Full Workshop Agenda, (PDF, 197 KB)
AI 2020 Presentations Index
| Title |
Presenter / Affiliation |
Session |
| Information on the 2nd NOAA AI Workshop: Logistics, Timeline and Structure, (PPTX, 8.33 MB) |
Kevin Garrett - NOAA/NESDIS/STAR, Local Organizing Committee |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Welcoming remarks and introduction of keynote speakers |
Harry Cikanek - NOAA/NESDIS, STAR Director |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Keynote Address, NOAA AI: Realizing Transformational Advances in Mission Performance and Our Culture of Innovation |
RADM Timothy Gallaudet - NOAA, Deputy NOAA Administrator |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Keynote Address |
Stephen Volz - NOAA, NESDIS Assistant Administrator |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Keynote Address, (PDF, 2.49 MB) |
Nicole LeBoeuf - NOAA, NOS Acting Assistant Administrator |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| NOAA AI Implementation Plan, (PPTX, 40.97 MB) |
Bill Michaels - NOAA, NMFS |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Efforts in NOAA to Leverage Modern AI techniques for Satellite Data Exploitation and NWP, (PPTX, 169.55 MB) |
Sid Boukabara - NOAA/NESDIS, STAR Principal Scientist |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Machine Learning at ECMWF, (PPTX, 15.98 MB) |
Peter Dueben - ECMWF |
Session 1: Overview Talks, Part 1,
Thursday, 30 July 2020 |
| Data Science and Machine Learning at the UK Met Office, (PPTX, 23.94 MB) |
Samantha Adams - UKMO |
Session 2: Fundamentals of AI, Part 1
Chairs: Dave Turner (NOAA. ESRL), Jebb Stewart (NOAA, ESRL),
Thursday, 6 August 2020 |
| Recent Machine Learning Research at NCAR, (PPTX, 39.84 MB) |
Sue Ellen Haupt - NCAR |
Session 2: Fundamentals of AI, Part 1
Chairs: Dave Turner (NOAA. ESRL), Jebb Stewart (NOAA, ESRL),
Thursday, 6 August 2020 |
| Data-driven (super-) parametrization using deep learning: Experimentation with a multi-scale Lorenz 96 system and transfer learning, (PPTX, 5.68 MB) |
Ashesh Chattopadhyay - Rice U. |
Session 2: Fundamentals of AI, Part 1
Chairs: Dave Turner (NOAA. ESRL), Jebb Stewart (NOAA, ESRL),
Thursday, 6 August 2020 |
| NOAA Center for AI (NCAI) Introduction, (PPTX, 60.95 MB) |
Bill Michaels - AI S&T Chair, Mary Wohlgemuth - NCEI Director,
Eric Kihn - NCEI CCOG Director, Rob Redmon - NCAI Acting Director, LCDP XI |
Session 3: Looking Ahead (Using AI for NOAA mission), Part 1
Chairs: Bill Michaels (NOAA, NMFS), John Ten Hoeve (Office of Organizational Excellence),
Thursday, 13 August 2020 |
| Artificial Intelligence for Advanced Earth Science Information Systems |
Jacqueline Le Moigne - NASA |
Session 4: AI/ML for Post-Processing and Data dissemination, Part 1
Chairs: Greg Dusek (NOAA/NOS), Andre van der Westhuysen (IMSG at NWS/NCEP/EMC),
Thursday, 20 August 2020 |
| Using Random Forests to Create Probabilistic Next-Day Severe Weather Guidance from NWP Ensembles, (PPTX, 112.77 MB) |
Eric Loken - OU CIMMS/OU |
Session 4: AI/ML for Post-Processing and Data dissemination, Part 1
Chairs: Greg Dusek (NOAA/NOS), Andre van der Westhuysen (IMSG at NWS/NCEP/EMC),
Thursday, 20 August 2020 |
| Modeling Clouds From Sub-grid to Global Scales with Deep Generative Models |
Tianle Yuan - NASA GSFC/UMBC JCET |
Session 4: AI/ML for Post-Processing and Data dissemination, Part 1
Chairs: Greg Dusek (NOAA/NOS), Andre van der Westhuysen (IMSG at NWS/NCEP/EMC),
Thursday, 20 August 2020 |
| Combining data assimilation and machine learning for weather forecasting, (PPTX, 8.67 MB) |
Alan Geer - ECMWF |
Session 5: AI/ML for Environmental Data, Image, and Signal Processing, Part 1
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD)
,
Thursday, 27 August 2020 |
| Viewing Climate Signals through an AI Lens, (PDF, 5.59 MB) |
Elizabeth Barnes - CSU |
Session 5: AI/ML for Environmental Data, Image, and Signal Processing, Part 1
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD)
,
Thursday, 27 August 2020 |
| Video and Image Analytics for Marine Environments (VIAME), a Do-it-yourself AI Toolkit, (PDF, 5.62 MB) |
Matthew Dawkins - Kitware Inc |
Session 5: AI/ML for Environmental Data, Image, and Signal Processing, Part 1
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD),
Thursday, 27 August 2020 |
| Generating High Temporal and Spatial Microwave Hurricane Image Products Using Artificial intelligence and Machine Learning Technique, (PDF, 4.09 MB) |
Likun Wang - RTi at NESDIS/STAR |
Session 5: AI/ML for Environmental Data, Image, and Signal Processing, Part 1
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD),
Thursday, 27 August 2020 |
| AI Quality Control of NOAA Tide Gauge Observations, (PPTX, 3.45 MB) |
Gregory Dusek - NOAA/NOS |
Session 6: AI/ML for Information Extraction from Data, Part 1
Chairs: Philippe Tissot (Texas A&M University, Corpus Christi), Jebb Stewart (NOAA, ESRL),
Thursday, 3 September 2020 |
| Artificial Intelligence and Deep Machine learning for Passive Acoustic Monitoring at NOAA Fisheries, (PPTX, 16.67 MB) |
Ann Allen, Manuel Castellote, Shannon Rankin - NOAA/NMFS/PIFSC, NOAA/NMFS/AFSC, NOAA/NMFS/SWFSC |
Session 6: AI/ML for Information Extraction from Data, Part 1
Chairs: Philippe Tissot (Texas A&M University, Corpus Christi), Jebb Stewart (NOAA, ESRL),
Thursday, 3 September 2020 |
| Trustworthy AI for High Impact Weather Prediction, (PPTX, 24.81 MB) |
Amy McGovern - OU |
Session 7: Fundamentals of AI, Part 2
Chairs: Amy McGovern (OU), David Hall (NVIDIA),
Thursday, 10 September 2020 |
| Machine Learning for Model Error Inference and Correction, (PDF, 1.96 MB) |
Massimo Bonavita - ECMWF |
Session 7: Fundamentals of AI, Part 2
Chairs: Amy McGovern (OU), David Hall (NVIDIA),
Thursday, 10 September 2020 |
| Ensemble Oscillation Correction (EnOC): Leveraging oscillatory modes to improve forecasts of chaotic systems, (PDF, 1.15 MB) |
Eviatar Bach - UMD |
Session 7: Fundamentals of AI, Part 2
Chairs: Amy McGovern (OU), David Hall (NVIDIA),
Thursday, 10 September 2020 |
| Cost Sensitive Loss Function for Machine Learning, (PPTX, 1.24 MB) |
Richard Berk - U. Penn |
Session 7: Fundamentals of AI, Part 2
Chairs: Amy McGovern (OU), David Hall (NVIDIA),
Thursday, 10 September 2020 |
| Which strategies did my neural network learn?, (PPTX, 127.71 MB) |
Imme Ebert-Uphoff - CIRA |
Session 8: Machine Learning Tools and Best Practices, Part 1
Chairs: Sue Haupt (NCAR), Jason Hickey (Google),
Thursday, 17 September 2020 |
| ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather, (PPTX, 121.37 MB) |
Karthik Kashinath - Lawrence Berkeley National Lab |
Session 8: Machine Learning Tools and Best Practices, Part 1
Chairs: Sue Haupt (NCAR), Jason Hickey (Google),
Thursday, 17 September 2020 |
| The AI for Earth System Science Hackathon: Challenge Problems and Lessons Learned, (PPTX, 26.42 MB) |
David Gagne - NCAR |
Session 8: Machine Learning Tools and Best Practices, Part 1
Chairs: Sue Haupt (NCAR), Jason Hickey (Google),
Thursday, 17 September 2020 |
| "AI for Science" program at Argonne NL, (PPTX, 21.65 MB) |
Ian Foster - ANL |
Session 8: Machine Learning Tools and Best Practices, Part 1
Chairs: Sue Haupt (NCAR), Jason Hickey (Google),
Thursday, 17 September 2020 |
| The role of machine learning in a seamless modeling approach from weather to climate time scales, (PDF, 13.06 MB) |
V. Balaji - NOAA/GFDL |
Session 10: AI/ML for Post-Processing and Data dissemination, Part 2
Chairs: Nikunj Oza (NASA), Allen Huang (UW-Madison),
Thursday, 24 September 2020 |
| Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces, (PDF, 51.24 MB) |
Maike Sonnewald - NOAA/GFDL |
Session 10: AI/ML for Post-Processing and Data dissemination, Part 2
Chairs: Nikunj Oza (NASA), Allen Huang (UW-Madison),
Thursday, 24 September 2020 |
| Accelerating Google's Flood Forecasting Initiative with Tensor Processing Units, (PDF, 4.26 MB) |
Vova Anisimov, Anudhyan Boral, Lily Hu, Sella Nevo, Damien Pierce, Yusef Shafi |
Session 10: AI/ML for Post-Processing and Data dissemination, Part 2
Chairs: Nikunj Oza (NASA), Allen Huang (UW-Madison),
Thursday, 24 September 2020 |
| Modelling runoff from green roofs using Deep Neural Networks, (PDF, 1.02 MB) |
Elhadi Abdalla - NTNU |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Fine-Delineated Tropical Cyclone Detection from Geostationary Satellites and IBTrACS data using Advanced Neural Networks, (PDF, 26.2 MB) |
Ata Akbari Asanjan - Universities Space Research Association |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Pixel-wise Deep Sequence learning for wildfire spread prediction in Alberta, Canada, (PDF, 2.08 MB) |
Xinli Cai - University of Alberta |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Using deep super-resolution for high resolution precipitation images, (PDF, 5.76 MB) |
Xinli Cai - University of Alberta |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Lightning prediction in the Atlantic offshore region, (PDF, 1.01 MB) |
John Cintineo - University of Wisconsin -- Madison |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Connecting ocean physical and biogeochemical properties with the spatial distribution of mesopelagic fish abundance, (PDF, 3.85 MB) |
Donglai Gong - Virginia Institute of Marine Science - William & Mary |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Using Data Mining Decision Tree Method to Identify the Optimal Fire Detection Thresholds, (PDF, 876 KB) |
Yingxin Gu - IMSG at NOAA/NESDIS/STAR |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Application of Advanced Deep Learning Algorithms in Precipitation Estimation from Multiple Sources of Information, (PDF, 11.3 MB) |
Negin Hayatbini - University of California, Irvine |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Low Cloud Detection for the GOES ABI using a Random Forest Classifier, (PDF, 15.65 MB) |
John Haynes - CIRA / Colorado State University |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| 3D Convolutional Deep Learning for Coastal Fog Predictions, (PDF, 1.68 MB) |
Hamid Kamangir - Texas A&M University-Corpus Christi |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Verification of a Machine Learning Algorithm in the Prediction of Flash Flooding, (PDF, 2.61 MB) |
Mark Klein - NWS/Weather Prediction Center |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Utilizing CNN's to produce Quantitative Precipitation Estimates, (PDF, 2.09 MB) |
Micheal Simpson - University of Oklahoma |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: application to Himawari-8, (PDF, 4.6 MB) |
Tianning Su - UMD |
Session 11: Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR),
Tuesday, 29 September 2020 |
| First steps toward a machine-learning based moist physics parameterization by coarse-graining, (PDF, 18.54 MB) |
Jeremy McGibbon - Vulcan |
Session 12: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 1
Chairs: Vladimir Krasnopolsky (NOAA/NCEP/EMC), Kayo Ide (UMD)
,
Thursday, 1 October 2020 |
| Operational In-Field Forecasting using Online Sequential Extreme Learning Machines, (PPTX, 25.75 MB) |
Carlos Gaitan - Benchmark Labs |
Session 12: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 1
Chairs: Vladimir Krasnopolsky (NOAA/NCEP/EMC), Kayo Ide (UMD)
,
Thursday, 1 October 2020 |
| Representing Aerosol-Cloud Interactions Using Machine Learning Techniques in Energy Exascale Earth System Model, (PDF, 15.13 MB) |
Po-Lun Ma - PNNL |
Session 12: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 1
Chairs: Vladimir Krasnopolsky (NOAA/NCEP/EMC), Kayo Ide (UMD)
,
Thursday, 1 October 2020 |
| Robustness of NN Emulations of Radiative Transfer Parameterizations in a State-of-the-Art GCM, (PPTX, 105.85 MB) |
Alex Belochitski - IMSG at NOAA/NCEP/EMC |
Session 12: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 1
Chairs: Vladimir Krasnopolsky (NOAA/NCEP/EMC), Kayo Ide (UMD)
,
Thursday, 1 October 2020 |
| Overview of AI activities at IBM Weather, (PPTX, 45.58 MB) |
John Williams - IBM Weather |
Session 13: AI/ML for Data Fusion/Assimilation, Part 1
Chairs: Peter Jan van Leeuwen (CSU), Steve Penny (NOAA PSD/CIRES),
Thursday, 15 October 2020 |
| Overview of AI activities at Google, (PPTX, 79.38 MB) |
Jason Hickey - Google |
Session 13: AI/ML for Data Fusion/Assimilation, Part 1
Chairs: Peter Jan van Leeuwen (CSU), Steve Penny (NOAA PSD/CIRES),
Thursday, 15 October 2020 |
| Automated Analysis of Satellite Imagery in Support of Severe Weather Nowcasting, (PPTX, 195.04 MB) |
Michael Pavolonis - NOAA/NESDIS/STAR |
Session 13: AI/ML for Data Fusion/Assimilation, Part 1
Chairs: Peter Jan van Leeuwen (CSU), Steve Penny (NOAA PSD/CIRES),
Thursday, 15 October 2020 |
| Neural Networks for Postprocessing Ensemble Weather Forecasts, (PDF, 1.03 MB) |
Sebastian Lerch - KIT |
Session 15: AI for Innovation: New Ways to Exploit Environmental Data, Part 1
Chairs:Christina Kumler (CIRES/NOAA/GSL), Jeremy McGibbon (Vulcan),
Thursday, 22 October 2020 |
| What is 'AI-Ready' Open Data?, (PDF, 364 KB) |
Tyler Christensen - NOAA/NOS/IMO |
Session 15: AI for Innovation: New Ways to Exploit Environmental Data, Part 1
Chairs:Christina Kumler (CIRES/NOAA/GSL), Jeremy McGibbon (Vulcan),
Thursday, 22 October 2020 |
| Improving Passive Acoustic Monitoring Applications to the Endangered Cook Inlet Beluga Whale, (PPTX, 7.55 MB) |
Ming Zhong - Microsoft |
Session 15: AI for Innovation: New Ways to Exploit Environmental Data, Part 1
Chairs:Christina Kumler (CIRES/NOAA/GSL), Jeremy McGibbon (Vulcan),
Thursday, 22 October 2020 |
| Leveraging NWP for Operational Machine Learning Predictions for Coastal and Environmental Stakeholders, (PPTX, 16.87 MB) |
Philippe Tissot - Texas A&M University, Corpus Christi |
Session 15: AI for Innovation: New Ways to Exploit Environmental Data, Part 1
Chairs:Christina Kumler (CIRES/NOAA/GSL), Jeremy McGibbon (Vulcan),
Thursday, 22 October 2020 |
| AI and Clouds at Microsoft, (PPTX, 1.26 MB) |
Justin Worrilow - Microsoft |
Session 16: AI/ML for Post-Processing and Data Dissemination, Part 3
Chairs: John K. Williams (The Weather Company, an IBM Business), Maike Sonnewald (NOAA/GFDL)
,
Thursday, 29 October 2020 |
| Improving CFS Precipitation and 2m Temperature Anomaly Outlooks from Week-1 to Week-6 with Machine Learning, (PPTX, 3.03 MB) |
Yun Fan - NCEP/CPC |
Session 16: AI/ML for Post-Processing and Data Dissemination, Part 3
Chairs: John K. Williams (The Weather Company, an IBM Business), Maike Sonnewald (NOAA/GFDL)
,
Thursday, 29 October 2020 |
| Shifting to AI for Passive Acoustic Monitoring of the Endangered Cook Inlet Beluga Whale, (PPTX, 8.7 MB) |
Manuel Castellote - NOAA AFSC and UW |
Session 16: AI/ML for Post-Processing and Data Dissemination, Part 3
Chairs: John K. Williams (The Weather Company, an IBM Business), Maike Sonnewald (NOAA/GFDL)
,
Thursday, 29 October 2020 |
| Precipitation forecasting through NWP correction considering the Korean Peninsula terrain, (PPTX, 17.56 MB) |
Se-Young Yun - KAIST |
Session 16: AI/ML for Post-Processing and Data Dissemination, Part 3
Chairs: John K. Williams (The Weather Company, an IBM Business), Maike Sonnewald (NOAA/GFDL)
,
Thursday, 29 October 2020 |
| NIMS R&D strategy for Alpha Weather, (PPTX, 19.22 MB) |
Hyesook Lee - KMA |
Session 17: AI/ML for Post-Processing and Data dissemination, Part 4
Chairs: Andre van der Westhuysen (IMSG at NWS/NCEP/EMC), William Collins (LBNL, UC Berkeley),
Thursday, 5 November 2020 |
| ML for post processing model output at EMC, (PPTX, 4.55 MB) |
Vladimir Krasnopolsky - NOAA/NCEP/EMC |
Session 17: AI/ML for Post-Processing and Data dissemination, Part 4
Chairs: Andre van der Westhuysen (IMSG at NWS/NCEP/EMC), William Collins (LBNL, UC Berkeley),
Thursday, 5 November 2020 |
| Applying satellite observations of tropical cyclone internal structures to rapid intensification forecast with machine learning, (PPTX, 10.27 MB) |
Hui Su - JPL/Caltech |
Session 17: AI/ML for Post-Processing and Data dissemination, Part 4
Chairs: Andre van der Westhuysen (IMSG at NWS/NCEP/EMC), William Collins (LBNL, UC Berkeley),
Thursday, 5 November 2020 |
| A Practical Introduction to Deep Learning for the Earth System Sciences using PyTorch, (PDF, 57.66 MB) |
David Hall - NVIDIA |
Session 18: Tutorial 3,
Tuesday, 10 November 2020 |
| Machine learning for detection of climate extremes: New approaches to uncertainty quantification, (PDF, 53.35 MB) |
William Collins - LBNL, UC Berkeley |
Session 19: AI/ML for Environmental Data, Image, and Signal Processing, Part 2
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD),
Thursday, 12 November 2020 |
| Analysis of Multispectral Land Surface Reflectance Time-Series for Detecting and Classifying Land Cover Change, (PDF, 1.21 MB) |
Srija Chakraborty - NASA GSFC/ USRA |
Session 19: AI/ML for Environmental Data, Image, and Signal Processing, Part 2
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD),
Thursday, 12 November 2020 |
| Super-Resolution of VIIRS-Measured Ocean Color Products Using Deep Convolutional Neural Network, (PDF, 5.14 MB) |
Xiaoming Liu - NOAA/NESDIS/STAR |
Session 19: AI/ML for Environmental Data, Image, and Signal Processing, Part 2
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD),
Thursday, 12 November 2020 |
| Exploring the Frontiers of Deep Learning for Earth and Space, (PPTX, 183.75 MB) |
David Hall - NVIDIA |
Session 20: Looking Ahead (Using AI for NOAA mission), Part 2
Chairs: Michael Pavolonis (NESDIS/STAR), Philippe Tissot (Texas A&M University, Corpus Christi),
Thursday, 19 November 2020 |
| Accelerating biodiversity surveys with computer vision: successes and challenges, (PPTX, 37.81 MB) |
Dan Morris - Microsoft AI for Earth |
Session 20: Looking Ahead (Using AI for NOAA mission), Part 2
Chairs: Michael Pavolonis (NESDIS/STAR), Philippe Tissot (Texas A&M University, Corpus Christi),
Thursday, 19 November 2020 |
| Counting Belugas from Space: Can we use very high resolution satellite imagery to accurately assess the critically endangered beluga whale population in Cook Inlet, Alaska?, (PPTX, 30.31 MB) |
Kimberly Goetz - NOAA/NMFS/AFSC/MML |
Session 20: Looking Ahead (Using AI for NOAA mission), Part 2
Chairs: Michael Pavolonis (NESDIS/STAR), Philippe Tissot (Texas A&M University, Corpus Christi),
Thursday, 19 November 2020 |
| Tackling challenges of Ocean Exploration with Machine Learning and Artificial Intelligence, (PPTX, 23.65 MB) |
Matt Dornback - NOAA/OAR/OER |
Session 20: Looking Ahead (Using AI for NOAA mission), Part 2
Chairs: Michael Pavolonis (NESDIS/STAR), Philippe Tissot (Texas A&M University, Corpus Christi),
Thursday, 19 November 2020 |
| Using Neural Networks as Model Physics Components in Numerical Weather Prediction, (PPTX, 9.04 MB) |
Vladimir Krasnopolsky - NOAA/NCEP/EMC |
Session 22: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 2
Chairs: Likun Wang (ESSIC, University of Maryland), Ashesh Chattopadhyay (Rice University),
Thursday, 3 December 2020 |
| Challenges associated with training a machine-learning based moist physics parameterization by coarse-graining in a model with topography, (PPTX, 14.24 MB) |
Spencer Clark - Vulcan, Inc./NOAA GFDL |
Session 22: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 2
Chairs: Likun Wang (ESSIC, University of Maryland), Ashesh Chattopadhyay (Rice University),
Thursday, 3 December 2020 |
| Exploring Various Machine Learning Techniques for Emulating Simplified Physical Parameterizations in the Community Atmosphere Model, (PPTX, 20.15 MB) |
Garrett Limon - University of Michigan |
Session 22: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 2
Chairs: Likun Wang (ESSIC, University of Maryland), Ashesh Chattopadhyay (Rice University),
Thursday, 3 December 2020 |
| Predicting Algal Bloom Toxicity in Lake Erie: Lessons From Machine Learning, (PPTX, 14.4 MB) |
Theodore A.D. Slawecki - LimnoTech |
Session 22: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 2
Chairs: Likun Wang (ESSIC, University of Maryland), Ashesh Chattopadhyay (Rice University),
Thursday, 3 December 2020 |
| Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions |
Janni Yuval - MIT |
Session 22: AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 2
Chairs: Likun Wang (ESSIC, University of Maryland), Ashesh Chattopadhyay (Rice University),
Thursday, 3 December 2020 |
| Cloud classification with unsupervised deep learning, (PDF, 2.04 MB) |
Takuya Kurihana - University of Chicago |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Convection Classification in a Future Climate: What did Deep Learning Really Learn?, (PDF, 7.64 MB) |
Maria Molina - National Center for Atmospheric Research |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Engaging Freshmen Undergraduates in AI on Cloud Imagery and Model Output, (PDF, 1.06 MB) |
Alexandra Jones - UMD |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Preparing for the Future: Development of an Open-Source Workflow for AI driven Acoustic Data Analysis, (PDF, 670 KB) |
Shannon Rankin - Southwest Fisheries Science Center, NMFS |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| AI in the US Inland Waterways industry, (PDF, 4.43 MB) |
David Sathiaraj - Trabus Technologies |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Online bias correction of weather models using machine learning, (PDF, 1.98 MB) |
Oliver Watt-Meyer - Vulcan, Inc. |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Automatic Extraction of Internal Wave Signature from Multiple Satellite Sensors based on Deep Convolutional Neural Networks, (PDF, 9.53 MB) |
Shuangshang Zhang - University of Maryland Eastern Shore |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Development of machine learning based downscaling methods for wildfire risk, (PDF, 1.37 MB) |
Rackhun Son - Gwangju Institute of Science and Technology |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Combining spatio-temporal weather and crop data for network-based inference on the international wheat trade, (PDF, 1.62 MB) |
Srishti Vishwakarma - University of Maryland Center for Environmental Science Appalachian Laboratory |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| MLOps platforms to address the complexities of delivering a ML/AI product, (PDF, 1.13 MB) |
Pamela Perez - GAMA-1 Technologies |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones, (PDF, 1.62 MB) |
Trey McNeely - Carnegie Mellon University |
Session 23: Poster Session II,
Tuesday, 15 December 2020 |
| A Deep Learning Approach for Intelligent Thinning of Satellite Data, (PDF, 11.46 MB) |
Sarvesh Garimella - ACME AtronOmatic |
Session 24: AI/ML for Environmental Data, Image, and Signal Processing, Part 3
Chairs: Harry Cikanek (NOAA/NESDIS, STAR Director), Xiaoming Liu (NOAA/NESDIS/STAR)
,
Thursday, 17 December 2020 |
| Automation-assisted segmentation to expedite 3D coral mapping, (PPTX, 509.34 MB) |
Hugh Runyan - SIO/UCSD |
Session 24: AI/ML for Environmental Data, Image, and Signal Processing, Part 3
Chairs: Harry Cikanek (NOAA/NESDIS, STAR Director), Xiaoming Liu (NOAA/NESDIS/STAR)
,
Thursday, 17 December 2020 |
| A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology, (PPTX, 18.32 MB) |
Mark Veillette - MIT Lincoln Laboratory |
Session 24: AI/ML for Environmental Data, Image, and Signal Processing, Part 3
Chairs: Harry Cikanek (NOAA/NESDIS, STAR Director), Xiaoming Liu (NOAA/NESDIS/STAR)
,
Thursday, 17 December 2020 |
| Precipitation downscaling using conditional super-resolution based deep neural network., (PPTX, 13.69 MB) |
Jiali Wang - Argonne National Laboratory |
Session 24: AI/ML for Environmental Data, Image, and Signal Processing, Part 3
Chairs: Harry Cikanek (NOAA/NESDIS, STAR Director), Xiaoming Liu (NOAA/NESDIS/STAR)
,
Thursday, 17 December 2020 |
| Using Deep Learning to Generate Synthetic Radar Fields from GOES ABI and GLM, (PPTX, 96.16 MB) |
Kyle Hilburn - CIRA/CSU |
Session 25: AI/ML for Data Fusion/Assimilation, Part 2
Chairs: Steve Penny (NOAA PSD/CIRES), Kayo Ide (UMD),
Thursday, 7 January 2021 |
| Deep Multi-Sensor Domain Adaptation on Active and Passive Satellite Remote Sensing Data, (PDF, 3.56 MB) |
Sanjay Purushotham - UMBC |
Session 25: AI/ML for Data Fusion/Assimilation, Part 2
Chairs: Steve Penny (NOAA PSD/CIRES), Kayo Ide (UMD),
Thursday, 7 January 2021 |
| A satellite-station blended daily surface air temperature dataset for the Tibetan Plateau, (PDF, 8.41 MB) |
Yuhan (Douglas) Rao - CISESS/NCICS/NCSU |
Session 25: AI/ML for Data Fusion/Assimilation, Part 2
Chairs: Steve Penny (NOAA PSD/CIRES), Kayo Ide (UMD),
Thursday, 7 January 2021 |
| Retrieving Chlorophyll concentration from GOES-16 ABI using Deep Learning Techniques, (PDF, 3.7 MB) |
Guangming Zheng - NOAA/NESDIS/STAR |
Session 26: AI/ML for Information Extraction from Data, Part 2
Chairs: Shannon Rankin (Southwest Fisheries Science Center, NMFS), Matt Dornback (NOAA/OAR/OER),
Thursday, 21 January 2021 |
| Kick: Shift-N-Overlap Cascades of Transposed Convolutional Layer for Better Autoencoding Reconstruction on Remote Sensing Imagery, (PDF, 8.83 MB) |
Seungkyun Hong - Korea Institute of Science and Technology Information |
Session 26: AI/ML for Information Extraction from Data, Part 2
Chairs: Shannon Rankin (Southwest Fisheries Science Center, NMFS), Matt Dornback (NOAA/OAR/OER),
Thursday, 21 January 2021 |
| Deriving Fire Radiative Power from Numerical Weather Models and Satellites using Machine Learning Methods, (PPTX, 65.28 MB) |
Christina Kumler - CIRES/NOAA/GSL |
Session 27: AI/ML for Information Extraction from Data, Part 3
Chairs:Guangming Zheng (NOAA/NESDIS/STAR),
Thursday, 28 January 2021 |
| Effects of Balancing Dataset on Support Vector Machine Performance for Tropical Cyclone Intensity Predictions, (PPTX, 1.7 MB) |
Mu-Chieh Ko - NOAA/AOML/HRD |
Session 27: AI/ML for Information Extraction from Data, Part 3
Chairs:Guangming Zheng (NOAA/NESDIS/STAR),
Thursday, 28 January 2021 |
| What can we learn from Random Forest in the context of the tropical cyclone rapid intensification problem?, (PPTX, 22.36 MB) |
Chris Slocum - NOAA/NESDIS/STAR |
Session 27: AI/ML for Information Extraction from Data, Part 3
Chairs:Guangming Zheng (NOAA/NESDIS/STAR),
Thursday, 28 January 2021 |
| Cloud Cover Nowcasts from Process-Based Statistical Models, (PPTX, 5.5 MB) |
Chuyen Nguyen - Naval Research Laboratory |
Session 28: Machine Learning Tools and Best Practices, Part 2,
Thursday, 4 February 2021 |
| Radiant MLHub: Advancing Utilization of AI Applications on Earth Observations with Benchmark Training Datasets, (PDF, 57.59 MB) |
Hamed Alemohammad - Radiant Earth Foundation |
Session 28: Machine Learning Tools and Best Practices, Part 2,
Thursday, 4 February 2021 |
| Convolutional Neural Networks for Hydrometeor Classification using Dual Polarization Doppler Radars, (PPTX, 45.12 MB) |
Jitendra Kumar - Oak Ridge National Laboratory |
Session 29: AI/ML for Environmental Data, Image, and Signal Processing, Part 4,
Thursday, 11 February 2021 |
| Machine Learning for Earth Science Data Systems, (PDF, 4.38 MB) |
Manil Maskey - NASA |
Session 29: AI/ML for Environmental Data, Image, and Signal Processing, Part 4,
Thursday, 11 February 2021 |
| CoralNet: AI for Automatic Annotation of Benthic Imagery, (PDF, 24.95 MB) |
David Kriegman - UCSD |
Session 29: AI/ML for Environmental Data, Image, and Signal Processing, Part 4,
Thursday, 11 February 2021 |
| How NOAA Fisheries Leveraged Competitions and Collaboration to Automate the Identification of Right Whales using Deep Learning, (PPTX, 12.7 MB) |
Christin Khan - NOAA/NMFS/NEFSC/READ/PSB |
Session 29: AI/ML for Environmental Data, Image, and Signal Processing, Part 4,
Thursday, 11 February 2021 |
| Mapping Arctic Vegetation using Hyperspectral Airborne Remote Sensing Data, (PDF, 21.74 MB) |
Venkata S. Konduri - Northeastern University & Oak Ridge National Laboratory |
Session 30: AI/ML for Environmental Data, Image, and Signal Processing, Part 5,
Thursday, 18 February 2021 |
| Deep learning-based precipitation retrieval using passive microwave observations, (PDF, 4.17 MB) |
Yeji Choi - SI Analytics |
Session 30: AI/ML for Environmental Data, Image, and Signal Processing, Part 5,
Thursday, 18 February 2021 |
| A spatiotemporal quantification of the relative importance of indicator inputs for drought estimation, (PDF, 2.26 MB) |
Soni Yatheendradas - UMD/ESSIC & NASA/GSFC |
Session 30: AI/ML for Environmental Data, Image, and Signal Processing, Part 5,
Thursday, 18 February 2021 |
| Development of a Machine Learning-Based Radiometric Bias Correction for NOAA's Microwave Integrated Retrieval System (MiRS), (PPTX, 12.85 MB) |
Yan Zhou - UMD/ESSIC/CISESS |
Session 30: AI/ML for Environmental Data, Image, and Signal Processing, Part 5,
Thursday, 18 February 2021 |
| Radar Reflectivity Surface Rainfall Retrieval with cGAN Algorithm: An Idealized Study, (PPTX, 9.54 MB) |
Shujia Zhou - NASA GSFC |
Session 30: AI/ML for Environmental Data, Image, and Signal Processing, Part 5,
Thursday, 18 February 2021 |
| Leveraging Azure AI in Environmental Sciences, (PDF, 10.52 MB) |
Brian Keith - Microsoft |
Session 31: Tutorial - Microsoft,
Tuesday, 23 February 2021 |
| NOAA Fish Detector using AI: Fish species population management, (PDF, 2.97 MB) |
Anusua Trivedi - Microsoft |
Session 32: Tutorial - Microsoft,
Wednesday, 24 February 2021 |
| Energy efficiency and security aspects of Smart Homes, (PPTX, 37.35 MB) |
Olivera Kotevska - Oak Ridge National Laboratory |
Session 33: AI for Innovation: New Ways to Exploit Environmental Data, Part 2
Chairs: Forrest M. Hoffman (Oak Ridge National Laboratory), Soni Yatheendradas (UMD/ESSIC & NASA/GSFC),
Thursday, 25 February 2021 |
| Benefits of modeling interdependent environmental variables, streamflow and stream temperature, with deep learning |
Negin Hayatbini - Scripps/CW3E/UCSD |
Session 33: AI for Innovation: New Ways to Exploit Environmental Data, Part 2
Chairs: Forrest M. Hoffman (Oak Ridge National Laboratory), Soni Yatheendradas (UMD/ESSIC & NASA/GSFC),
Thursday, 25 February 2021 |
| Benefits of modeling interdependent environmental variables, streamflow and stream temperature, with deep learning, (PDF, 1.93 MB) |
Jeffrey Sadler - USGS |
Session 33: AI for Innovation: New Ways to Exploit Environmental Data, Part 2
Chairs: Forrest M. Hoffman (Oak Ridge National Laboratory), Soni Yatheendradas (UMD/ESSIC & NASA/GSFC),
Thursday, 25 February 2021 |
| Workshop Science Committee Panel: Achieving Efficiency and Added Value in Environmental Science Through AI: The power of Govt/Academia/Private Partnership, (PDF, 5.28 MB) |
Science Committee Panelists: Sid Boukabara (NOAA/NESDIS/STAR) Chair; Vladimir Krasnopolsky (NOAA/NWS/NCEP) Co-Chair; Jebb Stewart (NOAA/OAR/ESRL); Nikunj Oza (NASA/Ames and NASA/ESTO) ; Greg Dusek (NOS); Amy McGovern (Univ. Of Oklahoma, AMS AI Committee Member); Allen Huang (Univ. Of Wisconsin); Phillip Tissot (Texas A&M-Corpus Christi, AMS AI Committee Member); Sue E. Haupt (NCAR); Jason Hickey (Google Inc.); John Williams (The Weather Company, an IBM Business); David Hall (NVIDIA) |
Session 33: AI for Innovation: New Ways to Exploit Environmental Data, Part 2
Chairs: Forrest M. Hoffman (Oak Ridge National Laboratory), Soni Yatheendradas (UMD/ESSIC & NASA/GSFC),
Thursday, 25 February 2021 |
AI 2020 Recordings Index
| Title |
Session |
| Overview Talks, Part 1, (MP4, 1010.75 MB) |
Session 1,
30-Jul-20 |
Fundamentals of AI, Part 1
Chairs: Dave Turner (NOAA. ESRL), Jebb Stewart (NOAA, ESRL), (MP4, 671.68 MB) |
Session 2,
6-Aug-20 |
Looking Ahead (Using AI for NOAA mission), Part 1
Chairs: Bill Michaels (NOAA, NMFS), John Ten Hoeve (Office of Organizational Excellence), (MP4, 968.72 MB) |
Session 3,
13-Aug-20 |
AI/ML for Post-Processing and Data dissemination, Part 1
Chairs: Greg Dusek (NOAA/NOS), Andre van der Westhuysen (IMSG at NWS/NCEP/EMC) |
Session 4,
20-Aug-20 |
AI/ML for Environmental Data, Image, and Signal Processing, Part 1
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD), (MP4, 454.88 MB) |
Session 5,
27-Aug-20 |
AI/ML for Information Extraction from Data, Part 1
Chairs: Philippe Tissot (Texas A&M University, Corpus Christi), Jebb Stewart (NOAA, ESRL) |
Session 6,
3-Sep-20 |
Fundamentals of AI, Part 2
Chairs: Amy McGovern (OU), David Hall (NVIDIA), (MP4, 477.56 MB) |
Session 7,
10-Sep-20 |
Machine Learning Tools and Best Practices, Part 1
Chairs: Sue Haupt (NCAR), Jason Hickey (Google), (MP4, 683.88 MB) |
Session 8,
17-Sep-20 |
| Tutorial 1: Tutorial on Video and Image Analytics for Marine Environments (VIAME), a Do-It-Yourself AI Toolkit, (MP4, 178.54 MB) |
Session 9,
22-Sep-20 |
AI/ML for Post-Processing and Data dissemination, Part 2
Chairs: Nikunj Oza (NASA), Allen Huang (UW-Madison), (MP4, 506.46 MB) |
Session 10,
24-Sep-20 |
Poster Session I
Chair: Kevin Garrett (NOAA/NESDIS/STAR), (MP4, 444.76 MB) |
Session 11,
29-Sep-20 |
AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 1
Chairs: Vladimir Krasnopolsky (NOAA/NCEP/EMC), Kayo Ide (UMD), (MP4, 685.3 MB) |
Session 12,
1-Oct-20 |
AI/ML for Data Fusion/Assimilation, Part 1
Chairs: Peter Jan van Leeuwen (CSU), Steve Penny (NOAA PSD/CIRES), (MP4, 750.87 MB) |
Session 13,
15-Oct-20 |
| Tutorial 2: Learning the Fundamentals of Machine Learning through Forecasting El Niño, (MP4, 125.3 MB) |
Session 14,
20-Oct-20 |
AI for Innovation: New Ways to Exploit Environmental Data, Part 1
Chairs:Christina Kumler (CIRES/NOAA/GSL), Jeremy McGibbon (Vulcan), (MP4, 515.19 MB) |
Session 15,
22-Oct-20 |
AI/ML for Post-Processing and Data Dissemination, Part 3
Chairs: John K. Williams (The Weather Company, an IBM Business), Maike Sonnewald (NOAA/GFDL), (MP4, 609.36 MB) |
Session 16,
29-Oct-20 |
AI/ML for Post-Processing and Data dissemination, Part 4
Chairs: Andre van der Westhuysen (IMSG at NWS/NCEP/EMC), William Collins (LBNL, UC Berkeley), (MP4, 762.58 MB) |
Session 17,
5-Nov-20 |
| Tutorial 3: A Practical Introduction to Deep Learning for the Earth System Sciences using PyTorch, (MP4, 166.72 MB) |
Session 18,
10-Nov-20 |
AI/ML for Environmental Data, Image, and Signal Processing, Part 2
Chairs: Imme Ebert-Uphoff (CIRA), Ryan Lagerquist (CIRA/NOAA-GSD), (MP4, 214.89 MB) |
Session 19,
12-Nov-20 |
Looking Ahead (Using AI for NOAA mission), Part 2
Chairs: Michael Pavolonis (NESDIS/STAR), Philippe Tissot (Texas A&M University, Corpus Christi), (MP4, 758.86 MB) |
Session 20,
19-Nov-20 |
| Tutorial 4: Traditional Machine Learning Pipeline Applied to NWP Model Data, (MP4, 112.67 MB) |
Session 21,
1-Dec-20 |
AI/ML for Models Parameterization, Emulation, and Hybrid Model/AI Construct, Part 2
Chairs: Likun Wang (ESSIC, University of Maryland), Ashesh Chattopadhyay (Rice University), (MP4, 513.47 MB) |
Session 22,
3-Dec-20 |
| Poster Session II, (MP4, 192.55 MB) |
Session 23,
15-Dec-20 |
AI/ML for Environmental Data, Image, and Signal Processing, Part 3
Chairs: Harry Cikanek (NOAA/NESDIS, STAR Director), Xiaoming Liu (NOAA/NESDIS/STAR), (MP4, 657.89 MB) |
Session 24,
17-Dec-20 |
AI/ML for Data Fusion/Assimilation, Part 2
Chairs: Steve Penny (NOAA PSD/CIRES), Kayo Ide (UMD), (MP4, 640.42 MB) |
Session 25,
7-Jan-21 |
AI/ML for Information Extraction from Data, Part 2
Chairs: Shannon Rankin (Southwest Fisheries Science Center, NMFS), Matt Dornback (NOAA/OAR/OER), (MP4, 273.92 MB) |
Session 26,
21-Jan-21 |
AI/ML for Information Extraction from Data, Part 3
Chairs:Guangming Zheng (NOAA/NESDIS/STAR), (MP4, 670.05 MB) |
Session 27,
28-Jan-21 |
| Machine Learning Tools and Best Practices, Part 2, (MP4, 334.85 MB) |
Session 28,
4-Feb-21 |
| AI/ML for Environmental Data, Image, and Signal Processing, Part 4, (MP4, 538.84 MB) |
Session 29,
11-Feb-21 |
| AI/ML for Environmental Data, Image, and Signal Processing, Part 5, (MP4, 521.43 MB) |
Session 30,
18-Feb-21 |
| Tutorial 5 - Microsoft, (MP4, 99.86 MB) |
Session 31,
23-Feb-21 |
| Tutorial 6 - Microsoft, (MP4, 75.27 MB) |
Session 32,
24-Feb-21 |
AI for Innovation: New Ways to Exploit Environmental Data, Part 2
Chairs: Forrest M. Hoffman (Oak Ridge National Laboratory), Soni Yatheendradas (UMD/ESSIC & NASA/GSFC), (MP4, 674.64 MB) |
Session 33,
25-Feb-21 |
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