Session 12 19th Conference on Artificial Intelligence for Environmental Science

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

« Collapse View
- Indicates paper has been withdrawn from meeting
- Indicates paper is an Award Winner

Monday, 13 January 2020

Monday | Tuesday | Wednesday | Thursday | top

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


Joint Session 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; and the Eighth Symposium on Building a Weather-Ready Nation: Enhancing Our Nation's Readiness, Responsiveness, and Resilience to High Impact Weather Events )

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


AM Coffee Break (Monday)

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


Joint Session 3
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, NCAR; Scott Collis, Argonne National Laboratory
10:30 AM
J3.1
How Python Can Help us to Create the Physical Data Scientists of the Future (Core Science Keynote)
Amy McGovern, University of Oklahoma, Norman, OK

11:00 AM
J3.2
Cloud Nowcasting on Satellite Images: A Novel Dataset and Experimental Comparisons
Andreas Holm Nielsen, Aarhus University, Aarhus C, Denmark; and A. Wagner, A. Iosifidis, and H. Karstoft

11:15 AM
J3.3
A Deep Neural Network Deriving Cloud Properties from Satellite Remote Sensing
thomas rink, University of Wisconsin - Madison, Madison, WI; and A. Wimmers

11:30 AM
J3.4
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

11:45 AM
J3.5
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

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


Session 1A
AI for Environmental Science I
Location: 156A (Boston Convention and Exhibition Center)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Carlos F. Gaitan, Arable Labs, Inc.; Zhonghua Zheng, University of Illinois at Urbana−Champaign
11:00 AM
.1
Lessons from downscaling precipitation in Tasmania, Australia and northeast USA using machine learning approaches
Timothy Lynar, University of New south wales, Campbell, ACT, Australia; and C. D. Watson

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

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

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


Session 1B
AI for Environmental Science II
Location: 156BC (Boston Convention and Exhibition Center)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Chair: Auroop R. Ganguly, Northeastern Univ.
11:00 AM
.1
Convection Forecast Enhanced by the Deep Learning of Radar Observation and Numerical Prediction
Leiming Ma, Shanghai Central Meteorological Observatory, Shanghai, China

11:15 AM
.2
Smartphone Pressure Analysis with Machine Learning and Kriging
Conor McNicholas, University of Washington, Seattle, WA

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

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

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


Session 2A
Applications of Machine Learning in Earth System Modeling
Location: 156BC (Boston Convention and Exhibition Center)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Christiane Jablonowski, University of Michigan; Christoph A. Keller, GMAO
2:00 PM
.1
Discovering Novel Eddy Parameterisations with Machine Learning
Laure Zanna, University of Oxford, Oxford, United Kingdom; and T. Bolton

2:15 PM
.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

2:30 PM
.3
Towards Physics-informed Deep Learning for Spatiotemporal Modeling of Turbulent Flows
Rui Wang, Northeastern University, Boston, MA; and A. Albert, K. Kashinath, M. Mustafa, and R. Yu

2:45 PM
.4
Deep learning for weather prediction: Forecasting globally-gridded 500-hPa geopotential heights on short- to medium-range time scales
Jonathan A. Weyn, University of Washington, Seattle, WA; and D. R. Durran and R. Caruana

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

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

3:30 PM
.7
Developing the Snow Cover Fraction Schemes for land surface model using Machine Learning Approach
Yuan-Heng Wang, Univ. of Arizona, Tucson, AZ; and H. V. Gupta, P. D. Broxton, Y. Fang, A. Behrangi, X. Zeng, and G. Y. Niu

3:45 PM
.8
A machine learning-based parameterization of OH
M. B. Follette-Cook, Morgan State Univ./GESTAR, Greenbelt, MD; and J. M. Nicely, C. A. Keller, and B. Duncan


Session 2B
Deep Learning Applications for Environmental Science I
Location: 156A (Boston Convention and Exhibition Center)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Tianle Yuan, GSFC; Sarvesh Garimella, ACME AtronOmatic, LLC
2:00 PM
.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

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

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

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

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

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

3:30 PM
.7
Downscaling Numerical Weather Models with GANs
Alok Singh, Terrafuse, Berkeley, CA; and B. White and A. Albert

3:45 PM
.8
Fine-scale surface climate data with deep learning
Thomas C. M. Martin, University of São Paulo (USP), 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

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


Formal Poster Viewing Reception (Mon)

Poster Session 1
AI for Environmental Science Poster Session I
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: John K. Williams, The Weather Company, An IBM Business; Zhonghua Zheng, University of Illinois at Urbana−Champaign
U.S. Water Prices: a Machine Learning Approach
Quinn McColly, Texas A&M University-Corpus Christi, Corpus Christi, TX; and P. Tissot and D. Yoskowitz

Gradient-based optimization to reduce uncertainty in radar rainfall estimates using deep learning techniques and in situ measurements from disdrometers
Haonan Chen, Colorado State Univ. and NOAA/Earth System Research Laboratory, Fort Collins, CO; and R. Cifelli and V. Chandrasekar

Available air channel capacity prediction by weather-capacity Graphical Neural Network(wcGNN)
Yao Xiao, Shanghai Em-Data Technology Co., Ltd, Shanghai, China; and J. Hang, H. Zuo, X. Guo, Z. Yan, and C. Lu

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

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

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. huang, R. biswas, and E. Xia

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

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

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

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

Comparison of shallow and deep neural network water temperature predictions for resource management during cold stunning events
Jensen DeGrande, Texas A&M University-Corpus Christi, Corpus Christi, TX; and P. Tissot, J. Wiliams, H. Kamangir, N. Durham, and S. Bates

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

Seasonal Hurricane Forecasting Using Machine Learning
Timothy Hall, Association of Certified Meteorologists (ACM), Walkersville, MD; and K. Hall

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

Tuesday, 14 January 2020

Monday | Tuesday | Wednesday | Thursday | top

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


Session 3A
AI Applied to Airborne or Spaceborne Earth Observation Datasets
Location: 156BC (Boston Convention and Exhibition Center)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: James M. Kurdzo, MIT Lincoln Laboratory; Sid Boukabara, NOAA/NESDIS
8:30 AM
.1
NN Technique for Producing Consistent Ocean Color Data for Assimilation in Ocean Models
Vladimir Krasnopolsky Krasnopolsky, NOAA, College Park, MD

8:45 AM
.2
Machine Learning for inpainting QuikSCAT winds in Hawaii's Lee Region
William Chapman, 9500 Gilman Dr., La Jolla, CA; SIO, La Jolla, CA; SIO, La Jolla, CA; and T. J. Kilpatrick

9:00 AM
.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

9:15 AM
.4
The optimal single-scattering properties for retrieving ice cloud properties based on machine learning techniques
Yi Wang, Texas A&M University, College Station, TX; and P. Yang and Y. Huang

9:30 AM
.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
.6
Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data
Thomas Vandal, NASA / BAERI, Mountain View, CA; and R. Nemani


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

8:45 AM
.2
Regional High Impact Hail Forecasting using Random Forests
Amanda Burke, CAPS/University of Oklahoma, Norman, OK; and N. Snook and A. McGovern

9:00 AM
.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

9:15 AM
.4
Using machine learning to improve storm-scale 1-h probabilistic forecasts of severe weather
Montgomery L. Flora, University of Oklahoma, CIMMS, NSSL/NOAA, Norman, OK; and C. Potvin, P. Skinner, and A. McGovern

9:30 AM
.5
Local Severe Storm Warning in Pre-convection Stage with High Temporal Resolution Measurements from Advanced Baseline Imager onboard GOES-R series
Zheng Ma, CIMSS/Univ. of Wisconsin, Madison, WI; and Z. Li, J. Li, and J. Sun

9:45 AM
.6
Multi-prior LSTM (mpLSTM): predicting visibility with uncertainties from complex background states
Yao xiao, Shanghai Em-Data Technology Co., Ltd, Shanghai, China; and F. Qi, Y. Meng, H. Zuo, X. Guo, Z. Yan, and C. Lu

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


AM Coffee Break (Tuesday)

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


Session 4
AI Applications for the Detection of Earth Science Phenomena
Location: 156A (Boston Convention and Exhibition Center)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Christina Kumler, NOAA/ESRL and CIRES; Sid Boukabara, NOAA/NESDIS; Aaron Kaulfus, Univ. of Alabama
10:30 AM
.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

10:45 AM
.2
Rapid hail stone characterization: a 3d computer vision shape analysis model.
Stan Biryukov, Understory Weather, Madison, WI; and K. Jero, A. Kubicek, E. Hewitt, and J. Leonard

11:00 AM
.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
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

11:30 AM
.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

11:45 AM
.6
Analysis and Application of Mesoscale Radar Scenes During Severe Weather Events
Alex M. Haberlie, Louisiana State University, Baton Rouge, LA; and W. S. Ashley, V. A. Gensini, and M. Karpinski


Joint Session 16
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; and the 26th Conference on Probability and Statistics )
Cochairs: Auroop Ganguly, Northeastern Univ.; Karthik Kashinath, LBNL
10:30 AM
J16.1
Viewing Climate Signals through an AI Lens (Core Science Keynote)
Elizabeth A. Barnes, Colorado State University, Fort Collins, CO; and I. Ebert-Uphoff, J. Hurrell, C. W. Anderson, and D. Anderson

11:00 AM
J16.2
Evaluation of Data-Driven Causality Discovery Methods among Dominant Climate Modes
Steve R Hussung, Indiana University, Bloomington, Bloomington, IN; and S. Mahmud, A. Sampath, M. Wu, P. Guo, and J. Wang

11:15 AM
J16.3
Deep Learning Semantic Segmentation for Climate Change Precipitation Analysis
Andrew Lou, LBNL, Berkeley, CA; University of California Berkeley, Berkeley, CA; and E. Chandran, M. Prabhat, J. Biard, K. Kunkel, M. F. Wehner, and K. Kashinath

11:30 AM
J16.4
The Future of Severe Thunderstorms in the U.S. - Insights from Combining Deep Learning and High-Resolution Modeling
Maria J Molina, NCAR, Boulder, CO; and D. J. Gagne II and A. F. Prein

11:45 AM
J16.5
Downscaling Climate Model Data for Energy and Crop Modelling Using Self-Organizing Maps
Andrew Polasky, The Pennsylvania State Univ., University Park, PA; and J. L. Evans and J. Fuentes

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


Lunch Break (Tuesday)

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


Session 5A
AI for Environmental Science III
Host: 19th Conference on Artificial Intelligence for Environmental Science
CoChair: Carlos F. Gaitan, Benchmark Labs
1:30 PM
.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

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

2:00 PM
.3
Regionalisation of Aquifer Properties through Machine Learning in the Lake Chad Basin
Maximilian Nölscher, BGR = German Federal Institute for Geosciences and Natural Resources, Berlin, Germany; and M. Rückl and S. Broda

2:15 PM
.4
Using machine learning to predict complete winter ice cover of a freshwater lake
Campbell D. Watson, IBM Thomas J. Watson Research Center, Yorktown Heights, NY; and G. Auger, M. Tewari, and L. A. Treinish


Session 5B
Environet
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Karthik Kashinath, LBNL; Karthik mukkavilli, Environet
1:30 PM
.1
Environet: A Project Update
Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila), Montreal, QC, Canada; McGill University, Montreal, QC, Canada

1:45 PM
.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
.3
Community Earth System Science Datasets from NCAR
David John Gagne II, NCAR, Boulder, CO; and R. D. Loft and N. Flyer

2:15 PM
.4
IceNet: A large-scale dataset for tracking ice flow using unsupervised learning with adversarial networks
Yimeng Min, Montreal Institute for Learning Algorithms (Mila), Montreal, QC, Canada; and S. K. Mukkavilli and Y. Bengio


Joint Session 20
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, Colorado School of Mines
1:30 PM
J20.1
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

1:45 PM
J20.2
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.
Aaron Johnson, Univ. of Oklahoma, Norman, OK; and X. Wang

2:00 PM
J20.3
The long-term frontal system variation for future climate projections with machine learning weather classifier
Shih-Hao Su, Chinese Culture University, Taipei, Taiwan; and T. S. Yo, C. W. Chang, Y. C. Yu, and J. L. Chu

2:15 PM
J20.4
Statistical-Physical Microphysics Parameterization Schemes: A Proposed Framework for Physically-Based Microphysics Schemes that Learn from Observations
Marcus van Lier-Walqui, Columbia University & NASA/GISS, New York, NY; and H. Morrison, M. R. Kumjian, K. J. Reimel, O. P. Prat, S. Lunderman, and M. Morzfeld

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


PM Coffee Break (Tuesday)

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


Session 6
History of AI in Environmental Science (Centennial)
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Philippe Tissot, Texas A&M University−Corpus Christi; Sue Ellen Haupt, NCAR
3:00 PM
.1
The History of AI in the Environmental Sciences (Core Science Keynote)
Sue Ellen Haupt, NCAR, Boulder, CO

3:30 PM
.2
AI Applications to the Earth Sciences: 35 years through the lens of the AMS AI Committee
Philippe E. Tissot, Texas A&M University-Corpus Christi, CORPUS CHRISTI, TX

3:45 PM
Panel Discussion


Joint Session 28
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 Inc.
3:00 PM
J28.1
AI for Earth: Building an Extensible, Distributed API Pipeline Platform for Artificial Intelligence
Patrick Flickinger, Microsoft, Redmond, WA

3:15 PM
J28.2
Lightning Prediction for Space Launch Using Machine Learning Based on Electric Field Mills and Lighting Detection and Ranging Data.
Anson Cheng, AFIT = Air Force Institute of Technology, Wright-Patterson AFB, OH; and A. J. Geyer

3:30 PM
J28.3
Predicting Weather Conditions Utilizing Artificial Neural Networks for C-17 Mission Planning
Garrett A Alarcon, AFIT = Air Force Institute of Technology, Wright-Patterson AFB, OH; and A. J. Geyer

3:45 PM
J28.4
Artificial Intelligence Based Ensemble Modeling for Correction of GPM IMERG Precipitation Product over the Brahmaputra River Basin
Md ABUL EHSAN Bhuiyan, University 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)

Wednesday, 15 January 2020

Monday | Tuesday | Wednesday | Thursday | top

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


Session 7A
AI in Radar Observations
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Sarvesh Garimella, ACME AtronOmatic, LLC; Alex M. Haberlie, Louisiana State University
8:30 AM
.1
An AI Approach for Generating Instantaneous Rain Rates from Volumetric Radar Scans
Sarvesh Garimella, ACME AtronOmatic, LLC, Portland, OR

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

9:00 AM
.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

9:15 AM
.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

9:30 AM
.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

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


Session 7B
Deep Learning Applications for Environmental Science II
Host: 19th Conference on Artificial Intelligence for Environmental Science
Chair: Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila)
8:30 AM
.1
Multi-source Data Integration under a Deep Learning Framework to Improve Streamflow Forecast Ability
Dapeng Feng, Pennsylvania State Univ., Univ. Park, PA; and C. Shen and K. Fang

8:45 AM
.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
.3
A comparison of Deep Learning, Shallow Neural Network, and Principal Component Analysis based approaches to Thunderstorm Prediction
Hamid Kamangir, Texas A&M University-Corpus Christi, Corpus Christi, TX; and P. E. Tissot, W. G. Collins, and S. A. King

9:15 AM
.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

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


Joint Session 35
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 University - Corpus Christi
8:30 AM
J35.1
Multi-resolution Cluster Analysis - Addressing Trust in Climate Classification
Derek DeSantis, LANL, Los Alamos, NM; and P. Wolfram and B. Alexandrov

8:45 AM
J35.2
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

9:00 AM
J35.3
Selected Methods from Explainable AI to Improve Understanding of Neural Network Reasoning for Environmental Science Applications
Imme Ebert-Uphoff, CIRA - Colorado State University, Fort Collins, CO; and K. Hilburn, B. A. Toms, and E. A. Barnes

9:15 AM
J35.4
Emulation of Bin Microphysical Processes with Machine Learning
David John Gagne II, NCAR, Boulder, CO; and C. C. Chen and A. Gettelman

9:30 AM
J35.5
Using Physically Interpretable Neural Networks to Discover Modes of Climate and Weather Variability
Benjamin A. Toms, Colorado State University, Fort Collins, CO; and E. A. Barnes and I. Ebert-Uphoff

9:45 AM
J35.6
Lessons Learned Using ML For Knowledge Discovery In the Atmospheric Sciences
Amy McGovern, University of Oklahoma, Norman, OK

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


AM Coffee Break (Wednesday)

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


Session 8
AI for Environmental Science IV
Host: 19th Conference on Artificial Intelligence for Environmental Science
CoChair: Auroop R. Ganguly, Northeastern Univ.
10:30 AM
.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

10:45 AM
.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

11:00 AM
.3
Wind Variability Analysis for the Kuwait Region Using Self-Organizing Maps
Steven M. Naegele, Pennsylvania State University, University Park, PA; NCAR, Boulder, CO; and T. C. McCandless, S. E. Haupt, G. S. Young, and S. J. Greybush

11:15 AM
.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 University, College Station, TX; and I. Szunyogh, B. Hunt, and E. Ott

11:30 AM
.5
Short term hail prediction system based on numerical weather model and machine learning
CHANDRASEKAR RADHAKRISHNAN, Colorado State University, Fort Collins, CO; and V. Chandrasekar, A. Kubicek, J. krzak, and E. Hewitt

11:45 AM
.6
Development of 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


Joint Session
Tropical Cyclone Analysis and Prediction with Machine Learning I
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Tropical Meteorology and Tropical Cyclones Symposium )
Cochairs: Jebb Stewart, NOAA/ESRL, Boulder and CIRA/Colorado State Univ.; Eric D. Loken, CIMMS/University of Oklahoma
10:30 AM
6.1
Using Geostationary Imagery to Peer through the Clouds Revealing Hurricane Structure
C. J. Slocum, CIRA, Fort Collins, CO; and J. Knaff

10:45 AM
6.2
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

11:00 AM
6.3
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

11:15 AM
6.4
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

11:30 AM
6.5
Using Evolutionary Programming to Generate Improved Tropical Cyclone Intensity Forecasts
Jesse Schaffer, University of Wisconsin−Milwaukee, Milwaukee, WI; and P. Roebber and C. Evans

11:45 AM
6.6
An Updated Atlantic Basin Tropical Cyclone Rapid Intensification Scheme Using Machine Learning and Operational Forecast Data
Andrew Mercer, Mississippi State University, Mississippi State, MS; and A. D. Grimes and K. M. Wood

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


Lunch Break (Wednesday)

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


Session 9A
AI Applications for Air Quality
Host: 19th Conference on Artificial Intelligence for Environmental Science
CoChair: Surya Karthik Mukkavilli, Montreal Institute for Learning Algorithms (Mila)
1:30 PM
.1
PMNet: Improving Aerosol Predictions using Deep Neural Nets for Limited Ground Stations
Caleb Hoyne, McGill University, Montreal, QC, Canada; and S. K. Mukkavilli and D. Meger

1:45 PM
.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
.3
Using a Feedforward MLP Neural Network to Fill Gaps in N2O Emission Data
Benjamin Matthew Fehr, University of New Hampshire, Durham, NH; and C. Dorich and R. Conant

2:15 PM
.4
Satellite-derived PM2.5 concentrations over South Korea using GOCI aerosol product and a machine learning method
Yeseul Cho, Yonsei University, Seoul, Korea, Republic of (South); and J. Kim, H. Lee, M. Choi, S. Lee, H. Lim, and J. Im


Session 9B
Machine Learning for Subseasonal to Seasonal Prediction
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Carlos F. Gaitan, Arable Labs, Inc.; Maria J. Molina, NCAR
1:30 PM
.1
Applying Machine Learning to Improve Subseasonal to Seasonal (S2S) Forecasts
Soukayna Mouatadid, University of Toronto, Toronto, ON, Canada; and J. Cohen and L. Mackey

1:45 PM
.2
Using Machine Learning to Improve Sub-Seasonal to Seasonal Prediction (S2S)
Richard Garmong, University of Georgia, Athens, GA; and R. Bolinger and R. S. Schumacher

2:00 PM
.3
Basin of Prediction for Seasonal Weather Forecasting using Self-Similar Power Transforms
M. Jeremie Lafitte (Levitas), Metivdata, Safed, Israel

2:15 PM
.4
Applications of Deep Learning to S2S Precipitation Prediction and Downscaling for the Middle East and North Africa
Hamada S. Badr, Johns Hopkins Univ., Baltimore, MD; and K. Bergaoui, B. F. Zaitchik, A. Hazra, A. McNally, C. D. Peters-Lidard, and R. McDonnell


Joint Session 43
Big Data, Big Computing, Bigger Science: High-Performance Computing enabled Artificial Intelligence
Location: 155 (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, Texas Tech Univ.; David John Gagne II, NCAR
1:30 PM
J43.1
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

1:45 PM
J43.2
Towards 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

2:00 PM
J43.3
Assessing Changes in Tropical Cyclone Genesis Under Varying Climate Scenarios
Arturo Fernandez, Univ. of California, Berkeley, CA; Uber Technologies, Inc., San Francisco, CA; and K. Kashinath, J. McAuliffe, D. Nolan, C. M. Patricola, M. Prabhat, and M. F. Wehner

2:15 PM
J43.4
Meteorological Event Identification Using National Weather Service Forecast Discussions
Brian Freitag, Univ. of Alabama, Huntsville, AL; and K. Bugbee, J. Miller, J. Zhang, R. Ramachandran, and M. Maskey

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


PM Coffee Break (Wednesday)

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


Session 10
The Future of AI in Environmental Science
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: David John Gagne II, NCAR; Amy McGovern, University of Oklahoma; Carlos F. Gaitan, Arable Labs, Inc.
3:00 PM
.1
AI2ES: Alpha-Institute: Artificial Intelligence for Environmental Sciences
Amy McGovern, University of Oklahoma, Norman, OK; and J. Hickey, D. Hall, I. Ebert-Uphoff, C. Thorncroft, J. Williams, R. J. Trapp, R. He, and C. Bromberg

3:15 PM
.2
Building a cross-disciplinary network to tackle climate change with machine learning
Kelly Kochanski, University of Colorado Boulder, Boulder, CO; and D. Rolnick, P. Donti, and L. Kaack

3:30 PM
.3
NOAA’s Artificial Intelligence (AI) Strategy
Timothy Gallaudet, NOAA, Washington, DC; and W. L. Michaels, S. Boukabara, V. M. Krasnopolsky, 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

3:45 PM
Panel Discussion


Joint Session 47
Artificial Intelligence Applications in the Coastal Environment
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the 18th Symposium on the Coastal Environment )
Cochairs: Philipe Tissot, Texas A&M University-Corpus Christi; Michael J. Starek, Texas A&M University−Corpus Christi
3:00 PM
J47.1
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

3:15 PM
J47.2
Applications of Artificial Neural Network in Predicting Water Quality Indicators: Case Studies from Korean Coastal Waters
Jongseong Ryu, Anyang University, Ganghwa-gun, Korea, Republic of (South); and Y. H. Kim, H. C. Kim, S. Son, and M. Lee

3:30 PM
J47.3
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

3:45 PM
J47.4
Suggesting an Efficient Deep Learning Architecture for Coastal Wetland Land Cover Mapping with UAS Imagery
Mohammad Pashaei, Texas A&M University-Corpus Christi, Corpus Christi, TX; and H. Kamangir, M. J. Starek, P. Tissot, and S. A. King

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


Formal Poster Viewing Reception (Wed)

Poster Session 2
AI for Environmental Science Poster Session II
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: John K. Williams, The Weather Company; Zhonghua Zheng, University of Illinois at Urbana−Champaign; Maria J Molina, NCAR
Projected changes in summertime circulation patterns imply increased drought risk for the South-Central U.S.
Jung-Hee Ryu, Texas Tech Univ., Lubbock, TX; and K. Hayhoe and S. L. Kang

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

Improved forecasts of incoming solar radiation using machine learning and ensemble weather model output
Sarah-Ellen Calise, Northern Vermont University-Lyndon, Lyndonville, VT; and D. M. Siuta

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

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

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

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

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

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

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

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

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

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

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

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

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

Thursday, 16 January 2020

Monday | Tuesday | Wednesday | Thursday | top

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


Joint Session 55
Incorporating Data Science and Machine Learning into Atmospheric Science Education
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: David John Gagne II, NCAR; Dorit Hammerling, Colorado School of Mines
8:30 AM
J55.1
Atmospheric Sciences + Data Science at the University of Illinois Urbana-Champaign
Anna E. Nesbitt, University of Illinois Urbana-Champaign, Urbana, IL; and S. W. Nesbitt, B. F. Jewett, R. L. Sriver, S. Lasher-Trapp, R. J. Trapp, and R. M. Rauber

8:45 AM
J55.2
Client-driven, University Student Capstone Project in Environmental Machine Learning
Timothy J. Hall, The Aerospace Corporation, Greenbelt, MD; and E. B. Wendoloski

9:00 AM
J55.3
Practical AI in the classroom
Jianghao Wang, MathWorks, Natick, MA

9:15 AM
J55.4
Mining Students' Digital Behaviors in Class to Create an Earlier Warning System of Student Success
Perry J. Samson, Univ. of Michigan, Ann Arbor, MI


Joint Session
Societal and Economic Impacts of AI
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the 15th Symposium on Societal Applications: Policy, Research and Practice )
Cochairs: Daniel Rothenberg, ClimaCell Inc.; Tyler C. McCandless, NCAR
8:30 AM
.1
From decision support to decision services: an expanded role for AI in the weather enterprise
John K. Williams, The Weather Company, an IBM Business, Andover, MA; and P. Neilley

8:45 AM
.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

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

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

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


Exhibit Hall Breakfast

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


Joint Session 59
Machine Learning Applications in the Energy Sector
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the 11th Conference on Weather, Climate, and the New Energy Economy )
Cochairs: Tyler C. McCandless, NCAR; Sue Ellen Haupt, NCAR
10:30 AM
J59.1
Machine and Deep Learning Methods for Fault Detection and Classification in Photovoltaic Modules
Warren James Brettenny, Nelson Mandela University, Port Elizabeth, South Africa; and C. W. Dunderdale, C. M. Clohessy, E. E. van Dyk, and G. D. Sharp

10:45 AM
J59.2
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

11:00 AM
J59.3
Performance of Alternate Machine Learning Configurations for the 0-120 Hour Prediction of Extreme Wind Gusts for Outage Management in the Consolidated Edison Company of New York Service Area
John W. Zack, MESO, Inc., Troy, NY; and J. M. Freedman, M. Berlinger, and C. Cheng

11:15 AM
J59.4
Comparing Implicit vs 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

11:30 AM
J59.5
Optimizing Training Windows for Wind and Solar Generation Forecasting
Daniel B. Kirk-Davidoff, UL, Albany, MD; and P. Tardaguila and T. Melino

11:45 AM
J59.6
A Deep Learning Framework for Forecasting Power in a Full-Scale Wind Farm
Rajitha Meka, University of Texas at San Antonio, San Antonio, TX; and K. Bhaganagar and A. Alaeddini


Joint Session 60
Machine Learning for Subgrid Parameterization in Weather and Climate Models
Hosts: (Joint between 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: Ryan A. Lagerquist, CIMMS; Christiane Jablonowski, University of Michigan; Carlos F. Gaitan, Arable Labs, Inc.
10:30 AM
J60.1
Building a Hierarchy of Hybrid, Neural-network Parametrizations of Convection
Tom Beucler, UCI, Irvine, CA; Columbia University, New York, CA; and P. Gentine, M. S. Pritchard, S. Rasp, and V. Eyring

10:45 AM
J60.2
Data-driven super-parameterization using deep learning: Experimentations with a multi-scale Lorenz 96 model
Pedram Hassanzadeh, Rice University, 6100 Main St., Houston, TX; and A. Chattopadhyay, A. Subel, and K. Palem

11:00 AM
J60.3
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

11:15 AM
J60.4
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

11:30 AM
J60.5
Towards sub-grid scale parameterizations using a super-resolution generative adversarial network
Eden Au, University of Edinburgh, Edinburgh, United Kingdom; and K. Kashinath, A. Albert, M. Prabhat, and S. F. B. Tett

11:45 AM
J60.6
Utilizing Machine Learning to Replace Physical Parameterization Schemes: How do Different Techniques Compare?
Garrett Limon, University of Michigan, Ann Arbor, MI; and C. Jablonowski

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


Joint Session 62
Advances in the Use of Artificial Intelligence Techniques in Support of Aviation, Range, and Aerospace Meteorology
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the 20th Conference on Aviation, Range, and Aerospace Meteorology )
Cochairs: Haig Iskendarian, MIT; James M. Kurdzo, MIT Lincoln Laboratory
1:30 PM
J62.1
Using a neural network to predict future radar frames
Claire Sheila Bartholomew, Met Office, Exeter, UK; University of Leeds, Leeds, United Kingdom; and D. Hogg, J. H. Marsham, and T. Howard

1:45 PM
J62.2
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

2:00 PM
J62.3
Global Synthetic Weather Radar in AWS GovCloud for the US 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

2:15 PM
J62.4
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
J62.5
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

2:45 PM
J62.6
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


Joint Session 63
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, National Center for Atmospheric Research
1:30 PM
J63.1
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

1:45 PM
J63.2
Machine Learning Classification of Interplanetary Coronal Mass Ejections using Satellite Accelerometers
Kelsey Doerksen, Univ. of Western Ontario, London, ON, Canada

2:00 PM
J63.3
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

2:15 PM
J63.4
Tracking Equatorial Plasma Bubbles based on an Artificial Neural Network Classifier
David DeBonis, AER, Albuquerque, NM; and B. Drummond

2:30 PM
J63.5
Leveraging Topological Data Analysis and Deep Learning for Solar Flare Prediction
Thomas Berger, University of Colorado at Boulder, Boulder, CO; and V. Deshmukh, E. Bradley, J. Meiss, and N. Nishizuka

2:45 PM
J63.6
Emerging Frontiers in Science and Exploration Enabled by AI and Public-Private Partnerships
Madhulika guhathakurta, Ames Research Center, Mountain View, CA

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


Session 19
Tropical Cyclone Analysis and Prediction with Machine Learning II
Host: 19th Conference on Artificial Intelligence for Environmental Science
CoChair: Philippe E. Tissot, Texas A&M University-Corpus Christi
3:30 PM
.1
A Tropical Cyclone Similarity Search Algorithm Based on Deep Learning Method
Yu Wang, China Meteorological Administration, Beijing, China; and L. Han

3:45 PM
.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

4:00 PM
.3
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

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

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


Session 12
AI for Decision Support
Host: 19th Conference on Artificial Intelligence for Environmental Science
Cochairs: Amanda Burke, CAPS/University of Oklahoma; Nicholas McCarthy, OneConcern
3:30 PM
.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

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

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

4:15 PM
.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
.5
Phenomena Portal for Machine Learning Applications in Earth Science
Brian Freitag, Univ. of Alabama, Huntsville, AL; and A. Acharya, M. Ramasubramanian, D. Bollinger, A. Kaulfus, I. Gurung, M. Maskey, and R. Ramachandran

4:45 PM
.6
Probabilistic forecast of thunderstorms using Artificial Neural Network with Google Keras libraries for deep learning
Mamoudou Bocar Ba, NOAA/NWS/STI, Silver Spring, MD

« Collapse View