday, 15 January 2020


Lunch Break (Wednesday)

1:30 PM-2:30 PM: Wednesday, 15 January 2020

Recording files available
Session 9A
AI Applications for Air Quality
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
CoChair: Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila)
1:30 PM
9A.1
PMNet: Improving Aerosol Predictions Using Deep Neural Nets for Limited Ground Stations
Caleb Hoyne, McGill Univ., Montreal, Canada; and S. K. Mukkavilli and D. Meger
Recorded Presentation

1:45 PM
9A.2
Improving Geophysical Air Quality Forecasts With Machine Learning Algorithms
Hervé Petetin, Barcelona Supercomputing Center, Barcelona, Spain; and A. Soret, M. Guevara, K. Serradell, and C. Pérez García-Pando

2:00 PM
9A.3
Using a Feed-Forward MLP Neural Network to Fill Gaps in N2O Emission Data
Benjamin Matthew Fehr, Univ. of New Hampshire, Durham, NH; and C. Dorich and R. Conant
Recorded Presentation

2:15 PM
9A.4
Satellite-Derived PM2.5 concentrations over South Korea Using GOCI Aerosol Products and a Machine Learning Method
Yeseul Cho, Yonsei Univ., Seoul, Korea, Republic of (South); and J. Kim, H. Lee, M. Choi, S. Lee, H. Lim, J. Im, and Y. S. Choi

Recording files available
Session 9B
Machine Learning for Subseasonal to Seasonal Prediction
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Carlos F. Gaitan, Arable Labs, Inc.; Maria J. Molina, AccuWeather, Inc.
1:30 PM
9B.1
Applying Machine Learning to Improve Subseasonal-to-Seasonal (S2S) Forecasts
Soukayna Mouatadid, Univ. of Toronto, Toronto, ON, Canada; and J. Cohen and L. Mackey
Recorded Presentation

1:45 PM
9B.2
Using Machine Learning to Improve Subseasonal-to-Seasonal (S2S) Prediction
Richard Garmong, Univ. of Georgia, Athens, GA; and R. Bolinger and R. S. Schumacher
Recorded Presentation

9B.3
Basin of Prediction for Seasonal Weather Forecasting Using Self-Similar Power Transforms

2:00 PM
9B.4
Applications of Deep Learning to S2S Precipitation Prediction and Downscaling for the Middle East and North Africa
Hamada S. Badr, The Johns Hopkins Univ., Baltimore, MD; and K. Bergaoui, B. F. Zaitchik, A. Hazra, A. McNally, C. D. Peters-Lidard, and R. McDonnell

Recording files available
Joint Session 47
Big Data, Big Computing, Bigger Science: High-Performance Computing Enabled Artificial Intelligence
Location: 212 (Boston Convention and Exhibition Center)
Hosts: (Joint between the Sixth Symposium on High Performance Computing for Weather, Water, and Climate; and the 19th Conference on Artificial Intelligence for Environmental Science )
Cochairs: Timothy S. Sliwinski, Group NIRE; David John Gagne II, Univ. of Oklahoma
1:30 PM
J47.1
Deep Learning for Automated Feature Detection in Climate, Weather, and Space
David Hall, NVIDIA Corporation, Lafayette, CO; and C. Tierney, S. Posey, and J. Hooks
Recorded Presentation

1:45 PM
J47.2
Toward Unsupervised Segmentation of Extreme Weather Events
Adam Rupe, Univ. of California, Davis, CA; and K. Kashinath, N. Kumar, V. Lee, M. Prabhat, and J. P. Crutchfield

J47.3
Assessing Changes in Tropical Cyclone Genesis under Varying Climate Scenarios

2:15 PM
J47.4
Meteorological Event Identification Using National Weather Service Forecast Discussions
Brian Freitag, Univ. of Alabama in Huntsville, Huntsville, AL; and K. Bugbee, J. Miller, J. Zhang, R. Ramachandran, and M. Maskey
Recorded Presentation

2:30 PM-3:00 PM: Wednesday, 15 January 2020


PM Coffee Break (Wednesday)
Location: Boston Convention and Exhibition Center

3:00 PM-4:00 PM: Wednesday, 15 January 2020

Recording files available
Session 10
The Future of AI in Environmental Science
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: David John Gagne II, Univ. of Oklahoma; Amy McGovern, Univ. of Oklahoma; Carlos F. Gaitan, Arable Labs, Inc.
3:00 PM
10.1
AI2ES: Alpha-Institute—Artificial Intelligence for Environmental Sciences
Amy McGovern, Univ. of Oklahoma, Norman, OK; and J. Hickey, D. Hall, I. Ebert-Uphoff, C. Thorncroft, J. Williams, R. J. Trapp, R. He, and C. Bromberg
Recorded Presentation

3:15 PM
10.2
Building a Cross-Disciplinary Network to Tackle Climate Change with Machine Learning
Kelly Kochanski, Univ. of Colorado Boulder, Boulder, CO; and D. Rolnick, P. Donti, and L. Kaack
Recorded Presentation

3:30 PM
10.3
NOAA’s Artificial Intelligence (AI) Strategy
Jamese Sims, NOAA/OFCM, Silver Spring, MD; and T. Gallaudet, W. L. Michaels, V. M. Krasnopolsky, S. A. Boukabara, C. Alexander, G. Dusek, F. Indiviglio, E. J. Kearns, M. Malik, J. McDonough, V. Ramaswamy, J. Q. Stewart, N. Saraf, H. L. Tolman, and F. Werner
Recorded Presentation

3:45 PM
Panel Discussion

Recording files available
Joint Session 52
Artificial Intelligence Applications in the Coastal Environment
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 18th Symposium on the Coastal Environment; and the Events )
Cochairs: Philipe Tissot, Texas A&M University-Corpus Christi; Michael J. Starek, Texas A&M University−Corpus Christi
3:00 PM
J52.1
Machine Learning Approaches for the Quality Control of Tide Gauge Observations
Gregory Dusek, NOAA, Silver Spring, MD; and P. Tissot, A. Pruessner, V. Soika, and G. Story
Recorded Presentation

3:15 PM
J52.2
Applications of Artificial Neural Network in Predicting Water Quality Indicators: Case Studies from Korean Coastal Waters
Jongseong Ryu, Anyang Univ., Ganghwa-gun, Korea, Republic of (South); and Y. H. Kim, H. C. Kim, S. Son, and M. Lee
Recorded Presentation

3:30 PM
J52.3
Machine Learning Classification of Flood Waters from Hurricanes Harvey and Florence as Captured by Synthetic Aperture Radar and Optical Remote Sensing
A. L. Molthan, MSFC, Huntsville, AL; and A. Melancon, J. R. Bell, L. A. Schultz, and E. Gebremichael
Recorded Presentation

3:45 PM
J52.4
Suggesting an Efficient Deep Learning Architecture for Coastal Wetland Land Cover Mapping with UAS Imagery
Mohammad Pashaei, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and H. Kamangir, M. J. Starek, P. Tissot, and S. A. King
Recorded Presentation

4:00 PM-6:00 PM: Wednesday, 15 January 2020


Formal Poster Viewing Reception (Wed)
Location: Hall B (Boston Convention and Exhibition Center)

Poster Session 2
AI for Environmental Science Poster Session II
Location: Hall B (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: John K. Williams, The Weather Company; Zhonghua Zheng, University of Illinois at Urbana−Champaign; Maria J. Molina, AccuWeather, Inc.
1352
Projected Changes in Summertime Circulation Patterns Imply Increased Drought Risk for the South-Central United States
Jung-Hee Ryu, Texas Tech Univ., Lubbock, TX; and K. Hayhoe and S. L. Kang

1353
Wind Power Forecasting Using Hybrid ANN–NWP Models
Martin Boden, ETH, Zurich, Switzerland; and B. Afshar, G. West, and R. Stull

1354
Improved Forecasts of Incoming Solar Radiation Using Machine Learning and Ensemble Weather Model Output
Sarah-Ellen Calise, Northern Vermont Univ., Lyndonville, VT; and D. M. Siuta

1355
Characterizing Regime-Based Flow Uncertainty for Source Term Estimation Applications
Robert C. Tournay, Air Force Institute of Technology, Wright-Patterson AFB, OH; US Air Force, Offutt Air Force Base, NE; and J. Fioretti

1356
Applications of Deep Learning to Enhance Environmental Sensing Capabilities of Mobile Devices and Other Image Sensors
David R. Callender, Creare LLC, Hanover, NH; and J. Bieszczad, M. Shapiro, and J. Milloy

Handout (952.3 kB)

1357
AI-Powered Chatbot For Effective Weather Communication
Saiadithya Cumbulam Thangaraj, Earth Networks, Germantown, MD; and M. Stock and J. Lapierre

1358
A Machine Learning Based Cloud Mask and Thermodynamic Phase Classification Method using Suomi-NPP VIIRS Spectral Observations
Chenxi Wang, GSFC/ESSIC/UMD, College Park, MD; and S. Platnick, K. Meyer, Z. Zhang, and Y. Zhou

1359
The Use of a Deep Neural Network to Represent Radiation Transfer Calculations in the E3SM
Linsey Passarella, ORNL, Oak Ridge, TN; and A. Pal, S. Mahajan, and M. R. Norman

1360
Emulating Numeric Hydroclimate Models with Physics-Informed cGANs
Ashray H Manepalli, Terrafuse, Berkeley, CA; and A. Albert, A. M. Rhoades, D. Feldman, and A. D. Jones

1361
Machine Intelligence Approach to Precipitation Nowcasting for Transportation Network-of-Networks Resilience
Nishant Yadav, Northeastern Univ., Boston, MA; and A. Ganguly and S. Chatterjee

1362
An Update on the MRMS Product Suite for the Transportation Sector
Heather D. Reeves, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and S. Handler, A. Eddy, and A. A. Rosenow

1363
Applying Deep Learning to Sea Surface Temperature Retrieval
Zichao Liang, Atholton High School, Columbia, MD; and X. Liang

1364
Applying Deep Learning to Top-of-the-Atmosphere Radiance Simulation for VIIRS by Community Radiative Transfer Model
X. Liang, ESSIC/UMD, College Park, MD; and Q. Liu

1365
Hourly PM2.5 Estimates from Different Measurements of a Geostationary Satellite Using an Ensemble Learning Algorithm
Jianjun Liu, Environmental Model and Data Optima Laboratory, Laurel, MD

1366
XCO2 Retrieval Using a Neural Network–Based Algorithm from OCO–2 measurements
Jaemin Hong, Yonsei Univ., Seoul, Korea, Republic of (South); and J. Kim, W. Kim, Y. Cho, H. Chong, and H. Lim

1367
Application of Machine Learning to Classify and Predict Events of Severe PM2.5 Pollution in Taiwan
Wei-Ting Chen, National Taiwan Univ., Taipei, Taiwan; and C. W. Chang, P. J. Chen, T. S. Yo, S. H. Su, C. Y. Su, and C. M. Wu

1367A
Using Statistical Learning to Predict the Extratropical Transition of Tropical Cyclones
Melanie Bieli, Columbia Univ., New York, NY; and A. H. Sobel, S. J. Camargo, and M. K. Tippett

6:30 PM-9:00 PM: Wednesday, 15 January 2020


Centennial Celebration (Centennial)
Location: Ballroom East (Boston Convention and Exhibition Center)

Thursday, 16 January 2020

Monday | Tuesday | Wednesday | Thursday | top

8:30 AM-9:30 AM: Thursday, 16 January 2020

Recording files available
Joint Session 60
Incorporating Data Science and Machine Learning into Atmospheric Science Education
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 29th Conference on Education; and the Events )
Cochairs: David John Gagne II, Univ. of Oklahoma; Dorit Hammerling, Jupiter Technology
8:30 AM
J60.2
Client-Driven, University Student Capstone Project in Environmental Machine Learning
Timothy J. Hall, The Aerospace Corporation, Greenbelt, MD; and E. B. Wendoloski
Recorded Presentation

J60.1
Atmospheric Sciences + Data Science at the University of Illinois Urbana-Champaign

9:00 AM
J60.1A
Broadening of the AI Workforce through a Junior College Program
Philippe Tissot, Texas A&M Univ. - Corpus Christi, Corpus Christi, TX
Recorded Presentation

9:15 AM
J60.3
Practical AI in the Classroom
Jianghao Wang, MathWorks, Natick, MA
Recorded Presentation

9:30 AM
J60.4
Mining Students' Digital Behaviors in Class to Create an Earlier Warning System of Student Success
Perry J. Samson, Univ. of Michigan, Ann Arbor, MI
Recorded Presentation

Recording files available
Joint Session 61
Societal and Economic Impacts of AI
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 15th Symposium on Societal Applications: Policy, Research and Practice; and the Events )
Cochairs: Daniel Rothenberg, ClimaCell; Tyler C. McCandless, NCAR
8:30 AM
J61.1
From Decision Support to Decision Services: An Expanded Role for AI in the Weather Enterprise
John K. Williams, The Weather Company, Andover, MA; and P. Neilley
Recorded Presentation

8:45 AM
J61.2
Analyzing and Predicting the Influence of Weather on Health, Safety, and Environment in an Operational Setting
David Gold, IBM, Houston, TX; and T. Garvin
Recorded Presentation

9:00 AM
J61.3
Predicting Weather-Related Train Delays
Roope Tervo, Finnish Meteorological Institute, Helsinki, Finland; and L. Daniel and J. S. ylhaisi
Recorded Presentation

9:15 AM
J61.4
Integrated Climate Extremes: Modeling Future Impacts for Visualizing Climate Change
Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms, Montreal, Canada; and Y. Min, A. Madanchi, V. B. Pacela, S. Patel, and Y. Bengio
Recorded Presentation

9:30 AM-10:30 AM: Thursday, 16 January 2020


Exhibit Hall Breakfast
Location: Hall A (Boston Convention and Exhibition Center)

10:30 AM-12:00 PM: Thursday, 16 January 2020

Recording files available
Joint Session 65
Machine Learning Applications in the Energy Sector
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 11th Conference on Weather, Climate, and the New Energy Economy; and the Events )
Cochairs: Tyler C. McCandless, NCAR; Sue Ellen Haupt, NCAR
10:30 AM
J65.1
Machine and Deep Learning Methods for Fault Detection and Classification in Photovoltaic Modules
Warren James Brettenny, Nelson Mandela Univ., Port Elizabeth, South Africa; and C. W. Dunderdale, C. M. Clohessy, E. E. van Dyk, and G. D. Sharp

10:45 AM
J65.2
New Developments in Weather-Based Power Outage Prediction Modeling
Diego Cerrai, Univ. of Connecticut, Storrs, CT; and P. Watson, M. Koukoula, F. Yang, and E. Anagnostou
Recorded Presentation

11:00 AM
J65.3
Performance of Alternate Machine Learning Configurations for the 0–120-h Prediction of Extreme Wind Gusts for Outage Management in the Consolidated Edison Company of the New York Service Area
John W. Zack, MESO, Inc., Troy, NY; and J. M. Freedman, M. Berlinger, and C. Cheng
Recorded Presentation

11:15 AM
J65.4
Comparing Implicit versus Explicit Regime Identification in Machine Learning Approaches to Short-Range Solar Power Forecasting
Tyler C. McCandless, NCAR, Boulder, CO; and S. Dettling and S. E. Haupt
Recorded Presentation

11:30 AM
J65.5
Optimizing Training Windows for Wind and Solar Generation Forecasting
Daniel B Kirk-Davidoff, EPRI, Albany, NY; and P. Tardaguila and T. Melino
Recorded Presentation

11:45 AM
J65.6
A Deep Learning Framework for Forecasting Power in a Full-Scale Wind Farm
Rajitha Meka, Univ. of Texas at San Antonio, San Antonio, TX; and K. Bhaganagar and A. Alaeddini
Recorded Presentation

Recording files available
Joint Session 66
Machine Learning for Subgrid Parameterization in Weather and Climate Models
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 30th Conference on Weather Analysis and Forecasting (WAF)/26th Conference on Numerical Weather Prediction (NWP); and the Events )
Cochairs: Ryan A. Lagerquist, CIMMS; Christiane Jablonowski, University of Michigan; Carlos F. Gaitan, Arable Labs, Inc.
10:30 AM
J66.1
Building a Hierarchy of Hybrid, Neural Network Parameterizations of Convection
Tom Beucler, Univ. of California, Irvine, Irvine, CA; Columbia Univ., New York, CA; and P. Gentine, M. S. Pritchard, S. Rasp, and V. Eyring
Recorded Presentation

10:45 AM
J66.2
Data-Driven Superparameterization Using Deep Learning: Experimentations with a Multiscale Lorenz 96 Model
Pedram Hassanzadeh, Rice University, Houston, TX; and A. Chattopadhyay, A. Subel, and K. Palem

11:00 AM
J66.3
Machine Learning Parameterization of the Surface Layer: Integration with WRF
David John Gagne II, NCAR, Boulder, CO; and T. C. McCandless, B. Kosovic, A. DeCastro, R. D. Loft, S. E. Haupt, and B. Yang
Recorded Presentation

11:15 AM
J66.4
Data-Driven Approaches for Simulating Rainfall in Climate Models
R. Saravanan, Texas A&M Univ., College Station, TX; and J. Yang, M. Jun, C. Schumacher, J. Wang, and R. K. W. Wang
Recorded Presentation

11:30 AM
J66.6
Utilizing Machine Learning to Replace Physical Parameterization Schemes: How Do Different Techniques Compare?
Garrett Limon, Univ. of Michigan, Ann Arbor, MI; and C. Jablonowski
Recorded Presentation

J66.5
Toward Subgrid-Scale Parameterizations Using a Superresolution Generative Adversarial Network

1:30 PM-3:00 PM: Thursday, 16 January 2020

Recording files available
Joint Session 69
Advances in the Use of Artificial Intelligence Techniques in Support of Aviation, Range, and Aerospace Meteorology
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 20th Conference on Aviation, Range, and Aerospace Meteorology; and the Events )
Cochairs: Haig Iskendarian, MIT; James M. Kurdzo, MIT Lincoln Laboratory
1:30 PM
J69.1
Using a Neural Network to Predict Future Radar Frames
Claire Sheila Bartholomew, Met Office, Exeter, UK; Univ. of Leeds, Leeds, United Kingdom; and D. Hogg, J. H. Marsham, and T. Howard
Recorded Presentation

1:45 PM
J69.2
The WSR-88D Chaff Detection Algorithm Utilizing a Support Vector Machine Based on Human Truthing
James M. Kurdzo, MIT Lincoln Laboratory, Lexington, MA; and B. J. Bennett, D. J. Smalley, M. F. Donovan, and E. R. Williams
Recorded Presentation

2:00 PM
J69.3
Global Synthetic Weather Radar in AWS GovCloud for the U.S. Air Force
Mark S. Veillette, MIT Lincoln Laboratory, Lexington, MA; and H. Iskenderian, P. M. Lamey, C. J. Mattioli, A. Banerjee, M. Worris, A. B. Proschitsky, R. F. Ferris, A. Manwelyan, S. Rajagopalan, H. Usmani, T. E. Coe, J. E. Luce, and B. A. Esgar
Recorded Presentation

2:15 PM
J69.4
Detection of Aircraft Lightning Potential Areas by Using a Deep Neural Network with Interpretability
Eiichi Yoshikawa, Japan Aerospace Exploration Agency, Mitaka, Japan; and T. Ushio

2:30 PM
J69.5
Improvements to Convective Weather Avoidance Modeling Using Supervised Learning
Christopher J. Mattioli, MIT Lincoln Laboratory, Lexington, MA; and M. Matthews, H. Iskendarian, and M. S. Veillette
Recorded Presentation

2:45 PM
J69.6
Short-Term Wind Forecasts for Aviation
William J. Dupree, MIT Lincoln Laboratory, Lexington, MA; and M. S. Veillette, A. Banerjee, J. P. Morgan, T. Bonin, H. Iskenderian, and M. McPartland
Recorded Presentation

Recording files available
Joint Session 70
Machine Learning and AI for Space Weather
Location: 205A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 17th Conference on Space Weather; and the 19th Conference on Artificial Intelligence for Environmental Science )
Cochairs: Kelsey Doerksen, Univ. of Western Ontario; Alexander Engell, NextGen Federal Systems; David John Gagne II, Univ. of Oklahoma
1:30 PM
J70.1
Imputation of Geomagnetic Disturbance Fields with Nonlinear Regression Based on Synthetic Data
E. Joshua Rigler, USGS, Denver, CO; and D. Lin, K. Pham, and G. Lucas
Recorded Presentation

1:45 PM
J70.2
Machine Learning Classification of Interplanetary Coronal Mass Ejections Using Satellite Accelerometers
Kelsey Doerksen, Univ. of Western Ontario, London, ON, Canada
Recorded Presentation

2:00 PM
J70.3
Developing Deep Learning for Solar Feature Recognition in Satellite Images
Michael Kirk, GSFC, Greenbelt, MD; and R. Attie, J. Stockton, M. Penn, D. Hall, B. Thompson, and J. Willert
Recorded Presentation

2:15 PM
J70.4
Tracking Equatorial Plasma Bubbles Based on an Artificial Neural Network Classifier
David DeBonis, AER, Albuquerque, NM; and B. Drummond

2:30 PM
J70.5
Leveraging Topological Data Analysis and Deep Learning for Solar Flare Prediction
Thomas Berger, Univ. of Colorado, Boulder, CO; and V. Deshmukh, E. Bradley, J. Meiss, and N. Nishizuka
Recorded Presentation

2:45 PM
J70.6
Emerging Frontiers in Science and Exploration Enabled by AI and Public–Private Partnerships
Madhulika Guhathakurta, Ames Research Center, Mountain View, CA
Recorded Presentation

3:30 PM-4:30 PM: Thursday, 16 January 2020

Recording files available
Session 11B
Tropical Cyclone Analysis and Prediction with Machine Learning. Part II
Location: 156A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
CoChair: Philippe E. Tissot, Texas A&M University-Corpus Christi
3:30 PM
11B.1
A Tropical Cyclone Similarity Search Algorithm Based on Deep Learning Method
Yu Wang, China Meteorological Administration, Beijing, China; and L. Han
Recorded Presentation

3:45 PM
11B.2
A Deep Neural Network to Globally Forecast the Track and Intensity of Tropical Cyclones
Hammad Usmani, Georgia Institute of Technology, Atlanta, GA; and A. Habibi and D. Habibi
Recorded Presentation

4:00 PM
11B.3 is now Poster 1367A

4:15 PM
11B.4
Predicting Hurricane Genesis and Evolution with Deep Learning
Tianle Yuan, JCET, Baltimore, MD; and M. G. Nida and H. Song
Recorded Presentation

3:30 PM-5:00 PM: Thursday, 16 January 2020

Recording files available
Session 11A
AI for Decision Support
Location: 156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs: Amanda Burke, CAPS/University of Oklahoma; Nicholas McCarthy, CAPS/University of Oklahoma
3:30 PM
11A.1
Natural Language Processing to Predict National Weather Service Products from Winter-Related Transportation Incidents
Louvere M. Walker-Hannon, MathWorks, Natick, MA; and C. L. Walker
Recorded Presentation

3:45 PM
11A.2
Machine Learning for Operational Weather
S. W. Miller, Raytheon Intelligence, Information and Services, Aurora, CO
Recorded Presentation

4:00 PM
11A.3
River Flood Prediction Using a Long Short-Term Memory Recurrent Neural Network
Andrew T. White, Univ. of Alabama in Huntsville, Huntsville, AL; and K. D. White, C. R. Hain, and J. L. Case
Recorded Presentation

4:15 PM
11A.4
Deep Learning to Improve Numerical Weather Prediction Cloud Forecasts
Billy D. Felton, Northrop Grumann Corporation, McLean, VA; and R. J. Alliss and M. Mason

4:30 PM
11A.5
Phenomena Portal for Machine Learning Applications in Earth Science
Brian Freitag, Univ. of Alabama in Huntsville, Huntsville, AL; and A. Acharya, M. Ramasubramanian, D. Bollinger, A. Kaulfus, I. Gurung, M. Maskey, and R. Ramachandran
Recorded Presentation

4:45 PM
11A.6
Probabilistic Forecast of Thunderstorms Using Artificial Neural Networks with Google Keras Libraries for Deep Learning
Mamoudou Bocar Ba, NOAA/NWS/STI, Silver Spring, MD
Recorded Presentation

Handout (1.4 MB)

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