Program: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences (99th American Meteorological Society Annual Meeting)

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

Reviewers: Auroop Ganguly , Northeastern University ; Timothy S. Sliwinski , Texas Tech University ; Scott Collis , Argonne National Laboratory ; Gerry Creager , Oklahoma Univ./CIMMS, and NOAA/NSSL ; Philippe Tissot , Texas A&M University−Corpus Christi ; Amy McGovern , University of Oklahoma ; David John Gagne II , NCAR ; Christiane Jablonowski , University of Michigan ; Ryan A. Lagerquist , CIMMS ; Edwin Campos , Uptake Technologies ; Michael J. Starek , Texas A&M University−Corpus Christi ; Chuyen Nguyen , Texas A&M University−Corpus Christi ; Andre Van der Westhuysen , NOAA ; Daniel Rothenberg , ClimaCell Inc. ; Sarvesh Garimella , ACME AtronOmatic, LLC ; Elizabeth A. Satterfield , NRL ; William W. Hsieh , Univ. of British Columbia ; John K. Williams , The Weather Company, An IBM Business ; Christine Card , AMS ; Karthik Mukkavilli , Unknown ; Carlos Gaitan , ClimateAI ; Carlos Gaitan , ClimateAI

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Sunday, 6 January 2019

4:00 PM-5:00 PM: Sunday, 6 January 2019

Plenary Session

Presidential Forum: Building Resilience to Extreme Political Weather: Advice for Unpredictable Times

Location: North Ballroom 120CD (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the Presidential Forum and Annual Meeting Welcome; the Seventh Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 16th Conference on Space Weather; the 17th History Symposium; the 20th Conference on Middle Atmosphere; the 22nd Atmospheric Science Librarians International Conference; the 24th Conference on Applied Climatology; the 28th Symposium on Education; the 35th Conference on Environmental Information Processing Technologies; the 23rd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS); the 17th Symposium on the Coastal Environment; the 10th Conference on Weather, Climate, and the New Energy Economy; the Ninth Symposium on Advances in Modeling and Analysis Using Python; the Seventh Symposium on Building a Weather-Ready Nation; the Fifth Symposium on High Performance Computing for Weather, Water, and Climate; the Phased Array Radar Symposium; the Special Symposium on Meteorological Observations and Instrumentation; the Ninth Conference on the Meteorological Application of Lightning Data; the Ninth Symposium on Lidar Atmospheric Applications; the 10th Conference on Environment and Health; the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; the 19th Conference on Aviation, Range, and Aerospace Meteorology; the 32nd Conference on Climate Variability and Change; the 33rd Conference on Hydrology; the Richard Anthes Symposium; the Special Symposium on Mesoscale Meteorological Extremes: Understanding, Prediction, and Projection; the Second Conference on Earth Observing SmallSats; the Seventh AMS Symposium on the Joint Center for Satellite Data Assimilation; the Ninth Conference on Transition of Research to Operations; the Seventh Symposium on the Weather, Water, and Climate Enterprise; the 11th Symposium on Aerosol–Cloud–Climate Interactions; the 14th Symposium on Societal Applications: Policy, Research and Practice; the 21st Conference on Atmospheric Chemistry; the Fourth Symposium on US-International Partnerships; and the Events )

Moderator: Michael Henry, American Institute of Physics

Presider: Roger Wakimoto, Univ. of California, Los Angeles

Speakers: Dan Kahan, American Institute of Physics; David Goldston, MIT

4:00 PM

Presidential Forum: Building Resilience to Extreme Political Weather: Advice for Unpredictable Times

Monday, 7 January 2019

8:30 AM-10:00 AM: Monday, 7 January 2019

Session 1

AI Techniques Applied to Environmental Science

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Chair: Carlos F. Gaitan, ClimateAI

8:30 AM

1.1

Machine Learning Classification to Inform the Search Space Extension Variant for the Analog Ensemble Technique

Laura Clemente-Harding, Engineer Research and Development Center, Alexandria, VA; and W. Hu, G. S. Young, G. Cervone, S. Haupt, and L. Delle Monache

8:45 AM

1.2

Object-Based Deep Learning for Detection of Synoptic-Scale Atmospheric Fronts

Ryan A. Lagerquist, CIMMS, Norman, OK

Recorded Presentation

1.3

Ultra-fast machine learning solver for chemical kinetics in atmospheric models

9:00 AM

1.4

Lightning Nowcasting with Multisource Data: A Deep Learning Approach

Kanghui Zhou, National Meteorological Center of China, Beijing, China

Recorded Presentation

9:15 AM

1.5

Precipitation Classification Using the Lowest Tilt Radar Data with a Support Vector Machine Method

Yadong Wang, Southern Illinois University, Edwardsville, IL; and L. Tang

Recorded Presentation Manuscript (3.2 MB)

9:30 AM

1.6

Automatic Generation of Text Weather Forecast with Spatio-Temporal Aggregation

Lior Perez, Météo-France, Toulouse, France

Recorded Presentation

10:00 AM-10:30 AM: Monday, 7 January 2019

AM Coffee Break (Monday)

Location: Meeting room foyers (Phoenix Convention Center - West and North Buildings)

10:30 AM-12:00 PM: Monday, 7 January 2019

Session 2A

AI Techniques for Decision Support, Part I

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Chair: Edwin Campos, Uptake Technologies

10:30 AM

2A.1

Avoiding AI Autopilot

Paul Roebber, University of Wisconsin−Milwaukee, Milwaukee, WI

Recorded Presentation

10:45 AM

2A.2

Application of Machine Learning in NCAR’s Runway Friction and Closure Prediction System (RFCPS)

Ishita Srivastava, NCAR, Boulder, CO; and S. Linden, T. Brummet, G. Wiener, and W. Petzke

Recorded Presentation

11:00 AM

2A.3

Using Deep Learning with Polarimetric Radar Data to Predict Hail Size

Kiel L. Ortega, CIMMS/University of Oklahoma and NOAA/OAR/NSSL, Norman, OK; and K. L. Elmore and J. C. Snyder

Recorded Presentation

11:15 AM

2A.4

Applications of Biomimetic Image Processing to Storm Segmentation and Tracking

Patrick A. Campbell, University of Oklahoma/CIMMS and NOAA/NSSL, Norman, OK; and K. L. Ortega and T. M. Smith

Recorded Presentation

11:30 AM

2A.5

An AI-Based System for Objective Tropical Cyclone Intensity Estimation

Jeffrey Miller, University of Alabama in Huntsville, Huntsville, AL; and M. Maskey, R. Ramachandran, I. Gurung, B. Freitag, D. Bollinger, R. Mestre, D. Silva, A. L. Molthan, C. R. Hain, and D. J. Cecil

Recorded Presentation

11:45 AM

2A.6

How Forecast “Prototypes” Help Optimize Decisions (When Probability Forecasts Can’t)

John K. Williams, The Weather Company, An IBM Business, Andover, MA; and P. Neilley

Recorded Presentation

Session 2B

Deep Learning for Environmental Datasets

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: David John Gagne II, NCAR; Amy McGovern, University of Oklahoma; Ryan A. Lagerquist, CIMMS

10:30 AM

2B.1

Exascale Deep Learning for Climate Science

Mr Prabhat, LBNL, Berkeley, CA; and T. Kurth, S. Treichler, J. Romero, M. Mudigonda, A. Mahesh, T. A. O'Brien, M. Fatica, M. Houston, K. Kashinath, M. Matheson, M. Shankar, M. F. Wehner, and W. D. Collins

Recorded Presentation

11:00 AM

2B.2

Real-Time and Climatological Storm Classification through Deep Learning

G. Eli Jergensen, University of Oklahoma, Norman, OK; and A. McGovern, H. Obermeier, and T. Smith

Recorded Presentation

11:15 AM

2B.3

Using Deep Learning to Estimate Tropical Cyclone Intensity from 89-GHz Band Microwave Satellite Imagery

Anthony Wimmers, CIMSS/University of Wisconsin−Madison, Madison, WI; and C. Velden and J. H. Cossuth

11:30 AM

2B.4

A Deep Learning−Based Correction to Radar Beam Blockage

Katherine Avery, University of Oklahoma, Norman, OK; and M. S. Veillette, C. J. Mattioli, and H. Iskenderian

Recorded Presentation

11:45 AM

2B.5

Using Deep Learning for Advanced Remote Sensing of Clouds

Alexandria M. Russell, Northrop Grumann Corporation, McLean, VA; and M. Mason, B. D. Felton, and R. J. Alliss

Recorded Presentation

12:00 PM-1:30 PM: Monday, 7 January 2019

Lunch Break (Monday)

2:00 PM-2:45 PM: Monday, 7 January 2019

Session 3B

Deep Learning for Environmental Datasets, Part II

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: David John Gagne II, NCAR; Amy McGovern, University of Oklahoma

2:00 PM

3B.1

Development and Interpretation of Deep Learning Models for Nowcasting Convective Hazards

Ryan A. Lagerquist, CIMMS, Norman, OK; and A. McGovern, C. R. Homeyer, C. K. Potvin, T. Sandmael, and T. M. Smith

Recorded Presentation

2:15 PM

3B.2

Deep Neural Networks−Based Augmentation of Rainfall Data

Muhammed A. Sit, University of Iowa, Iowa City, IA; and B. C. Seo, W. F. Krajewski, and I. Demir

Recorded Presentation

2:30 PM

3B.3

Extreme Weather Event Prediction Using Bayesian Deep Neural Network

Orhun Aydin, ESRI, Redlands, CA

Recorded Presentation

2:00 PM-4:00 PM: Monday, 7 January 2019

Session 3A

AI Techniques for Extreme Weather and Risk Assessment

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Chair: Carlos F. Gaitan, ClimateAI

2:00 PM

3A.1

Application of Artificial Intelligence in Lightning Detection and Nowcasting Using Polarimetric RADAR Data

Yunish Shrestha, University Of Oklahoma, Norman, OK; and Y. Zhang, R. J. Doviak, and P. W. Chan

Recorded Presentation

2:15 PM

3A.2

Probabilistic Detection of Extreme Weather Using Deep Learning Methods

Ankur Mahesh, LBNL, Berkeley, CA; and T. A. O'Brien, W. D. Collins, M. Prabhat, K. Kashinath, and M. Mudigonda

Recorded Presentation

2:30 PM

3A.3

An Updated Machine Learning Ensemble for Atlantic Tropical Cyclone Rapid Intensification Forecasting

Andrew Edward Mercer, Mississippi State University, Mississippi State, MS; and A. D. Grimes and K. M. Wood

Recorded Presentation

2:45 PM

3A.4

The Application of a Clustering Algorithm to Identify and Measure Tornado Swarms

Jim Tang, Unaffiliated, Walnut Creek, CA

Recorded Presentation

3:00 PM

3A.5

Self-Organizing Maps for Tornadic Near-Storm Environments of the Southeastern United States

Alexandra Anderson-Frey, CIMMS, Norman, OK; and Y. P. Richardson, A. R. Dean, R. L. Thompson, and B. T. Smith

Recorded Presentation

3:15 PM

3A.6

Improved Estimation of Hail Damage Integrating Radar and Ground Truth Data

Stan Biryukov, Understory Weather, Madison, WI; and K. Jero, E. Hewitt, and A. Kubicek

Recorded Presentation

3:30 PM

3A.7

Coupling Convection-Allowing Model Guidance with Machine Learning for Day-Ahead Extreme Precipitation Outlooks

Gregory R. Herman, The Climate Corporation, Seattle, WA; and R. S. Schumacher

Recorded Presentation

3A.8

Short-Range Forecasting System of Extreme Weather Events

2:45 PM-4:00 PM: Monday, 7 January 2019

Session 3B

AI Applications to Air Quality

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Chair: Edwin Campos, Uptake Technologies

2:45 PM

3B.1

Application of Deep Neural Networks in Top-Down NOx Emissions Estimation

Hyun Cheol Kim, NOAA/OAR/ARL, College Park, MD; and B. Baker, T. Chai, C. Bae, S. You, B. U. Kim, and S. Kim

Recorded Presentation

3:00 PM

3B.2

Gradient Boosting Machine Learning to Improve Satellite-Derived Column Water Vapor with Implications for Estimating Ground-Level Fine Particulate Air Pollution

Allan C. Just, Icahn School of Medicine at Mount Sinai, New York, NY; and R. B. Chatfield, A. Lyapustin, Y. Wang, M. Sorek-Hamer, J. Rush, and I. Kloog

Recorded Presentation

3:15 PM

3B.3

Application of Machine Learning Algorithms for Short Term Forecasting of PM_2.5: A Novel Hybrid Transfer Learning Approach

Mohammad A Moghaddam, The University of Arizona, Tucson, AZ; and A. Sorooshian

Recorded Presentation

3:30 PM

3B.4

Short-Term Forecasting of PM_2.5 in High-Density City: A Spatio-Temporal Deep Learning Model Framework in Hong Kong

Meilan Wang, Hong Kong University of Science and Technology, Hong Kong, Hong Kong; and K. H. Lau

3:45 PM

3B.5

Improved Lidar Data Classification, Using State-of-the-Art Machine Learning Techniques

Ghazal Farhani, The University of Western Ontario, London, ON, Canada; and R. J. Sica, M. Daley, A. Haefele, and D. Cecil

Recorded Presentation

4:00 PM-6:00 PM: Monday, 7 January 2019

Formal Poster Viewing Reception

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

Joint Poster Session 1

Big Data, Big Computing, Bigger Science

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the Fifth Symposium on High Performance Computing for Weather, Water, and Climate; and the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences )

Chair: Timothy S. Sliwinski, Texas Tech University

324

Community Data Management Systems for CMIP6

Denis Nadeau, LLNL, Livermore, CA; and C. Doutriaux, D. Williams, and T. Reshel

325

An Update on the Collaborative REAnalysis Technical Environment (CREATE)

Gerald L. Potter, NASA GSFC, Greenbelt, MD; and L. Carriere, J. Hertz, J. Peters, T. P. Maxwell, S. Strong, J. Shute, Y. Shen, and D. Duffy

326

Analyzing Big Data Produced by Mesoscale-Domain Large-Eddy Simulation

Song-Lak Kang, Gangneung-Wonju National Univ., Gangneung, Korea, Republic of (South)

327

Developing a Machine Learning–Based Hail Climatology Using the SHAVE and MYRORSS Databases

Skylar S. Williams, Oklahoma Univ./CIMMS and NOAA/OAR/NSSL, Norman, OK; and K. L. Ortega

Handout (12.3 MB)

Poster Session

Artificial and Computational Intelligence and Its Applications to the Environmental Sciences Poster Session

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Chair: Philippe Tissot, Texas A&M University−Corpus Christi

176

One Problem: Two Methods? Factor Separation in the Atmospheric Sciences

Judah L. Cleveland, ORAU, Concord, NH; and J. A. Smith

Handout (1.4 MB)

177

Estimating Hourly PM_2.5Concentration from Satellite-Measured Top-of-Atmosphere Reflectance by Using a Machine Learning Algorithm

Jianjun Liu, Laboratory of Environmental Model and Data Optima, Laurel, MD; and F. Weng and Z. Li

Augmentation of Oceanographic Data with Deep Learning

178

A New Machine Learning−Based Cloud Phase Discrimination Algorithm Designed for Passive Infrared Satellite Sensors

Chenxi Wang, University Of Maryland, College Park, College Park, MD; and S. Platnick and K. Meyer

179

Radar Super-Resolution Using a Deep Convolutional Neural Network

Andrew Geiss, University of Washington, Seattle, WA; and J. C. Hardin

Handout (1.8 MB)

180

Estimating Tropical Cyclone Intensity in Passive Microwave Images Using Deep Learning Models

K. Ryder Fox, University of Miami, RSMAS, Miami, FL; and I. Gurung, J. J. Miller, M. Maskey, and A. L. Molthan

181

Toward an Enhanced Surface Classification Within MiRS Using a Deep Neural Network Approach

Ryan Honeyager, STAR, College Park, MD; and C. Grassotti, Y. K. Lee, S. Liu, and Q. Liu

182

Cloud Classification Using Artificial Intelligence

183

Using Machine Learning Techniques to Construct a Climatology of Mesoscale Convective Systems in the United States

Alex M. Haberlie, Louisiana State University, Baton Rouge, LA; and W. S. Ashley

184

Using Machine Learning to Classify Lightning for Earth Networks Total Lightning Network (ENTLN)

Saiadithya Cumbulam Thangaraj, Earth Networks, Germantown, MD; and M. Stock, J. Lapierre, M. Hoekzema, Y. Zhu, C. Schumann, R. Sonnenfeld, and L. C. Vidal

185

Atmospheric River Forecast Model Bias Correction

William Chapman, SIO, Brea, CA

186

Exploring the Use of Machine Learning to Develop a Predictive Model for Future Fire Seasons

Andrew T. White, University of Alabama in Huntsville, Huntsville, AL; and C. R. Hain, C. J. Schultz, J. L. Case, and K. D. White

187

Machine Learning to Predict Multi-Aerosol Mixing State Metrics

Zhonghua Zheng, University of Illinois at Urbana−Champaign, Urbana, IL; and V. G. Anantharaj, J. Gasparik, J. H. Curtis, Y. Yao, M. P. Hughes, D. Schmidt, M. West, and N. Riemer

188

Impacts of Different Background Error Samples Simulation Schemes on Numerical Forecast—Taking Typhoon "Soudelor" (1513) as an Example

Yehong Wang, Xiamen Meteorological Bureau, Xiamen, China

189

The Cloud-Based Future of ECMWF Data Services

Meghan Plumridge, ECMWF, Reading, United Kingdom

190

Spatio-Temporal Modeling for Regional Climate Model Evaluation: Eigenvector Filtering Versus Bayesian CAR

Meng Wang, Arizona State University, Tempe, AZ; and Y. Kamarianakis, M. Georgescu, and M. Moustaoui

191

Machine Learning and Big Data Analytics in Support of Fleet Safety during Severe Weather

Steve Wysmuller, IBM, Armonk, NY; and J. Traiteur and C. Reese

6:00 PM-8:00 PM: Monday, 7 January 2019

Exhibits Opening and Reception

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

Tuesday, 8 January 2019

8:30 AM-10:00 AM: Tuesday, 8 January 2019

Session 4A

AI Applied to Airborne or Spaceborne Earth Observation Datasets

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: Michael J. Starek, Texas A&M University−Corpus Christi; Chuyen Nguyen, Texas A&M University−Corpus Christi

8:30 AM

4A.1

Development of Merged Cloud Forecasts from Satellite and Numerical Model Data

Jason Nachamkin, NRL, Monterey, CA; and M. Surratt, R. L. Bankert, Y. Jin, E. Hyer, and D. A. Peterson

Recorded Presentation

8:45 AM

4A.2

NN Technique for Producing Consistent Ocean Color Data for Assimilation in Ocean Models

Vladimir Krasnopolsky, NOAA/NWS/NCEP, Camp Springs, MD

Recorded Presentation

9:00 AM

4A.3

Deep Learning Methods for Cyclogenesis, Extratropical, and Tropical Cyclone Regions of Interest (ROI) from Satellite Observations

Christina Bonfanti, NOAA/ESRL and CIRES, Boulder, CO; and J. Stewart, D. Hall, S. Maksimovic, M. W. Govett, L. Trailovic, and I. Jankov

Recorded Presentation

9:15 AM

4A.4

Sematic Segmentation of Hyperspatial UAS Imagery for Land Cover Mapping Using Convolutional-Wavelet Neural Networks

Mohammad Pashaei, Texas A&M University-Corpus Christi, corpus christi, TX; and M. J. Starek

Recorded Presentation

9:30 AM

4A.5

A Machine Learning-Trained ICVS VIIRS Clear-Sky Mask Algorithm Applicable for Multiple Satellites

Xingming Liang, ERT Inc., Laurel, MD; and B. Yan, N. Sun, A. Ignatov, and X. Zhou

Recorded Presentation

9:45 AM

4A.6

Finding Ship Tracks with Machine Learning Algorithms in Remote Sensing Images

Tianle Yuan, GSFC, Greenbelt, MD; and C. Wang, K. Meyer, and S. Platnick

Recorded Presentation

Session 4B

AI Techniques for Numerical Weather Prediction, Part I

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: Ryan A. Lagerquist, CIMMS; Edwin Campos, Uptake Technologies

8:30 AM

4B.1

Using Evolutionary Programming to Generate Improved Tropical Cyclone Intensity Forecasts

Jesse Schaffer, University of Wisconsin−Milwaukee, Milwaukee, WI; and P. Roebber and C. Evans

Recorded Presentation

8:45 AM

4B.2

An Overview of Hail Prediction Using Random Forests during the 2018 Hazardous Weather Testbed Spring Forecast Experiment

Nathan Snook, CAPS, Norman, OK; and D. J. Gagne II, A. Burke, and A. McGovern

Recorded Presentation

9:00 AM

4B.3

Real-Time Hail Prediction Using Machine Learning Algorithms and HREFv2

Amanda Burke, CAPS/University of Oklahoma, Norman, OK; and N. Snook, D. J. Gagne II, and A. McGovern

Recorded Presentation

9:15 AM

4B.4

Microclimate Hourly Downscaling Using Wireless Sensor and Online Sequential Extreme Learning Machines

Carlos F. Gaitan, ClimateAI, San Francisco, CA; and A. R. Lima

4B.5

Model-Free Forecasting of Weather Time Series Using Self-Similar Power Transforms

9:30 AM

4B.5A

Using Machine Learning to Improve Storm-Scale 1-h Probabilistic Low-Level Rotation Forecasts

Montgomery L. Flora, University of Oklahoma, CIMMS, NSSL/NOAA, Norman, OK; and C. K. Potvin, P. S. Skinner, and A. McGovern

Recorded Presentation

9:45 AM

4B.6

Object Processing of High-Resolution Precipitation Forecasts Based on Intensity and Texture Segmentation

Yamina Hamidi, Météo-France, Toulouse, France; and L. Raynaud, L. Rottner, and P. Arbogast

10:00 AM-10:30 AM: Tuesday, 8 January 2019

AM Coffee Break (Tuesday)

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

10:30 AM-11:45 AM: Tuesday, 8 January 2019

Session 5A

AI Techniques Applied to Environmental Science, Part II

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: Michael J. Starek, Texas A&M University−Corpus Christi; Chuyen Nguyen, Texas A&M University−Corpus Christi

10:30 AM

5A.1

Assured Operations of the Environmental Information Enterprise

Stephen Marley, The Aerospace Corporation, Silver Spring, MD; and K. Collett, I. Guch, J. Neff, N. Perlongo, C. Sprague, and Y. C. Tung

Recorded Presentation

10:45 AM

5A.2

Intraday Ground Horizontal Irradiance Forecasting Using LSTM

Xiuhong Chen, University of Michigan, Ann Arbor, MI; and H. Shen, J. Lu, and X. Huang

11:00 AM

5A.4

Estimation of Ground Ozone Concentration by Using the Synthetic Data of Geostationary Satellite and Machine Learning Techniques

Guanyu Huang, Spelman College, Atlanta, GA; and C. Miller

Recorded Presentation

11:15 AM

5A.5

Arabian Peninsula Extreme Rainfall Characteristics Using Self-Organizing Maps

Thang M. Luong, King Abdullah Univ. of Science and Technology, Thuwal, Saudi Arabia; and I. Hoteit

Session 5B

AI Techniques for Numerical Weather Prediction, Part II

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: Ryan A. Lagerquist, CIMMS; Edwin Campos, Uptake Technologies

10:30 AM

5B.1

Surface Layer Flux Machine Learning Parameterizations

David John Gagne II, NCAR, Boulder, CO; and T. C. McCandless, T. Brummet, B. Kosovic, and S. E. Haupt

Recorded Presentation

10:45 AM

5B.2

Downscaling Air Temperature Forecasts along Intricate Coastlines

Mathias Nipen, Norwegian Meteorological Institute, Oslo, Norway; and T. Nipen and I. A. Seierstad

Recorded Presentation

11:00 AM

5B.3

Postprocessing HREFv2 Heavy Rainfall Forecasts Using Machine Learning

Eric D. Loken, CIMMS/University of Oklahoma, Norman, OK; and A. J. Clark and A. McGovern

Recorded Presentation

5B.4

Numerical Weather Prediction Postprocessing with Convolutional Neural Network

11:30 AM

5B.5

PROGNOS: A Renewed Statistical Postprocessing Infrastructure and Opportunity for AI Applications to Weather and Air Quality Forecasting for the Meteorological Service of Canada (MSC)

Christian Saad, EC, Dorval, Canada; and J. Baik, A. Teakles, J. Montpetit, S. Antonopoulos, Y. Chartier, and S. Beauregard

Recorded Presentation

12:00 PM-1:30 PM: Tuesday, 8 January 2019

Lunch Break (Tuesday)

1:30 PM-2:30 PM: Tuesday, 8 January 2019

Themed Joint Session 10

Big Data, Big Computing, Bigger Science: Earth Data for AI

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; the Ninth Symposium on Advances in Modeling and Analysis Using Python; the Fifth Symposium on High Performance Computing for Weather, Water, and Climate; and the 32nd Conference on Climate Variability and Change )

Cochairs: Timothy S. Sliwinski, Texas Tech University; Surya Karthik Mukkavilli, University of New South Wales/CSIRO Australia/World Energy Meteorology Council UK/GroundObs Ltd.; Auroop Ganguly, Northeastern University; Scott Collis, Argonne National Laboratory; Gerald J. Creager, CIMMS/University of Oklahoma and NOAA/NSSL; Philippe Tissot, Texas A&M University−Corpus Christi

1:30 PM

TJ10.1

AI for Earth (Core Science Keynote)

Lucas Joppa, Microsoft, Redmond, WA

Recorded Presentation

2:00 PM

TJ10.2

The Need for HPC for Deep Learning with Real-Time Satellite Observations

Jebb Q. Stewart, NOAA, Boulder, CO; and C. Bonfanti, I. Jankov, L. Trailovic, and M. W. Govett

Recorded Presentation

2:15 PM

TJ10.3

Machine Learning Applications of the Earth Data Analytic Services (EDAS) Framework

Thomas P. Maxwell, NASA, Greenbelt, MD; and D. Duffy, G. L. Potter, and L. Carriere

Recorded Presentation

2:30 PM-3:00 PM: Tuesday, 8 January 2019

PM Coffee Break (Tuesday)

Location: Meeting room foyers (Phoenix Convention Center - West and North Buildings)

3:00 PM-4:00 PM: Tuesday, 8 January 2019

Panel Discussion 2

Environet: The ImageNet for Environmental Sciences

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Moderator: Surya Karthik Mukkavilli, GroundObs

Panelists: Lucas Joppa, Microsoft; Valliappa Lakshmanan, McGill University; Gregory Dudek, McGill University; Mr Prabhat, LBNL

3:00 PM

6.1

EnviroNet: ImageNet for Environment

Surya Karthik Mukkavilli, Groundobs, Montreal, Canada & London, United Kingdom; and A. Bara, D. J. Gagne II, P. Tissot, E. Campos, A. R. Ganguly, L. Joppa, D. Meger, and G. Dudek

Recorded Presentation

4:00 PM-6:00 PM: Tuesday, 8 January 2019

Formal Poster Viewing Reception (Tues)

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

Wednesday, 9 January 2019

8:30 AM-10:00 AM: Wednesday, 9 January 2019

Joint Session 1

Machine Learning Techniques for Atmospheric and Oceanic Prediction Models

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; and the 17th Symposium on the Coastal Environment )

Cochairs: Christiane Jablonowski, University of Michigan; Amy McGovern, University of Oklahoma; Alan Blumberg, Stevens Institute of Technology

8:30 AM

J1.1

Testing the Potential of Machine Learning for Weather Prediction

Peter Dueben, ECMWF, Reading, United Kingdom; and P. Bauer

Recorded Presentation

8:45 AM

J1.2

Evaluating Generative Adversarial Network Stochastic Parameterizations of the Lorenz '96 Model at Weather and Climate Time Scales

David John Gagne II, NCAR, Boulder, CO; and H. Christensen, A. Subramanian, and A. H. Monahan

9:00 AM

J1.3

Using Machine Learning to Emulate Critical Cloud Microphysical Processes

Chih-Chieh Chen, NCAR, Boulder, CO; and A. Gettelman, R. D. Loft, D. J. Gagne II, and N. Sobhani

Recorded Presentation

9:15 AM

J1.4

Using Deep Learning as Cost-Effective Surrogate Models for GCM Radiative Transfer.

Anikesh Pal, ORNL, Oak Ridge, TN; and S. Mahajan and M. R. Norman

Recorded Presentation

9:30 AM

J1.5

Can Machine Learning Provide A Shortcut To Fog Prediction

Yuchao Jiang, Weathernews America Inc., Norman, OK; and D. Makino and K. Sakamoto

Recorded Presentation

J1.6

Predictive statistical representations of observed and simulated rainfall using generalized linear models

9:45 AM

J1.6A

A Deep Recurrent Neural Network to Forecast the Intensity and Trajectory of Atlantic Tropical Storms

Hammad Usmani, Georgia Institute of Technology, Atlanta, GA

Recorded Presentation

10:00 AM-10:30 AM: Wednesday, 9 January 2019

AM Coffee Break (Wed)

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

10:30 AM-12:00 PM: Wednesday, 9 January 2019

Themed Joint Session 17

How Can AI/Stat Models be Interpreted Physically (Joint with the AMS Committee on Probability and Statistics)

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Cochairs: Philippe Tissot, Texas A&M University−Corpus Christi; Amy McGovern, University of Oklahoma; Elizabeth A. Satterfield, NRL

10:30 AM

TJ17.1

Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning (Core Science Keynote)

Amy McGovern, University of Oklahoma, Norman, OK; and D. J. Gagne II, R. A. Lagerquist, E. Jergensen, and K. L. Elmore

Recorded Presentation

11:00 AM

TJ17.2

Physics-Informed Generative Learning to Emulate Unresolved Physics in Climate Models

Jinlong Wu, Virginia Tech, Blacksburg, VA; and K. Kashinath, A. Albert, M. Prabhat, and H. Xiao

Recorded Presentation

11:15 AM

TJ17.3

Neural Networks for Postprocessing Ensemble Weather Forecasts

Stephan Rasp, Ludwig-Maximilians-University, Munich, Germany; and S. Lerch

Recorded Presentation

11:30 AM

TJ17.4

Some Conclusions on Applying Statistical Design of Experiments to Numerical Weather Prediction

Jeffrey A. Smith, U.S. Army Research Laboratory, White Sands Missile Range (WSMR), NM; and R. S. Penc, J. W. Raby, and J. L. Cleveland

Recorded Presentation

Handout (1.7 MB)

11:45 AM

TJ17.5

Interpretable AI for Deep Learning−Based Meteorological Applications

Conner Sprague, The Aerospace Corporation, Chantilly, VA; and E. B. Wendoloski and I. Guch

Recorded Presentation

12:00 PM-1:30 PM: Wednesday, 9 January 2019

Lunch Break (Wed)

12:15 PM-1:15 PM: Wednesday, 9 January 2019

Statistics vs Machine Learning for Complex Problems: White, Black or Grey Boxes?

Location: North 224A (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the Town Hall Meetings; and the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences )

Organizer: Elizabeth A. Satterfield, NRL

Facilitator: Philippe E. Tissot, Texas A&M University-Corpus Christi

Panelists: Elana Fertig, Johns Hopkins University; David John Gagne II, NCAR; Sebastian Lerch, Heidelberg Institute for Theoretical Studies; Amy McGovern, University of Oklahoma; Elaine Yang, Jupiter

1:30 PM-2:30 PM: Wednesday, 9 January 2019

Joint Session 6

Artificial Intelligence and Climate: Impact and Opportunities

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; and the 32nd Conference on Climate Variability and Change )

Cochairs: Carlos F. Gaitan, ClimateAI; Philippe Tissot, Texas A&M University−Corpus Christi

1:30 PM

J6.1

Deep Learning Recognizes Climate and Weather Patterns and Emulates Complex Processes Critical to the Modeling of Earth's Climate

Karthik Kashinath, LBNL, Berkeley, CA; and M. Prabhat, M. Mudigonda, A. Mahesh, S. Kim, J. Wu, A. Albert, A. Rupe, A. Fernandez, T. A. O'Brien, M. F. Wehner, and W. D. Collins

Recorded Presentation

1:45 PM

J6.2

A Topology-Based Approach To Characterization and Detection of Weather Patterns in Climate Model Output: Application to Atmospheric Blocking Events

Grzegorz Muszynski, LBNL, Berkeley, CA; and K. Kashinath, V. Kurlin, M. F. Wehner, and M. Prabhat

Recorded Presentation

2:00 PM

J6.3

Climate Science, Deep Learning, and Pattern Discovery: The Madden−Julian Oscillation as a Test Case

Benjamin A. Toms, Colorado State Univ., Fort Collins, CO; and K. Kashinath, M. Prabhat, and D. Yang

Recorded Presentation

2:15 PM

J6.4

Early Predictions of Extreme Heat Events in the Eastern United States Using Machine Learning

Negin Sobhani, NCAR, Boulder, CO; and D. Del Vento and A. Fanfarillo

2:30 PM-3:00 PM: Wednesday, 9 January 2019

PM Coffee Break (Wed)

Location: Meeting room foyers (Phoenix Convention Center - West and North Buildings)

3:00 PM-4:00 PM: Wednesday, 9 January 2019

Panel Discussion 1

Status of AI

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

Host: 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Moderator: Carlos Gaitan, ClimateAI

Panelists: Sue Haupt, NCAR; Lucas Joppa, Microsoft; Karthik Kashinath, LBNL; Daniel Rothenberg, ClimaCell Inc.

Joint Session 2

Applications of Artificial Intelligence in the Coastal Environment

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; and the 17th Symposium on the Coastal Environment )

Cochairs: Michael J. Starek, Texas A&M University−Corpus Christi; Philippe Tissot, Texas A&M University−Corpus Christi; Alan Blumberg, Stevens Institute of Technology

3:00 PM

J2.1

Building Custom Neural Net Models to Classify Coastal Imagery

Anthony Reisinger, Texas A&M University−Corpus Christi, Corpus Christi, TX; and A. Unruh, P. Tissot, and V. Lakshmanan

Recorded Presentation

3:15 PM

J2.2

Assessment of Machine Learning Ensembles for Modeling DEM Uncertainty in Marshes with Terrestrial Laser Scanning

Chuyen Nguyen, Texas A&M University−Corpus Christi, Corpus Christi, TX; and M. J. Starek, P. Tissot, and X. Cai

Recorded Presentation

3:30 PM

J2.3

Estimates of Spatial Variability of Digital Elevation Models from Ensemble Neural Networks

Xiaopeng Cai, Texas A&M Univ., Corpus Christi, TX; and P. Tissot, C. Nguyen, and M. J. Starek

Recorded Presentation

3:45 PM

J2.4

Probabilistic AR Detection for Understanding Western Coastal Hydroclimate

Travis A. O'Brien, LBNL, Berkeley, CA; and A. Mahesh, M. D. Risser, C. J. Paciorek, M. F. Wehner, C. M. Patricola, J. P. O'Brien, M. Prabhat, and W. D. Collins

Recorded Presentation

4:00 PM-6:00 PM: Wednesday, 9 January 2019

Formal Poster Viewing Reception (Wed)

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

5:30 PM-6:30 PM: Wednesday, 9 January 2019

Awards Banquet Reception

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

7:00 PM-10:00 PM: Wednesday, 9 January 2019

99th AMS Awards Banquet

Location: North Ballroom (Phoenix Convention Center - West and North Buildings)

Thursday, 10 January 2019

8:30 AM-9:30 AM: Thursday, 10 January 2019

Joint Session 4

Scalable Operational Artificial Intelligence Applications with Python

Location: North 221C (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the Ninth Symposium on Advances in Modeling and Analysis Using Python; and the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences )

Cochairs: David John Gagne II, NCAR; Scott Collis, Argonne National Laboratory; Daniel Rothenberg, ClimaCell Inc.; Sarvesh Garimella, ACME AtronOmatic, LLC

8:30 AM

J4.1

The Influence of Model Architecture on Multi-GPU Training of Adversarial Networks

Sarvesh Garimella, ACME AtronOmatic, LLC, Portland, OR

Recorded Presentation

8:45 AM

J4.2

Exploring the Use of Artificial Intelligence (AI) to Optimize the Exploitation of Big Satellite Data in NWP and Nowcasting

Sid Ahmed Boukabara, NOAA/NESDIS, College Park, MD; and E. Maddy, N. Shahroudi, R. N. Hoffman, T. Connor, S. Upton, A. Karpovich, C. Sprague, and K. Kumar

Recorded Presentation

Handout (6.4 MB)

9:00 AM

J4.3

Deep Learning for Improved Use of Satellite Observations

David Hall, NVIDIA Corporation, Lafayette, CO; and J. Q. Stewart, C. Bonfanti, M. W. Govett, S. Maksimovic, and L. Trailovic

Recorded Presentation

9:15 AM

J4.4

Using Python and Machine Learning Techniques for High-Impact Weather Nowcasting in an Operational Center in Brazil

Cesar Beneti, Parana Meteorological System (SIMEPAR), Curitiba, Brazil; and N. Rozin, L. Calvetti, J. Ruviaro, and C. Oliveira

Recorded Presentation

10:30 AM-12:00 PM: Thursday, 10 January 2019

Joint Session 3

Machine Learning Applications in the Energy Sector (Joint with 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences)

Location: North 129A (Phoenix Convention Center - West and North Buildings)

Hosts: (Joint between the 10th Conference on Weather, Climate, and the New Energy Economy; and the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences )

Cochairs: David John Gagne II, NCAR; Lawrence Gloeckler III, Riskpulse

10:30 AM

J3.1

Artificial Intelligence and Meteorology Empowering Renewable Energy

Sue Ellen Haupt, NCAR, Boulder, CO

Recorded Presentation

11:00 AM

J3.2

Statistical Modeling of Wind Plant Power Production: Contrasting Different Machine Learning Algorithms and Atmospheric Features

Mike Optis, National Renewable Energy Laboratory, Golden, CO

Handout (1.6 MB)

11:15 AM

J3.3

The Super Turbine Wind Power Conversion Paradox: Using Machine Learning to Overcome Jensen’s Inequality

Tyler C. McCandless, NCAR, Boulder, CO; and S. E. Haupt

Recorded Presentation

11:30 AM

J3.4

The Relationship between GHI and Power in Kuwait

Thomas Brummet, NCAR, Boulder, CO; and G. Wiener and J. A. Lee

Recorded Presentation

J3.5

Reducing Wind Power Prediction Uncertainty with Atmospheric Characterization and Machine Learning Models

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Sunday, 6 January 2019

4:00 PM-5:00 PM: Sunday, 6 January 2019

Plenary Session Presidential Forum: Building Resilience to Extreme Political Weather: Advice for Unpredictable Times

Location: North Ballroom 120CD (Phoenix Convention Center - West and North Buildings)

Monday, 7 January 2019

8:30 AM-10:00 AM: Monday, 7 January 2019

AI Techniques Applied to Environmental Science

Location: North 124B (Phoenix Convention Center - West and North Buildings)

10:00 AM-10:30 AM: Monday, 7 January 2019

AM Coffee Break (Monday)

Location: Meeting room foyers (Phoenix Convention Center - West and North Buildings)

10:30 AM-12:00 PM: Monday, 7 January 2019

AI Techniques for Decision Support, Part I

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Deep Learning for Environmental Datasets

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

12:00 PM-1:30 PM: Monday, 7 January 2019

Lunch Break (Monday)

2:00 PM-2:45 PM: Monday, 7 January 2019

Deep Learning for Environmental Datasets, Part II

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

2:00 PM-4:00 PM: Monday, 7 January 2019

AI Techniques for Extreme Weather and Risk Assessment

Location: North 124B (Phoenix Convention Center - West and North Buildings)

2:45 PM-4:00 PM: Monday, 7 January 2019

AI Applications to Air Quality

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

4:00 PM-6:00 PM: Monday, 7 January 2019

Formal Poster Viewing Reception

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

JP 1

Joint Poster Session Big Data, Big Computing, Bigger Science

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

This session will address the challenges with respect to the collection, storage, transfer, and analysis of "Big Data" across the earth sciences, and the methods being used or developed to overcome them. Speakers will address real-world Big Data challenges they've encountered in their workflows and the solutions they've used to overcome them. Solutions could include data mining or machine learning algorithms, new or established Python toolkits, high performance parallel computing systems or platforms, advanced data storage and transfer capabilities, and more.

Poster Session Artificial and Computational Intelligence and Its Applications to the Environmental Sciences Poster Session

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

6:00 PM-8:00 PM: Monday, 7 January 2019

Exhibits Opening and Reception

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

Tuesday, 8 January 2019

8:30 AM-10:00 AM: Tuesday, 8 January 2019

AI Applied to Airborne or Spaceborne Earth Observation Datasets

Location: North 124B (Phoenix Convention Center - West and North Buildings)

AI Techniques for Numerical Weather Prediction, Part I

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

10:00 AM-10:30 AM: Tuesday, 8 January 2019

AM Coffee Break (Tuesday)

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

10:30 AM-11:45 AM: Tuesday, 8 January 2019

AI Techniques Applied to Environmental Science, Part II

Location: North 124B (Phoenix Convention Center - West and North Buildings)

AI Techniques for Numerical Weather Prediction, Part II

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

12:00 PM-1:30 PM: Tuesday, 8 January 2019

Lunch Break (Tuesday)

1:30 PM-2:30 PM: Tuesday, 8 January 2019

TJ 10

Themed Joint Session Big Data, Big Computing, Bigger Science: Earth Data for AI

Location: North 124B (Phoenix Convention Center - West and North Buildings)

Many challenges exist with respect to the collection, storage, transfer, and analysis of "Big Data" across the Earth sciences. New Earth observing sensors and models generate richer and bigger datasets, and many methods are being used or developed to overcome the difficulty of working with them. This session discusses ongoing efforts to identify, assemble, and make large multivariate Earth datasets widely accessible for comparative AI research. Speakers will address real-world Big Data challenges they have encountered in their workflows and/or the solutions they have used to overcome them. Solutions could include data mining or machine learning algorithms, high-performance parallel computing systems or platforms, advanced data storage and transfer capabilities, and more.

2:30 PM-3:00 PM: Tuesday, 8 January 2019

PM Coffee Break (Tuesday)

Location: Meeting room foyers (Phoenix Convention Center - West and North Buildings)

3:00 PM-4:00 PM: Tuesday, 8 January 2019

PD 2

Panel Discussion Environet: The ImageNet for Environmental Sciences

Location: North 124B (Phoenix Convention Center - West and North Buildings)

This panel discussion focuses on the development of project EnviroNet: an ImageNet analog for environmental sensing data and a global challenge to benchmark progress of artificial intelligence (AI) in addressing planetary boundaries we are on the verge of exceeding. The panel will include world leading representatives working at the cutting-edge of industry, universities, startups, and government labs in AI and geosciences. The goal of the discussion is to seek input on where the AI field is at present and the progress necessary over the next 30 years to address major challenges in meteorology and geoscientific fields before we push the planet into an unsustainable state. The panel and audience will provide their perspective on EnviroNet dataset and challenge design considerations that could maximize engagement with the wider communities, leading to progress on pressing global problems that bridge gaps across AI, geosciences and meteorology.

4:00 PM-6:00 PM: Tuesday, 8 January 2019

Formal Poster Viewing Reception (Tues)

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

Wednesday, 9 January 2019

8:30 AM-10:00 AM: Wednesday, 9 January 2019

J 1

Joint Session Machine Learning Techniques for Atmospheric and Oceanic Prediction Models

Location: North 124B (Phoenix Convention Center - West and North Buildings)

The session focuses on machine learning techniques for future-generation weather, climate, and ocean models, which includes machine learning testbeds with idealized modeling frameworks such as aquaplanets, shallow-water models, single-column models, or even more conceptual models. Potential topics might explore whether machine learning techniques can be used to estimate uncertain parameters or closure assumptions in subgrid-scale physical parameterizations, or even replace complex physical parameterization packages such as cloud or turbulence schemes. Of particular interest are physics-informed machine learning concepts.

10:00 AM-10:30 AM: Wednesday, 9 January 2019

AM Coffee Break (Wed)

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

10:30 AM-12:00 PM: Wednesday, 9 January 2019

TJ 17

Themed Joint Session How Can AI/Stat Models be Interpreted Physically (Joint with the AMS Committee on Probability and Statistics)

Location: North 124B (Phoenix Convention Center - West and North Buildings)

The session will tackle the use of AI and Stat models for physical interpretability of nonlinear systems as well as the interpretation of the models themselves. Nonlinear environmental systems cannot simply use linear analysis methods to understand their behavior. Can relevant information be obtained from successful AI/ML model development, or are the underlying processes themselves sufficiently complex as to not be decipherable? If the latter, insights from successful nonlinear model development could be misleading. Submissions are sought in the areas of AI and STAT methods used to tackle classification, deep learning, regression, and probabilistic distribution problems.

12:00 PM-1:30 PM: Wednesday, 9 January 2019

Lunch Break (Wed)

12:15 PM-1:15 PM: Wednesday, 9 January 2019

Statistics vs Machine Learning for Complex Problems: White, Black or Grey Boxes?

Location: North 224A (Phoenix Convention Center - West and North Buildings)

With the rapid growth of Machine Learning (ML) and its recent prominence in Environmental Sciences, this town hall aims to better understand how such methods can work in concert with or complement more traditional statistical methods. This town hall aims to better define the challenges of ML methods in environmental science applications, discuss how to determine which problems are better suited to particular methods, explore how statistical method and ML can work together and focus on how we can increase interpretability of ML algorithms.

1:30 PM-2:30 PM: Wednesday, 9 January 2019

J 6

Joint Session Artificial Intelligence and Climate: Impact and Opportunities

Location: North 124B (Phoenix Convention Center - West and North Buildings)

The use of machine learning techniques is already widespread in the climate community. Examples include the application of deep learning to characterize climate model outputs, high-resolution climate downscaling, or the use of neural networks for the modeling of climate time series. Submissions are sought for broad contributions that AI can make to climate studies and particularly topics relevant to the work of the National Climate Assessment Committee.

2:30 PM-3:00 PM: Wednesday, 9 January 2019

PM Coffee Break (Wed)

Location: Meeting room foyers (Phoenix Convention Center - West and North Buildings)

3:00 PM-4:00 PM: Wednesday, 9 January 2019

PD 1

Panel Discussion Status of AI

Location: North 125AB (Phoenix Convention Center - West and North Buildings)

J 2

Joint Session Applications of Artificial Intelligence in the Coastal Environment

Location: North 124B (Phoenix Convention Center - West and North Buildings)

4:00 PM-6:00 PM: Wednesday, 9 January 2019

Formal Poster Viewing Reception (Wed)

Location: Hall 4 (Phoenix Convention Center - West and North Buildings)

5:30 PM-6:30 PM: Wednesday, 9 January 2019

Awards Banquet Reception

Location: Hall 5-6 (Phoenix Convention Center - West and North Buildings)

7:00 PM-10:00 PM: Wednesday, 9 January 2019

99th AMS Awards Banquet

Location: North Ballroom (Phoenix Convention Center - West and North Buildings)

Thursday, 10 January 2019

8:30 AM-9:30 AM: Thursday, 10 January 2019

J 4

Joint Session Scalable Operational Artificial Intelligence Applications with Python

Location: North 221C (Phoenix Convention Center - West and North Buildings)

This session highlights projects in the private sector, government, and academia that have used Python software libraries to power applications utilizing artificial intelligence, machine learning, and other data science techniques for earth, oceanic, and atmospheric science problems. The presenters are encouraged to discuss the details of how they have implemented these systems and discuss both their successes and the challenges they have overcome or still face. Following the presentations, there will be a panel discussion featuring all of the presenters where they can answer more questions from the audience about incorporating and scaling AI systems. If there are a large number of submissions to this session, then a poster session will be added following the oral session to highlight other scalable AI and Python systems.

10:30 AM-12:00 PM: Thursday, 10 January 2019

J 3

Joint Session Machine Learning Applications in the Energy Sector (Joint with 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences)

Location: North 129A (Phoenix Convention Center - West and North Buildings)

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Joint Session 3 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences

Sunday, 6 January 2019

4:00 PM-5:00 PM: Sunday, 6 January 2019

Monday, 7 January 2019

8:30 AM-10:00 AM: Monday, 7 January 2019

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Tuesday, 8 January 2019

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Wednesday, 9 January 2019

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Thursday, 10 January 2019

8:30 AM-9:30 AM: Thursday, 10 January 2019

10:30 AM-12:00 PM: Thursday, 10 January 2019

Sunday, 6 January 2019

4:00 PM-5:00 PM: Sunday, 6 January 2019

Monday, 7 January 2019

8:30 AM-10:00 AM: Monday, 7 January 2019

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Tuesday, 8 January 2019

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Wednesday, 9 January 2019

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Thursday, 10 January 2019

8:30 AM-9:30 AM: Thursday, 10 January 2019

10:30 AM-12:00 PM: Thursday, 10 January 2019