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23 April 2019
Time |
Presentations / Topics |
Speaker |
Affiliation |
0700 - 0830 |
Registration |
0830 - 0940 |
Plenary Session |
0830 - 0835 |
Workshop logistics |
Kevin Garrett |
NOAA/NESDIS |
0835 - 0845 |
Welcoming remarks and introduction of keynote speakers |
Harry Cikanek |
Director NOAA/NESDIS/STAR |
0845 - 0900 |
NOAA Priorities and Plans, Keynote Speaker |
Dr. Neil Jacobs |
NOAA Administrator |
0900 - 0910 |
Perspectives on Exploiting AI / ML in NESDIS |
Dr. Steve Volz |
Assistant Administrator of NOAA/NESDIS |
0910 - 0920 |
Thoughts on Exploiting AI / ML in the National Weather Service (NWS) |
Dr. Louis Uccellini |
Assistant Administrator of NOAA/NWS |
0920 - 0940 |
NOAA Data Strategy and AI Potential Contribution, Keynote Speaker |
Dr. Ed Kearns |
NOAA Chief Data Officer |
0940 - 1010 |
Leadership Panel Discussion Facilitator: Harry Cikanek (Director, NOAA/NESDIS/STAR) |
1010 - 1030 |
Coffee break |
1030 - 1250 |
Session 1 (S1) - Overview Talks Chairs: Amy McGovern (University of Oklahoma) and Kenneth Casey (NOAA/NCEI) |
1030 - 1050 |
The AMS AI Committee: 25 Years and counting of supporting the community, (PPTX, 27.69 MB) |
Philippe Tissot* |
Corpus Christi, Texas A&M University |
1050 - 1110 |
Overview of NOAA AI activities in Satellite Observations and NWP: Status and Perspectives, (PPTX, 62.13 MB) |
Sid Boukabara |
NOAA/NESDIS |
1110 - 1130 |
Use of AI in Forecasting and Decision Services at The Weather Company, (PPTX, 41 MB) |
John K. Williams* |
The Weather Company, IBM Business |
1130 - 1150 |
Artificial Intelligence Applications at NCAR, (PPTX, 22.09 MB) |
Sue Ellen Haupt* |
NCAR |
1150 - 1210 |
Opportunities for using AI methods in weather forecasting at ECMWF, (PPTX, 3.37 MB) |
Alan Geer* |
ECMWF |
1210 - 1230 |
Alphabet AI and Weather, (PPTX, 64.75 MB) |
Jason Hickey* |
Google |
1230 - 1250 |
AI for Science: Applications of NVIDIA GPUs for Numerical Weather Prediction, (PPTX, 86.72 MB) |
David Hall* |
NVIDIA |
1250 - 1255 |
Group photo |
1255 - 1350 |
Lunch break |
1350 - 1450 |
1st Poster Session (1P) |
1450 - 1610 |
Session 2 (S2) - Satellite Earth Observations and Applications - Part I Chairs: David Hall (NVIDIA) and Sid Boukabara (NOAA/NESDIS) |
1450 - 1510 |
Combining the power of experts and machine learning to explore NASA and NOAA data: a few examples, (PPTX, 35.95 MB) |
Tianle Yuan* |
UMBC JCET/NASA GSFC |
1510 - 1530 |
Recent Application of Machine Learning Techniques to Environmental Science at PNNL, (PPTX, 11.25 MB) |
Philip Rasch* |
PNNL |
1530 - 1550 |
Preparing AI-Enabled Weather and Environment Satellite Big Data, (PPTX, 6.1 MB) |
Allen Huang |
SSEC/CIMSS |
1550 - 1610 |
Development of Convolutional Neural Networks for Ice and Flood Detection from Synthetic Aperture Radar (SAR), (PPTX, 44.16 MB) |
Sean Helfrich |
NOAA/NESDIS |
1610 - 1630 |
Coffee break |
1630 - 1730 |
1st Panel Discussion
Facilitator: Allen Huang (University of Wisconsin) |
1630 - 1730 |
Topic: How can scientists and engineers embrace AI technology to enhance their work? |
Panel Members:- Kayo Ide (Univ. Of Maryland)
- Amy McGovern (Univ. Of Oklahoma)
- Philippe Tissot (Texas A&M - Corpus Christi)
- Sue Ellen Haupt (NCAR)
- John Williams (The Weather Company, IBM Business)
- David Hall (NVIDIA)
|
|
1830 - 2030 |
No-host Social Dinner (Location TBD) |
|
|
5-14-2019 6:31 pm
24 April 2019
Time |
Presentations / Topics |
Speaker |
Affiliation |
0830 - 1010 |
Session 2 (S2) - Satellite Earth Observations and Applications - Part II Chairs: Sue Ellen Haupt (NCAR) and Jason Hickey (Google) |
0830 - 0850 |
Improving Processing and Extracting Value from Satellite Observations through Deep Learning, (PPTX, 34.38 MB) |
Jebb Stewart |
NOAA/OAR |
0850 - 0910 |
Democratizing machine learning through AI for Earth's API framework, (PPTX, 53.51 MB) |
Patrick Flickinger |
Microsoft, AI for Earth |
0910 - 0930 |
Using Deep Learning to Extract Regions of Interest from Satellite Data, (PPTX, 111.21 MB) |
Christina Kumler |
NOAA/OAR |
0930 - 0950 |
Quantitative Precipitation Estimate Results from a Convolutional Neural Network Machine Learning Model, (PPTX, 3.17 MB) |
Micheal Simpson |
CIMSS/NSSL |
0950 - 1010 |
A Deep Neural Network Perspective on Atmospheric Motion Vectors, (PDF, 1.73 MB) |
Fei He |
UCLA |
1010 - 1030 |
Coffee break |
1030 - 1210 |
Session 2 (S2) - Satellite Earth Observations and Applications - Part III Chairs: Jebb Stewart (NOAA/OAR/ESRL, CIRA) and Nikunj Oza (NASA/Ames and NASA/ESTO) |
1030 - 1050 |
EarthInsights: Parallel Clustering of Large Earth Science Datasets on the Summit Supercomputer, (PPTX, 30 MB) |
Sarat Sreepathi |
Oak Ridge National Lab |
1050 - 1110 |
Developing fine-scale snow cover fraction estimates using deep learning, (PDF, 4.43 MB) |
Soni Yatheendradas |
UMD/GSFC |
1110 - 1130 |
Intermediate Frame Interpolation to Improve Temporal Coverage of GOES-16/17, (PPTX, 352.89 MB) |
Thomas Vandal |
NASA Ames |
1130 - 1150 |
Deep learning for estimating land surface response with uncertainty: soil moisture and other opportunities, (PPTX, 9.93 MB) |
Chaopeng Shen |
Pennsylvania State University |
1150 - 1210 |
Deep learning estimation of tropical cyclone intensity from microwave satellite imagery, (PPTX, 7.96 MB) |
Tony Wimmers |
UW-CIMSS |
1210 - 1310 |
Lunch break |
1310 - 1410 |
2nd Poster Session (2P) |
1410 - 1550 |
Session 3 (S3) - Nowcasting and NWP data assimilation Chairs: Kevin Garrett (NOAA/NESDIS/STAR) and Kayo Ide (University of Maryland) |
1410 - 1430 |
Machine learning meets data assimilation, (PDF, 4.12 MB) |
Peter Jan van Leeuwen |
Colorado State University |
1430 - 1450 |
Improvement to hurricane track and intensity forecast by exploiting satellite data and machine learning, (PPTX, 18.31 MB) |
Narges Shahroudi |
RTi@NOAA/NESDIS |
1450 - 1510 |
Machine Learning Based Applications for Environmental Hazard Detection: Data vs. Actionable Information, User Evaluations, and Future Possibilities, (PPTX, 115.43 MB) |
Michael Pavolonis |
NOAA/NESDIS/STAR |
1510 - 1530 |
Development of Merged Cloud Forecasts from Satellite and Numerical Model Data, (PPTX, 5.22 MB) |
Jason Nachamkin |
Naval Research Laboratory |
1530 - 1550 |
Data driven numerical methods partial differential equations, (PPTX, 48.17 MB) |
Jason Hickey |
Google |
1550 - 1610 |
Coffee break |
1610 - 1730 |
Session 4 (S4) - Environmental and Numerical Modeling - Part I Chairs: Vladimir Krasnopolsky (NOAA/NWS/NCEP) and John Williams (The Weather Company, an IBM Business) |
1610 - 1630 |
Machine Learning for Climate Extremes: Training is Everything, (PPTX, 38.25 MB) |
William Collins* |
UC Berkeley |
1630 - 1650 |
Machine learning for moist physics parameterizations in weather and climate models, (PPTX, 10.05 MB) |
Christopher Bretherton* |
University of Washington |
1650 - 1710 |
Machine Learning for Predicting the Evolution of Large Complex Spatiotemporally Chaotic Systems, (PPTX, 2.13 MB) |
Edward Ott* |
UMD |
1710 - 1730 |
Training Neural Network Parameterizations with Near-Global Cloud-Resolving Models, (PPTX, 221.03 MB) |
Noah D Brenowitz |
University of Washington |
5-15-2019 11:18 am
25 April 2019
Time |
Presentations / Topics |
Speaker |
Affiliation |
0830 - 1010 |
Session 4 (S4): Environmental and Numerical Modeling - Part II Chairs: Pete Childs (Priogen Energy) and Philippe Tissot (Texas A&M-Corpus Christi, AMS AI Committee Member) |
0830 - 0850 |
Neural network applications in numerical modeling, (PPTX, 1.3 MB) |
Vladimir Krasnopolsky |
NOAA/NWS |
0850 - 0910 |
Atmospheric chemistry modeling and air quality forecasting using machine learning, (PPTX, 27.56 MB) |
Christoph Keller |
NASA/GMAO |
0910 - 0930 |
Improving reference evapotranspiration forecasting with numerical weather predictions, (PPTX, 1.92 MB) |
Hanoi Medina |
Auburn University |
0930 - 0950 |
Analog Forecast Models for Space Weather Predictions, (PPTX, 4.56 MB) |
Eric Kihn |
NOAA/NESDIS |
0950 - 1010 |
A deep learning model to improve WRF forecasts: a case study of temperature, relative humidity, and wind speed, (PPTX, 4.71 MB) |
Yunsoo Choi |
University of Houston |
1010 - 1030 |
Coffee break |
1030 - 1230 |
Session 5 (S5): Post-Forecast and Extreme Weather Chairs: John Ten Hoeve (NOAA/NWS Office of Org. Excellence) and Vladimir Krasnopolsky (NOAA/NWS) |
1030 - 1050 |
Using Machine Learning to Improve Prediction and Understanding of Convective Hazards, (PPTX, 29.14 MB) |
Amy McGovern* |
University of Oklahoma |
1050 - 1110 |
Leveraging AI for Tropical Cyclone Prediction, (PPTX, 15.12 MB) |
Stephanie Stevenson |
CIRA/NHC |
1110 - 1130 |
Hybrid AI hurricane forecasting system: deep learning ensemble approach and Kalman filter, (PDF, 2.45 MB) |
Yunsoo Choi |
University of Houston |
1130 - 1150 |
Nonlinear Wave Ensemble Averaging using Neural Networks, (PPTX, 6.64 MB) |
Ricardo Martins Campos |
University of Lisbon |
1150 - 1210 |
Nowcasting Lightning Events with a Cloud-based Deep Learning approach, (PPTX, 13.25 MB) |
Valliappa Lakshmanan |
Google |
1210 - 1230 |
Using Artificial Neural Networks to Improve CFS Week 3-4 Precipitation and 2 Meter Air Temperature Forecasts, (PPTX, 833 KB) |
Yun Fan |
NOAA/NWS |
1230 - 1330 |
Lunch break |
1330 - 1350 |
Live Survey from the audience (on recommendations and suggestions) |
1350 - 1500 |
Session VI: Conclusion General Discussion Facilitator: John Ten Hoeve (NOAA/NWS Office of Org Excellence) |
1350 - 1400 |
Special Talk: Building bridges between domain scientists and machine learning experts: the essential role of weather/climate scientists in machine learning collaborations , (PPTX, 417 KB) |
Imme Ebert-Uphoff |
Colorado State University |
1400 - 1500 |
Topic: Where do we go from here? |
Panel Members:- Imme Ebert-Uphoff (Colorado State University)
- Vladimir Krasnopolsky (NOAA/NWS/NCEP)
- Kenneth Casey (NOAA/NCEI)
- Jebb Stewart (NOAA/OAR/ESRL, CIRA)
- Nikunj Oza (NASA/Ames and NASA/ESTO)
- Jason Hickey (Google)
- Pete Childs (Priogen Energy)
|
|
1500 - 1700 |
Session VII: Parallel Sessions (Tutorials) Facilitator: Kevin Garrett (NOAA/NESDIS) |
1500 - 1700 |
How to do machine learning in the cloud: a fast intro to public data sets, Keras, TensorFlow, and Earth engine, (PPTX, 21.82 MB) |
Valliappa Lakshmanan |
|
1500 - 1700 |
Deep Learning in Python for Environmental Prediction |
David Gagne (NCAR) and Ryan Lagerquist (OU/CIMMS) |
|
9-09-2019 1:44 pm
List of Posters and Presenters
Title |
First author / presenter |
Affiliation |
Poster Session 1.P: Tuesday, April 23 |
|
|
Leveraging NWP for ML Coastal Predictions and Other Coastal ML Applications , (PDF, 1.27 MB) |
Philippe Tissot |
Texas A&M University/Corpus Christi |
Automatic Extraction of Internal Solitary Wave Signature in HIMAWARI-8 Images Based on Deep Convolutional Neural Networks , (PDF, 1.09 MB) |
Shuangshang Zhang |
University of Maryland Eastern Shore |
Separation of Forecast Wind Wave Systems Using K-Means Clustering , (PDF, 3.35 MB) |
Andre van der Westhuysen |
IMSG at NOAA/NWS |
Improvements in Remotely-Sensed Cloud Property Estimation using Simple Machine Learning Models, (PDF, 38.56 MB) |
Charles H. White |
University of Wisconsin/CIMSS |
Machine learning approach to understanding vegetation distribution and dynamics using high resolution remote sensing |
Jitendra Kumar |
Oak Ridge National Laboratory |
Nighttime sea-fog detection from geostationary satellite using machine learning , (PDF, 1.74 MB) |
Dae Sung Choi, Seyun Min, Jae Kim |
Pusan National University |
AI and Meteorology/Remote Sensing Applications Research at PGCAP/INPE, (PDF, 1.76 MB) |
Rafael Santos |
Brazilian National Institute for Space Research |
A Fully Connected Scheme for Detection of Convective Rain from Satellite Passive Microwave Measurements |
Veljko Petkovic |
CSU/UMD |
The new machine learning toolbox within NOAA's Microwave Integrated Retrieval System |
Ryan Honeyager |
IMSG at NOAA/NESDIS |
Development and application of a 33-year daily multi-layer cropland soil moisture dataset in China using machine learning |
Yaling Liu |
Columbia University |
An improved ATMS-based Hurricane Warm Core Animation System (HWCAS) Using Convolutional Neural Network Method , (PDF, 1.17 MB) |
Ding Liang |
GST |
Interpretable AI for Deep-Learning-Based Meteorological Applications, (PDF, 1.59 MB) |
Eric Wendoloski |
The Aerospace Corporation |
Improving Validation Performance of VIIRS Radiometric Measurements with ICVS Machine Learning-based Clear-Sky Mask Algorithm |
Xingming Liang |
GST Inc. |
AI for water infrastructure mapping in Africa, (PDF, 1.54 MB) |
Pamela Collins |
Conservation International |
Dimensionality Reduction for Fast and Accurate Radiative Transfer , (PDF, 9.26 MB) |
Patrick Stegmann |
JCSDA |
Airmass Properties from GOES ABI Using Machine Learning , (PDF, 9.98 MB) |
Kyle Hilburn |
CIRA/CSU |
Exploring AI Capabilities with JPSS/STAR Integrated Calibration/Validation Syste |
Banghua Yan |
NOAA/NESDIS/STAR |
Retrieving aerosol optical depth over land from satellites by using a machine learning algorithm to build the relationships between surface reflectance at different wavelengths |
Tianning Su |
UMD |
How much better can we do in identifying aerosols and distinct cloud types from radiometers by harnessing recent advances in deep learning methods? |
Willem Marais |
University of Wisconsin/CIMSS |
Machine Learning for Winter Precipitation Type Supervised by Citizen Science (mPING) Observations Plus a New Tornado Detection Algorithm for the WSR-88D Dual-Polarization Radar |
Kim Elmore |
SSEC/CIMSS |
Reconstruction of missing data in GOCI AOD using a deep learning algorithm , (PDF, 582 KB) |
Yunsoo Choi |
University of Houston |
Leveraging deep learning hyperparameter tuning frameworks for intelligent WRF ensembles, (PDF, 1.2 MB) |
Derek D. Jensen |
Lawrence Livermore National Laboratory |
Enterprise Validation to Characterize AI Products Performance |
Anthony L Reale |
NOAA/NESDIS |
GSICS Action Tracker: A Supervised Machine Learning Content extraction tool on Google Cloud, (PDF, 447 KB) |
Manik Bali |
UMD/ESSIC |
Improving Atmospheric River Forecasts with Convolutional Neural Networks |
William Chapman |
Oceanography |
List of Posters and Presenters
Title |
First author / presenter |
Affiliation |
Poster Session 2.P: Wednesday, April 24 |
|
|
Spatio-Temporal Climate Causality Analytics, (PDF, 542 KB) |
Jianwu Wang |
UMBC |
Stochastic PDE model identification from partial noisy data , (PDF, 6.32 MB) |
Fei Lu |
Johns Hopkins University |
Creating Synthetic Weather Radar Images Using Convolutional Neural Networks , (PDF, 7.06 MB) |
Christopher Mattioli |
MIT Lincoln Laboratory |
Satellite Imaging Techniques for the Analysis of High-Impact Historical Severe Thunderstorm Events, (PDF, 3.63 MB) |
Kenneth Pryor |
NOAA/NESDIS |
The improvement of predictability for very short range precipitation using Micro-genetic algorithm |
Jiyeon Jang |
KMA |
INPE Nowcasting system: Steps toward an automatic severe storm forecasting system in Brazil , (PDF, 2.1 MB) |
Alan James Peixoto Calheiros |
Brazilian National Institute for Space Research |
Assessing the variability of VIIRS Day/Night Band observed nocturnal light sources for use in atmospheric retrievals via k-means clustering |
Jeremy Solbrig |
Colorado State University |
Improving hydrological simulations via the integration of remotely sensed data assimilation in coupled land surface and hydrologic model framework |
Chandana Gangodagamage |
NASA |
Modelling HPC workloads with (some) Machine Learning , (PDF, 1.33 MB) |
Antonino Bonanni |
ECMWF |
Convective Storm Nowcasting: Capabilities of Remote Sensing in Central Europe , (PDF, 914 KB) |
Patrik Benacek |
Charles University in Prague |
Atlantic Basin RI Prediction Enhancement through NWP Spatial Information |
Alexandria Grimes |
Mississippi State University |
Learning to automatically detect avalanche deposits from SAR satellite imagery , (PDF, 4.79 MB) |
Sophie Giffard-Roisin, Saumya Sinha |
University of Colorado |
Is your Model right today? |
Marina Frants, Dean Wakeham |
XST, Inc. |
Engaging Freshmen Undergraduates in Atmospheric Data Science. |
Alexandra Jones |
University of Maryland |
Advancing Predictive Understanding of Terrestrial Ecosystem through Machine Learning |
Dan Lu |
Oak Ridge National Laboratory |
Visceralization of Future Climate Change |
S. Karthik Mukkavilli |
Montreal Institute for Learning Algorithms |
Translating climate and remote sensing information into improved decision making in agriculture and water resources , (PDF, 2.27 MB) |
Di Tian |
Auburn University |
Interpretation of Deep-learning Models for Short-term Forecasting of Tornadogenesis , (PDF, 49.42 MB) |
Ryan Lagerquist |
University of Oklahoma |
A Study on the Bias Correction of the Surface Temperature Forecast using Recurrent Neural Networks |
KIM MANKI |
KMA |
Post-processing 12-36 hour multi-model ensemble PQPFs using a random forest |
Eric Loken |
University of Oklahoma/CIMMS |
Machine Learning Parameterizations from the Surface to the Clouds , (PDF, 4.06 MB) |
David Gagne |
University of Oklahoma |
Linking GOES-R observations and Multi-Radar/Multi-Sensor precipitation using unsupervised learning approach |
Shruti A Upadhyaya |
CIMMS |
TPUs, TensorFlow Research Cloud, and Mesh Tensorflow |
Peter Dolan |
Google/Deep Mind |
An Evaluation of Deep Learning Approaches for Nationwide Land Cover Mapping |
Chris Robinson |
Lynker Tech at the NOAA Office for Coastal Management |
Session Recordings
Day |
Session Recording Links |
Tuesday, 23 April |
- Plenary Session, (MP4, 72.08 MB)
- Leadership Panel Discussion, (MP4, 417.5 MB)
Facilitator: Harry Cikanek (Director, NOAA/NESDIS/STAR)
- Session 1 (S1) - Overview Talks, (MP4, 173.28 MB)
Chairs: Amy McGovern (University of Oklahoma) and Kenneth Casey (NOAA/NCEI)
- Session 2 (S2) - Satellite Earth Observations and Applications - Part I, (MP4, 414.68 MB)
Chairs: David Hall (NVIDIA) and Sid Boukabara (NOAA/NESDIS)
- 1st Panel Discussion, (MP4, 759 MB)
Facilitator: Allen Huang (University of Wisconsin)
|
Wednesday, 24 April |
- Session 2 (S2) - Satellite Earth Observations and Applications - Part II, (MP4, 115.9 MB)
Chairs: Sue Ellen Haupt (NCAR) and Jason Hickey (Google)
- Session 2 (S2) - Satellite Earth Observations and Applications - Part III, (MP4, 611 MB)
Chairs: Jebb Stewart (NOAA/OAR/ESRL, CIRA) and Nikunj Oza (NASA/Ames and NASA/ESTO)
- Session 3 (S3) - Nowcasting and NWP data assimilation, (MP4, 164.55 MB)
Chairs: Kevin Garrett (NOAA/NESDIS/STAR) and Kayo Ide (University of Maryland)
- Session 4 (S4) - Environmental and Numerical Modeling - Part I, (MP4, 78.95 MB)
Chairs: Vladimir Krasnopolsky (NOAA/NWS/NCEP) and John Williams (The Weather Company, an IBM Business)
|
Thursday, 25 April |
|
*Invited Talk
|