Program: 23rd Conference on Artificial Intelligence for Environmental Science (104th AMS Annual Meeting)

Program Chairs: Christina E. Kumler , CIRA ; Aaron J. Hill , Colorado State University ; Kyle A. Hilburn

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Tuesday, 23 January 2024

1:00 PM-2:00 PM: Tuesday, 23 January 2024

Virtual Poster Slam Session #1

Hosts: (Joint between the Virtual Posters; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 23rd Conference on Artificial Intelligence for Environmental Science; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 14th Conference on Transition of Research to Operations; and the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones )

Cochairs: Gary B. McWilliams, EUMETSAT; Kyle A. Hilburn; Chanh Q Kieu; Robert G. Nystrom; James G. Yoe, NOAA/NWS/NCEP/Joint Center for Satellite Data Assimilation

20OESS Status of Joint Polar Satellite System (JPSS) and Low Earth Orbit (LEO) Satellite Products and Data Access

Lihang Zhou, NOAA, Lanham, MD

Handout (1.7 MB)

20OESS Optimizing Earth Observing Constellations of Satellite Sensors Using ASPEN: A Proof-of-Concept Study for Global NWP and Nowcasting Applications

Ross N. Hoffman, NOAA, Cambridge, MA; and L. Lin, S. L. Bunin, and S. A. Boukabara

Manuscript (1.3 MB)

Handout (1.4 MB) Handout (2.6 MB)

20OESS The volcanic eruption observed by the satellite microwave measurements and the MiRS retrieved temperature profiles: The Hunga Tonga-Hunga Ha’apai Volcanic Eruption case

YONG-KEUN LEE, ESSIC/UMD, Ellicott City, MD; and N. Hindley, C. Grassotti, and Q. Liu

Handout (1.1 MB)

V3A

20OESS Simplifying and Enhancing the User Experience with Metadata Catalog as a Service

Mei-Ling Freeman, NOAA, Silver Spring, MD

14R2O Establishing JEDI-Based MOM6 3DVAR Ocean Data Assimilation Capability for the Regional Hurricane Analysis and Forecast System

Lewis James Gramer, University of Miami, Miami, FL; NOAA-AOML, Miami, FL; and Y. Li, H. S. Kim, H. kang, K. Bhargava, M. Aristizabal, J. D. Steffen, PhD, B. Li, B. Thomas, B. Liu, T. Sluka, G. Vernieres, Z. Zhang, and A. Mehra

23AI Spatial-temporal Convolution Neural Networks for Tropical Cyclone Detection from Geostationary Satellite Images

TRAN HUY Hoang, University of Engineering and Technology, Vietnam National University, Hanoi, Viet Nam; Viettel Group, Hanoi, Viet Nam; and L. Q. DAO, L. V. Hung, M. V. KHIEM, B. Q. Hung, T. D. Du, L. R. Hole, and K. H. Mai

23AI The Retrieval of Surface Pressure and Wind Speed over Ocean using NOAA-20 ATMS Measurements through a U-Net Machine Learning Algorithm

YONG-KEUN LEE, ESSIC/UMD, Ellicott City, MD; and Z. Liang, Q. Liu, C. Grassotti, and L. Lin

Handout (1.1 MB)

V7A

23AI An Updated Consensus Machine Learning Modeling Using the HAFS dataset for Hurricane Rapid Intensification (RI) Prediction

Mu-Chieh Ko, NOAA/OAR/AOML/HRD & UM/CIMAS, Miami, FL; and G. J. Alaka Jr.

24ARAM Industry Perspective of Weather Technology in the Cockpit (WTIC) Program Pilot Industry Survey

Tenny Albert Lindholm, NCAR, Westminster, CO

Manuscript (372.0 kB)

Handout (2.0 MB)

24ARAM A Historical Review on Aviation Turbulence in Our Changing Climate

Nicholas D. Amundsen, Federal Aviation Administration, Washington, DC; and T. J. Flowe

Handout (2.0 MB)

V10

24ARAM Statistical Analysis of Aviation Turbulence in the Middle–Upper Troposphere over Japan

Yoshiaki Miyamoto, Keio University, Fujisawa, Kanagawa, Japan; and A. Matsumoto and S. Ito

Handout (1.6 MB)

V11

6TROPICAL Effect of polydisperse spray on hurricane and severe storm dynamics

Yevgenii Rastigejev, North Carolina A&T State Univ., Greensboro, NC; and S. A. Suslov

Handout (4.5 MB)

V12

6TROPICAL The Development of a Observing System Simulation Experiment (OSSE) Capability for the Operational Hurricane and Analysis Forecast System (HAFS)

T. Connor Nelson, Ph.D. , UCAR, Boulder, CO; and L. Cucurull, S. P. F. Casey, and A. Vidal

Handout (6.9 MB)

V13

6TROPICAL Analysis of Trends in Tropical Cyclone Rapid Intensification Frequency with the Aid of a Machine Learning Model

Kuilin Zhu, Hong Kong University of Science and Technology, Hong Kong, Hong Kong, China; and H. Su

V14

6TROPICAL Role of midlatitude baroclinic condition in heavy rainfall events directly induced by tropical cyclones over the East Asia.

Eun Jeong CHA, KMA, Seoul, South korea; and C. Park, S. W. Son, Y. M. CHA, and H. C. LEE

Manuscript (4.0 MB)

Handout (6.8 MB)

V15

6TROPICAL Tropical Cyclone Intensification and Fullness: The Role of Storm Size Configuration

Xi Guo, Nanjing Univ., Nanjing, China; and Z. M. Tan

Handout (555.2 kB)

V16

6TROPICAL Evaluating the Influence of Assimilating Surface Drifter on Ocean Response During Tropical Cyclones

Luyu SUN, University of Maryland College Park, College Park, MD

Handout (6.1 MB)

V16A

6TROPICAL TRACKING MESOSCALE CONVECTIVE SYSTEM CORES, OVER NORTHERN SOUTH AMERICA, USING OVERLAPPING TECHNIQUE

GERARDO DE JESUS MONTOYA GAVIRIA II, universidad nacional de Colombia, BOGOTA, D.C., CUN, Colombia

Manuscript (1.9 MB)

Handout (652.5 kB)

V16B

6TROPICAL Prediction of Seasonal Tropical Cyclone Track Density over the western North Pacific

Woosuk Choi, Sejong University, Seoul, South korea

Handout (2.7 MB)

V17

26SATMET Global Biomass Burning Emissions Product for Operational Air Quality Forecasting

Xiaoyang Zhang, South Dakota State Univ., Brookings, SD; South Dakota State Univ., Brookings, SD; and F. Li, Y. Ye, and S. Kondragunta

Sunday, 28 January 2024

4:00 PM-5:15 PM: Sunday, 28 January 2024

Presidential Forum: Climate Science as Service to Society

Location: Ballroom III/ IV (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the Town Hall Meetings; the 12th AMS Conference for Early Career Professionals; the Presidential Conference; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

Moderator: Kerry A. Emanuel, MIT

Panelists: Bob Inglis, republicEn.org; Monica P. Medina, Wildlife Conservation Society (WCS)

6:30 PM-8:30 PM: Sunday, 28 January 2024

AMS Career Resource & Graduate School Fair

Location: Hall D (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the 23rd Annual Student Conference; the 12th AMS Conference for Early Career Professionals; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

Annual Meeting Welcome Reception

Location: Hall E (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the 23rd Annual Student Conference; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the 12th AMS Conference for Early Career Professionals; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

Monday, 29 January 2024

8:30 AM-10:00 AM: Monday, 29 January 2024

Session 1A

Pure AI and Data-Driven Weather Forecasts I: Innovative Model Architectures and Applications

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Christina E. Kumler, CIRA; Daniel Rothenberg

8:30 AM

1A.1

Evaluation of Data-Driven Medium Range Weather Prediction Using Reanalysis Data

Shun Yao, Colorado State University, Fort Colins, CO; and H. Chen, J. Tang, PhD, and V. Chandrasekar

8:45 AM

1A.2

High-Frequency Nowcasting from Geostationary Satellites with End-to-End Emulation

Thomas J Vandal, Zeus AI, Arlington, MA; and K. Duffy and D. McDuff

9:00 AM

1A.3

WeatherX: Learning to Forecast Diagnostic Parameters Using Dense Representations

Peetak Mitra, Excarta, San Francisco, CA; and V. Ramavajjala

9:15 AM

1A.4

Using Deep Learning for Coupled Atmosphere-Ocean Modelling

Nathaniel Alize Cresswell-Clay, University of Washington, Seattle, WA

9:30 AM

1A.5

Improving Deep Learning Weather Prediction Using the HEALPix Mesh

Dale R. Durran, Univ. of Washington, Seattle, WA; and M. Karlbauer, N. A. Cresswell-Clay, R. A. Moreno, T. Kurth, M. Bisson, and M. V. Butz

9:45 AM

1A.6

Huge Ensembles (HENS) of Weather Extremes using the Fourier Forecasting Neural Network (FourCastNet)

William D. Collins, LBNL, BERKELEY, CA; and A. Mahesh, M. Pritchard, N. D. Brenowitz, Y. Cohen, P. Harrington, K. Kashinath, T. A. O'Brien, J. Pathak, and S. Subramanian

J1B

AI Advances in Tropical Meteorology: Tropical Cyclones, Sub-Seasonal Phenomena, and More

Location: 338 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; and the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability )

Cochairs: Marie McGraw; Gregory S. Elsaesser, JPL

8:30 AM

J1B.1

Predicting Tropical Cyclone Track Forecast Errors Using a Probabilistic Neural Network

Martin Fernandez, Colorado State University, Fort Collins, CO; and E. Barnes and M. DeMaria

8:45 AM

J1B.2

Causal Feature Selection for Tropical Cyclone Intensity Forecasting

Tom Georges Beucler, University of Lausanne, Lausanne, VD, Switzerland; and S. G. SUDHEESH, F. I. H. Tam, M. S. Gomez Jr., M. McGraw, M. DeMaria, K. Musgrave, A. Gerhardus, and J. Runge

9:00 AM

J1B.3

Statistical Characterization of Precipitation Events Collected from the GPM DPR

Yukari N. Takayabu, The University of Tokyo, Kashiwa, Chiba, Japan; and K. Hosotani

9:15 AM

J1B.4

Inference-Based Retrieval of Inner-Core Tropical Cyclone Surface Winds from Satellite-Based Microwave Imagers and Sounders

Anthony J. Wimmers, CIMSS, Univ. of Wisconsin−Madison, Madison, WI; and S. Griffin, D. C. Herndon, and C. S. Velden

9:30 AM

J1B.5

Evaluation of Typhoon Forecasts Using the Pangu-Weather Neural Network Model: A Comparative Analysis with ECMWF and Other Numerical Prediction Models

Yuanfu na Xie, Shenzhen Institute of Meteorological Innovation, Shenzhen, 44, China; and Z. Qin, Y. Wu, N. Wu, J. chen, X. Xu, F. Zheng, and H. Zhang

9:45 AM

J1B.6

Tropical Surface Analysis Using Deep Learning

William Downs, University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science, Miami, FL; and S. Majumdar and A. D. Mahoney

J1B

Tending the Treasure Trove: Advancing Stewardship for Nonsatellite Earth Observations

Location: 336 (The Baltimore Convention Center)

Cochairs: Stephanie M. Wingo, University of Alabama in Huntsville; Megan E. Buzanowicz; Geoffrey T. Stano, Global Hydrology Resource Center

8:30 AM

J1B.1

Blast from the Past: ASDC Curation for NASA Suborbital Legacy Missions to Promote Discovery and Accessibility

Megan E. Buzanowicz, ADNET Systems, Inc., Hampton, VA; and S. Leavor, N. Jester, G. Mojica, A. S. Porter, C. Gao, and J. Kusterer

8:45 AM

J1B.2

Extending CF Conventions to Enhance Data Fairness for Atmospheric Composition Observations

Sean Leavor, Atmospheric Science Data Center, Hampton, VA; and M. E. Buzanowicz, J. Kusterer, M. Shook, C. Gao, and M. Silverman

9:00 AM

J1B.3

Recent Updates and Next Steps for NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI)

Stephanie M. Wingo,

9:15 AM

J1B.4

An Overview of NASA’s Airborne and Field Data Resource Center

Sara H. Lubkin, NASA, Greenbelt, MD; NASA, Crofton, MD; and J. Brennan, D. K. Smith, S. M. Wingo, M. Thornton, M. Buzanowicz, B. Wilson, C. Gao, L. Sinclair, and G. T. Stano

9:30 AM

J1B.5

ASDC’s Python-Based Metadata Extraction Pipeline for Suborbital Campaigns

Abraham Stephen Porter, ADNET Systems, Inc., Hampton, VA; and N. Jester, M. E. Buzanowicz, S. Leavor, G. Mojica, J. Kusterer, and C. Gao

10:00 AM-10:45 AM: Monday, 29 January 2024

Coffee Break [Main Terrace (BCC), Camden Lobby (BCC), Hall E (BCC)]

Location: The Baltimore Convention Center

10:45 AM-12:00 PM: Monday, 29 January 2024

Session 2A

Artificial Intelligence for Environmental Science I

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Ryan Lagerquist; Rob Redmon; Philippe E. Tissot

10:45 AM

2A.1

2024 Update on the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Amy McGovern, NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography Univ. of Oklahoma (AI2ES), Norman, OK

11:00 AM

2A.2

Machine Integration and Learning for Earth Systems (MILES): Bridging Key Gaps in Machine Learning for Earth System Science

David John Gagne II, Ph.D., NCAR MILES, Boulder, CO; and J. Schreck, C. Becker, G. Gantos, T. Martin, W. Petzke, W. Chuang, W. E. Chapman, K. J. Mayer, M. J. Molina, J. T. Radford, C. D. Wirz, M. G. Cains, J. Demuth, O. V. Wilhelmi, R. E. Morss, and J. Anderson

11:15 AM

2A.3

A Benchmark Dataset for Statistical Downscaling of Meteorological Fields with Deep Neural Networks

Michael Langguth, Forschungszentrum Juelich, Juelich, Nordrhein-Westfalen, Germany; and S. Stadtler, B. Gong, and M. G. Schultz

11:30 AM

2A.4

Can AI Models Perform Dynamical Downscaling? Training Deep Generative Models to Downscale ERA5 Variables to High-Resolution COSMO-CLM Dynamical Reanalyses over Italy

Elena Tomasi, Fondazione Bruno Kessler - FBK, Trento, TN, Italy; and G. Franch and M. Cristoforetti

11:45 AM

2A.5

Uncovering the Drivers of Urban Air Pollution Using Spatial Machine Learning for Improved Analysis and Forecasting

Firas Gerges, Princeton University, Princeton, NJ; and M. Llaguno-Munitxa, M. A. Zondlo, M. Boufadel, and E. Bou-Zeid

Using Unbalanced Optimal Transport as an Image-to-Image Comparison Metric

Session 2B

Applying Uncertainty Quantification Methods to Environmental Artificial Intelligence Models

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Eric D. Loken, OU/CIMMS; Ryan Lagerquist

10:45 AM

2B.1

Machine-Learned Uncertainty Quantification Is Not Magic: Lessons Learned from Emulating Radiative Transfer with ML

Ryan A. Lagerquist, CIRA and NOAA/ESRL/GSL, Boulder, CO; and D. D. Turner, J. Q. Stewart, and I. Ebert-Uphoff

11:00 AM

2B.2

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Gabrielle Gantos, NCAR, Boulder, CO; and J. Schreck, D. J. Gagne II, C. Becker, W. E. Chapman, D. Kimpara, E. Kim, T. Martin, M. J. Molina, J. T. Radford, B. Saavedra, J. Willson, and C. D. Wirz

11:15 AM

2B.3

Explaining the Sources of Uncertainty in Machine Learning Winter Precipitation-Type Predictions

Charlie Becker, NCAR, Boulder, CO; and D. J. Gagne II, Ph.D., J. Schreck, G. Gantos, T. Martin, D. Kimpara, B. Saavedra, J. Willson, E. Kim, J. Demuth, C. D. Wirz, N. P. Bassill, K. J. Sulia, and A. McGovern

11:30 AM

2B.4

Uncertainty Estimation of Wind Gust Predictions Using the Deep Evidential Model

Israt Jahan, University of Connecticut, Storrs, CT; and M. Astitha, J. Schreck, and D. J. Gagne II

11:45 AM

2B.5

Uncertainty Quantifications of the Onset and Offset of Cold-Stunning Events Using AI Ensemble Methods

Hector Miguel Marrero Colominas, Texas A&M University-Corpus Christi, Corpus Christi, TX; NSF Artificial Intelligence Institute (AI2ES), Corpus Christi, TX; and M. Vicens-Miquel, P. E. Tissot, J. Woodall, C. Duff, and B. Colburn

12:00 PM-1:45 PM: Monday, 29 January 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Monday, 29 January 2024

Session 3

Explainable Artificial Intelligence (XAI) for Environmental Science I

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Christina E. Kumler, CIRA; Aaron J. Hill, Colorado State University

1:45 PM

3.1

Meteorological Interpretation of XAI Output Applied to a 3D Convolutional Neural Network Fog Prediction Model

Waylon G. Collins, NOAA/National Weather Service, Corpus Christi, TX; and E. Krell, P. E. Tissot, and S. A. King

2:00 PM

3.2

Identifying Data Sources and Physical Strategies Used By Neural Networks to Predict TC Rapid Intensification

Ryan A. Lagerquist, CIRA and NOAA/ESRL/GSL, Boulder, CO; and J. Knaff, C. Slocum, K. Musgrave, and I. Ebert-Uphoff

2:15 PM

3.3

Insights Gained from Explainable Artificial Intelligence for Convective Initiation Prediction

Da Fan, The Pennsylvania State Univ., State College, PA; and S. J. Greybush and D. J. Gagne II

2:30 PM

3.4

Human-in-the-Loop Explainable AI : A Case Study in Flood Response (Core Science Keynote)

Srinivasan Parthasarathy, Ohio State University, OH

J3C

Using Artificial Intelligence to Analyze Satellite Earth Observations I

Location: 338 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; and the 20th Annual Symposium on Operational Environmental Satellite Systems )

Cochairs: Haonan Chen, Cooperative Institute for Research in the Atmosphere (CIRA); Jebb Q. Stewart, ESRL

1:45 PM

J3C.1

Neural Network Estimates of Cloud Water Content Profiles from Passive Satellite Imagery

Charles White, CIRA, Fort Collins, CO; and Y. J. Noh, J. M. Haynes, and I. Ebert-Uphoff

2:00 PM

J3C.2

3-D Nowcasting of Clouds Using Artificial Intelligence

David Ryglicki, MyRadar, Lakeland, FL; and A. T. Wimmers and S. Garimella

Handout (3.1 MB)

2:15 PM

J3C.3

Retrieving 3D Cloud Radar Reflectivity from MODIS Cloud Products Based on CGAN

Yubao Liu, NUIST, Nanjing, Jiangsu, China; Nanjing University of Information and Science Technology, Nanjing, Jiangsu, China; and F. Wang, Y. Zhou, Y. Qin, and H. Fan

2:30 PM

J3C.4

Continual Lifelong Learning for Precipitation Retrievals Using ABI and GLM Measurements on the GOES-R Series

Yifan Yang, CIRA, Fort Collins, CO; and H. Chen and M. R. Azimi-Sadjadi

2:45 PM

J3C.5

Detecting Above-Anvil Cirrus Plumes on a Pixel Scale Using Deep Learning

Amanda Burke, Univ. of Oklahoma, Norman, OK; and C. R. Homeyer and A. McGovern

3:00 PM-3:40 PM: Monday, 29 January 2024

23rd Conference on Artificial Intelligence for Environmental Science Eposters (Monday)

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

AI’s Next Wave: Foundation Models, Generative Models, and LLMs for Weather and Climate Prediction

David Matthew Hall, NVIDIA, Lafayette, CO

Leveraging Machine Learning to Exploit SmallSats

Hui Christophersen, NRL, Monterey, CA; and D. Sidoti, B. Campbell, PhD, and E. Satterfield

3:00 PM-4:30 PM: Monday, 29 January 2024

Coffee Break & Formal Poster Viewing

Location: Hall E (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the 12th AMS Conference for Early Career Professionals; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

Posters I

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

130

Extreme Value Analysis of K-Means Single (k=1) Cluster Statistical Distances, a Heuristic Approach with Los Angeles and San Francisco Hourly Temperatures and Monthly Precipitation

Charles J. Fisk, Individual, Newbury Park, CA

Manuscript (1.3 MB)

131

Enhancing Predictability of Southern California Precipitation Using a Multi-Input Multi-Output Autoencoder Network

Hannah Bao, University of Maryland, College Park, MD; and L. S. Passarella, S. Mahajan, and M. J. Molina

Manuscript (1.1 MB)

132

Process-Constrained Bias Correction of NOAA UFS Subseasonal-to-Seasonal (S2S) Precipitation Forecasts through Machine Learning

Kyle Joseph Chen Hall, NOAA, Boulder, CO; CIRES, Boulder, CO; and L. Bengtsson

133

Exploring the Relative and Combined Contribution of the MJO and ENSO to Midlatitude Subseasonal Predictability with an Interpretable Neural Network

William Anthony Manriquez, UCAR, Boulder, CO; and W. E. Chapman and K. J. Mayer

134

Empowering Coastal Resilience: A Multi-Layer Perceptron Approach for Subseasonal-to-Seasonal Sea Level Predictions in the Gulf of Mexico

Marina Vicens Miquel, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and C. Radin, V. Nieves, P. Tissot, and A. Medrano, PhD

135

An Assessment of the S2S Forecast Skill of a Hybrid Model That Combines Machine Learning with an AGCM.

Dhruvit Patel, University of Maryland, College Park, MD

Handout (961.4 kB)

136

Denoising Raw Photon Counting Atmospheric Lidar Data Using Autoencoders

Patrick A. Selmer, SSAI, Lanham, MD; and J. E. Yorks and E. P. Nowottnick

Handout (2.0 MB)

137

Cloudstream: Long-term Cloud Detection Image Prediction Model Using Multi-Channel Satellite Images

Eunbin Cho, SIA(SI analytics), Daejeon, South korea; SI-Analytics, Daejeon, South korea; and E. Kim and Y. Choi

138

Deep Domain Adaptation Technique for Cloud Property Retrieval with Multiple Satellite Remote Sensor Data

Xin Huang, Towson University, Towson, MD

139

Machine Learning Classification of Single-Particle Mass Spectra

Jose Angel Perez Chavez, Howard University, Washington, DC; and M. A. Zawadowicz

143

Enhancing Air Quality Prediction through Bias Correction using Machine Learning on CMAQ

140

In Search of an Alternative to Ensembles for Estimating Uncertainty in Air Quality Modelling

144

Exploring Relationships Between Environmental Variables and Global Distribution of Particulate Inorganic Carbon versus Particulate Organic Carbon Using Random Forest Analysis

Rui Jin, Johns Hopkins University, Baltimore, MD; and A. Gnanadesikan

141

Generative Deep Learning Models for Air Quality Numeric Model Emulation

145

Predictive Modeling in Environmental Science with Machine Learning Algorithms

Michael Nia, DRI, RENO, NV; and G. Mehdizadeh, PhD student, R. Barjeste Vaezi, E. Erfani, and F. Hosseinpour, PhD

146

Post-Processing NWP Using Deep Learning with a Custom Loss Function to Create Ensemble Forecasts to Quantify Forecast Uncertainty

Ashley Elizabeth Payne, Tomorrow.io, Golden, CO; and L. Conibear, A. Reed Harris, T. McCandless, K. Keshavamurthy, M. E. Green, MA, and S. Flampouris

142

Deep Generative Modeling of Air Quality Tracers

147

Exploring Potential Bias in Data Selection & Processing for Artificial Intelligence in Environmental Sciences

Haley Perez, University of Oklahoma, Norman, OK

4:30 PM-5:45 PM: Monday, 29 January 2024

J4B

Developing Cloud-Based Tools for Data Analysis and Archiving I

Location: 336 (The Baltimore Convention Center)

Hosts: (Joint between the 40th Conference on Environmental Information Processing Technologies; the 23rd Conference on Artificial Intelligence for Environmental Science; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; and the 10th Symposium on High Performance Computing for Weather, Water, and Climate )

Cochairs: Nazila Merati, NESDIS; Monica Youngman

4:30 PM

J4B.1

Lightning in a Flash: A Cloud-Based, Open-Source Capability to Support Data Access and Analysis of Space-Based Lightning Observations

Navaneeth Selvaraj, Global Hydrometeorology Resource Center, Huntsville, AL; and A. Subedi, D. Acharya, G. T. Stano, W. Ellett, L. Sinclair, and A. Kaulfus

4:45 PM

J4B.2

Transitioning Science Team Development from On-Prem to the Cloud via the NESDIS Common Cloud Framework (NCCF)

Gian A. Villamil-Otero, NOAA, Silver Spring, MD; and J. Hellmers, M. Zweng, P. A. Perez, and W. W. Wolf

Handout (1.3 MB)

5:00 PM

J4B.3

Field Campaign eXplorer (FCX): Empowering Scientific Advancements through Cloud-Based Data Visualization and Analysis

Navaneeth Selvaraj, Global Hydrometeorology Resource Center, Huntsville, AL; and S. Thapa, G. T. Stano, W. Ellett, L. Sinclair, and A. Kaulfus

5:15 PM

J4B.4

Developing a Cross-Institutional Open-Source Cyberinfrastructure to Explore, Analyze, and Communicate Greenhouse Gas Data and Information

Siddharth Chaudhary, Univ. of Alabama in Huntsville, Huntsville, AL; MSFC, Huntsville, AL; and D. K. Smith, J. le Roux, S. Adhikari, V. Gaur, S. T. (Pitter) Combley, M. Maskey, and A. Kavvada

4:30 PM-6:00 PM: Monday, 29 January 2024

Session 4A

Pure AI and Data-Driven Weather Forecasts II: Highlights on AI Forecast Performance and Evaluation

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Christina E. Kumler, CIRA; Daniel Rothenberg

4:30 PM

4A.1

A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models (Core Science Keynote)

Imme Ebert-Uphoff, CIRA, Fort Collins, CO; and J. Q. Stewart, K. A. Hilburn, J. T. Radford, R. T. DeMaria, R. Chase, R. A. Lagerquist, C. White, Y. Lee, J. Apke, K. D. Musgrave, L. Ver Hoef, C. E. Kumler, M. S. Wandishin, J. Duda, I. Jankov, and D. D. Turner

5:00 PM

4A.2

Evaluation of Tropical Cyclone Track and Intensity Forecasts from Purely ML-based Weather Prediction Models, Illustrated with FourCastNet

Robert T. DeMaria, CIRA, Fort Collins, CO; and M. DeMaria, G. Chirokova, K. Musgrave, J. T. Radford, and I. Ebert-Uphoff

5:15 PM

4A.3

AI-Models: A Tool for Making Forecasts with Data-Driven NWP Models

Baudouin Raoult, ECMWF, Reading, United kingdom; ECMWF, Reading, Berkshire, United kingdom; and M. Chantry, F. Pinault, J. Dramsch, and F. Pappenberger

5:30 PM

4A.4

Towards Comprehensive Evaluation of Data-Driven Numerical Weather Prediction Models

Jaideep Pathak, NVIDIA Corporation, Santa Clara, CA; Nvidia Corporation, Santa Clara, CA; and B. Bonev, T. Kurth, N. D. Brenowitz, Y. Cohen, K. Kashinath, J. Kossaifi, K. Azizzadenesheli, N. Kovachki, M. Baust, C. Hundt, A. Anandkumar, and M. Pritchard

5:45 PM

4A.5

Dynamical Tests of a Deep-Learning Weather Prediction Model

Gregory J. Hakim, University of Washington, Seattle, WA

AI Applications in the Energy Sector

Location: 347/348 (The Baltimore Convention Center)

Hosts: (Joint between the 15th Conference on Weather, Water, Climate, and the New Energy Economy; and the 23rd Conference on Artificial Intelligence for Environmental Science )

Cochairs: Charles Jones; David M Siuta

4:30 PM

J4.1

Predictive Intelligence for Lightning Events: A Machine Learning Solution to Mitigate Risks in Power Energy Operations

Luis Pavam, UFPR - Federal University of Parana, Curitiba, Brazil; SIMEPAR - Parana Environmental Technology and Monitoring Service, Curitiba, PR, Brazil; and C. Beneti, L. E. S. Oliveira, M. A. Z. Alves, and L. Calvetti

4:45 PM

J4.2

From High Impact Weather Nowcasting to Power Outage Prediction Using Machine Learning - A Short-Term R2O Project in Southern Brazil

Cesar Beneti, SIMEPAR - Parana Environmental Technology and Monitoring Service, Curitiba, PR, Brazil; and L. Calvetti, S. Paz, J. Silva Jr., R. T. Inouye, F. Verdelho, R. Lins da Rocha Jr., L. Pavam, G. Rasera, and M. V. Cardoso

5:00 PM

J4.3

Improved Power Outage Restoration Predictions using Neural Networks

Brennan Joseph Stutsrim, Univ. at Albany, Albany, NY; and N. P. Bassill and K. J. Sulia

5:15 PM

J4.4

Forecasting Energy Poverty for Missouri using different Machine Learning Techniques

Sarah Sharlene Balkissoon, University of Missouri, Columbia, MO

5:30 PM

J4.5

Bridging Real-World Lidar Measurements and Large-Eddy Simulation for Wind Turbine Validation Through Diffusion Models

Alex Rybchuk, National Renewable Energy Laboratory, Golden, CO; and P. Doubrawa, S. Letizia, N. Hamilton, A. Scholbrock, E. Maric, and L. A. Martínez-Tossas

5:45 PM

J4.6

Evaluating Bias Correction Methods for Gridded WRF Output Using Floating LiDAR Buoy Measurements

Polina Khapikova, TGS, Houston, TX; California Institute of Technology, Pasadena, CA; and K. Brennan, S. J. Eichelberger, PhD, B. Lasscock, and A. Sansal

J4C

Using Artificial Intelligence to Analyze Satellite Earth Observations II

Location: 338 (The Baltimore Convention Center)

Cochairs: Haonan Chen, Cooperative Institute for Research in the Atmosphere (CIRA); Jebb Q. Stewart, ESRL

4:30 PM

J4C.1

Application of a U-Net to Microwave Integrated Retrieval System (MiRS) Retrieved Vertical Profiles for Improved Precipitation Estimation

Shuyan Liu, CIRA, Fort Collins, CO; and C. Grassotti and Q. Liu

4:45 PM

J4C.2

Super-Resolution of GOES-16 ABI Channels to a Common High Resolution with a Convolutional Neural Network

Charles White, CIRA, Fort Collins, CO; and I. Ebert-Uphoff, J. M. Haynes, and Y. J. Noh

5:00 PM

J4C.3

Bridging the Scale Gap: Machine Learning for Urban Heat Island Modeling and Mitigation

Firas Gerges, Princeton University, Princeton, NJ; and A. B. Dieng and E. Bou-Zeid

J4C.4

Utilizing a Combination of Mathematical Analysis Tools to Locate Gravity Wave Airglow Signatures in Day-Night Band Imagery

5:15 PM

J4C.5

Harnessing Machine Learning and Principal Component Analysis for Atmospheric and Glint Correction of Ocean Color Measurements Using Hyperspectral Satellite Observations Such As the Geostationary TEMPO (Tropospheric Emissions: Monitoring of POllution)

Joanna Joiner, GSFC, Greenbelt, MD; and Z. Fasnacht, D. P. Haffner, M. Bandel, W. qin, A. Vasilkov, P. Castellanos, N. A. Krotkov, A. Ibrahim, and A. Heidinger

5:45 PM

J4C.6

Estimating Near-Surface Marine Air Temperature Using High-resolution Infrared Radiation Sounder Data and In Situ Observations

Yuhan Rao, North Carolina State University, Asheville, NC; Cooperative Institute for Satellite Earth System Studies, Asheville, NC; and J. L. Matthews and L. Shi

6:00 PM-7:30 PM: Monday, 29 January 2024

Exhibit Hall Opening Reception

Location: Hall F/ Swing (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the Agency Updates; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the Town Hall Meetings; the 12th AMS Conference for Early Career Professionals; the Presidential Conference; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

6:30 PM-9:00 PM: Monday, 29 January 2024

Technology and Data-science Mixer

Location: Key Ballroom Salon 3-4 (Hilton Baltimore Inner Harbor)

Hosts: (Joint between the Networking and Events; the 28th Conference on Probability and Statistics; the 23rd Conference on Artificial Intelligence for Environmental Science; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; and the 10th Symposium on High Performance Computing for Weather, Water, and Climate )

7:00 PM-9:00 PM: Monday, 29 January 2024

ESWN Reception

Location: Pratt Street Ale House

7:30 PM-10:00 PM: Monday, 29 January 2024

Coriolis (LGBTQ+ and Allies) Reception

Location: Pisces (Hyatt Regency Baltimore)

Tuesday, 30 January 2024

8:30 AM-9:45 AM: Tuesday, 30 January 2024

J5B

Developing Cloud-Based Tools for Data Analysis and Archiving II

Location: 336 (The Baltimore Convention Center)

Hosts: (Joint between the 40th Conference on Environmental Information Processing Technologies; the 23rd Conference on Artificial Intelligence for Environmental Science; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; and the 10th Symposium on High Performance Computing for Weather, Water, and Climate )

Cochairs: Eugene F. Burger, PMEL; Tiffany C. Vance, NOAA

8:30 AM

J5B.1

Continuing Warning Decision Assessments with Enhanced Simulations: Incorporating WES-in-the-Cloud

Alexander B. Zwink, NWS, Norman, OK; and T. Lindley, D. A. Morris, and S. R. Cobb

J5B.2

The Development and Application of the Cloud-Based Climate Operational Platform (CIPAS3.0)

9:00 AM

J5B.3

Enabling Scalable, Serverless Weather Model Analyses by "Kerchunking" Data in the Cloud

Daniel Rothenberg, Frederick, CO; Google Research, Boulder, CO

9:15 AM

J5B.4

Application of Fuzzy Logic–Based Techniques in Weather Prediction

Sarbari Ghosh, Univ. of Calcutta, Kolkata, WB, India; Kolkata, India; Univ. of Calcutta, Kolkata, India

Handout (8.0 MB)

8:30 AM-10:00 AM: Tuesday, 30 January 2024

J5A

AI for Statistical Parameterization of Unresolved Processes in Earth System Models I

Location: 345/346 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science )

Cochairs: Maria J. Molina; Kara D. Lamb, Earth and Environmental engineering

8:30 AM

J5A.1

Quantum Mechanics for Parametrization of Dynamical Systems

David Freeman, Dartmouth College, Hanover, NH

8:45 AM

J5A.2

Towards Well-Calibrated Stochastic Neural Network Convective Parameterizations

Jerry Lin, University of California, Irvine, Irvine, CA, CA; and E. Wong-Toi, S. Mandt, and M. Pritchard

9:00 AM

J5A.3

A Posteriori Learning of Closures for Geophysical Turbulence Using Ensemble Kalman Inversion

Yifei Guan, Rice University, Houston, TX; and P. Hassanzadeh, T. Schneider, Z. Huang, O. Dunbar, I. Lopez-Gomez, and J. Wu

9:15 AM

J5A.4

Quantifying 3D Gravity Wave Drag in a Library of Convection-Permitting Simulations: What Do We Need for Data-driven Parameterization of Convective Gravity Waves?

Y. Qiang Sun, Rice Univ., Houston, TX; and P. Hassanzadeh, M. J. Alexander, and C. G. Kruse

9:30 AM

J5A.5

Incorporation of Physical Equations into a Neural Network Structure for Predicting Shortwave Radiative Heat Transfer

Henry Schneiderman, Pittsburgh Data Science LLC, Pittsburgh, PA

9:45 AM

J5A.6

Practical Limitations of Machine Learning Approaches for Online Emulation of Simplified Physical Processes in CAM6

Garrett Limon, University of Michigan, Ann Arbor, MI; and C. Jablonowski

J5B

Advances in the Use of Artificial Intelligence in Support of Aviation, Range, and Aerospace Meteorology

Location: 338 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 24th Conference on Aviation, Range, and Aerospace Meteorology )

Cochairs: John K. Williams; Anthony J. Wimmers, CIMMS

8:30 AM

J5B.1

A Variational Autoencoder for Coastal Visibility Predictions: Architecture, Performance and R2X Potential

Brian Colburn, Conrad Blucher Institute for Surveying and Science, Corpus Christi, TX; and P. Tissot, J. K. Williams, S. A. King, W. G. Collins, E. Krell, L. C. Gaudet, H. kamangir, and M. C. White

8:45 AM

J5B.2

Toward Trustworthy Wind and Turbulence Predictions for Advanced Air Mobility

Mounir Chrit, University of North Dakota (UND), GRAND FORKS, ND; University of North Dakota, Grand Forks, ND; and M. Majdi

9:00 AM

J5B.3

Detection and Characterization of Contrails Using Artificial Intelligence: Insights from a 2023 Field Campaign

Jay P. Hoffman, CIMSS, Madison, WI; and T. Rahmes, A. J. Wimmers, W. Feltz, M. J. Foster, J. Feltz, and C. Phillips

9:15 AM

J5B.4

LaunchCast: Deep Learning for Space Operations

Brandon T. McClung, Air Force, Norman, OK; NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, Norman, OK; and A. McGovern

9:30 AM

J5B.5

Exploring Additional Predictors to Improve Probabilistic Forecasting of Thunderstorms Using a 2-D Convolutional Neural Network (CNN)

Dean Calhoun, Purdue University, West Lafayette, IN; and M. B. Ba, K. Zigner, and J. E. Ghirardelli

9:45 AM

J5B.6

Operational Use of Machine Learning and Numerical Weather Prediction to Predict Aircraft Turbulence and Icing

Lance E. Steele, Certified Consulting Meteorologist, Weathernews, Norman, OK

10:00 AM-10:45 AM: Tuesday, 30 January 2024

Coffee Break [Main Terrace (BCC), Camden Lobby (BCC), Hall E (BCC), Hall F (BCC)]

Location: The Baltimore Convention Center

12:00 PM-1:45 PM: Tuesday, 30 January 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Tuesday, 30 January 2024

Session 7B

Artificial Intelligence for Environmental Science II

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Rob Redmon; Philippe E. Tissot; Ryan Lagerquist

1:45 PM

7B.1

Ensemble Probabilistic Severe Weather Prediction Using Convection-Allowing Models and Deep Generative Models

Yingkai Sha, NCAR, Boulder, CO; and R. A. Sobash, D. J. Gagne II, and C. Schwartz

Handout (4.5 MB)

2:00 PM

7B.2

Impacts of Multiscale Predictors on Random Forest Based Probabilistic Forecasts of Severe Weather Hazards

Daniel Lee Kubalek, University of Oklahoma, Norman, OK; and A. T. Johnson and X. Wang

2:15 PM

7B.3

True Positive Convective Storms Without Lightning in ThunderCast

Stephanie M. Ortland, University of Wisconsin-Madison, Madison, WI; Univ. of Wisconsin -- Madison, Madison, WI; and M. J. Pavolonis and J. L. Cintineo

2:30 PM

7B.4

Artificial Intelligence-Based Investigation of Severe Thunderstorms Producing Downbursts in Canada.

Mohammad Hadavi, McGill University, Montreal, QC, Canada; and D. Romanic

2:45 PM

7B.5

Using Deep Learning to Predict Cloud-to-Ground Lightning in the Western United States

Dmitri Alexander Kalashnikov, Washington State University Vancouver, Vancouver, WA; and F. V. Davenport, Z. M. Labe, PhD, P. C. Loikith, J. T. Abatzoglou, and D. Singh

J7A

AI for Statistical Parameterization of Unresolved Processes in Earth System Models II

Location: 345/346 (The Baltimore Convention Center)

Cochairs: Kara D. Lamb, Earth and Environmental engineering; Maria J. Molina

1:45 PM

J7A.1

A Machine Learning Parameterization of Clouds in a Coarse-Resolution Climate Model for Unbiased Radiation

Brian Henn, Allen Institute for Artificial Intelligence (AI2), Seattle, WA; and Y. R. Jauregui, S. Clark, N. D. Brenowitz, J. McGibbon, O. Watt-Meyer, A. G. Pauling, and C. S. Bretherton

Handout (1.6 MB)

2:00 PM

J7A.2

Improving Low-Cloud Fraction Predictions through Machine-Learning Techniques

Haipeng Zhang, University of Maryland, College Park, College Park, MD; and Y. Zheng Prof and Z. Li

2:15 PM

J7A.3

Emulation of Cloud Microphysics in a Climate Model

Walter A. Perkins, Allen Institute for Artificial Intelligence, Seattle, WA; and N. D. Brenowitz, C. S. Bretherton, and J. M. Nugent

2:30 PM

J7A.4

A Neural Network Aerosol Optics Emulator for E3SM

Andrew V. Geiss, PNNL, SEATTLE, WA; and P. L. Ma and B. Singh

2:45 PM

J7A.5

Convection-Generated Gravity Waves from Weather Radar Observations Via a Neural Network and a Dynamical Atmospheric Model

M. Joan Alexander, NorthWest Research Associates, Boulder, CO; and C. G. Kruse, M. Bramberger, A. K. Chattopadhyay, P. Hassanzadeh, B. Green, and L. Hoffmann

J7A

FAIR and Open Data and Software within the Atmospheric and Ocean Sciences to Support Transparent, Reusable, and Efficient Research and Operations I

Location: 337 (The Baltimore Convention Center)

Hosts: (Joint between the 40th Conference on Environmental Information Processing Technologies; the 27th Conference of Atmospheric Science Librarians International; the 23rd Conference on Artificial Intelligence for Environmental Science; the 14th Conference on Transition of Research to Operations; and the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science )

Cochairs: Douglas C. Schuster, NCAR; Matthew S. Mayernik, NCAR

1:45 PM

J7A.1

Towards a FAIRest Copernicus Climate and Atmosphere Data Store

Angel Lopez Alos, ECMWF, Reading, United kingdom; and M. Fuentes, B. Raoult, M. Znacchi, E. Damasio Da-Costa, and E. Comyn-Platt

2:00 PM

J7A.2

Interactive Visualization of the CESM-LENS2 Climate Dataset – Lessons Learned and Recommendations for Visualizing Gridded Datasets using Open Science Tools

Pritam Das, Univ. of Washington, Seattle, WA; NCAR, Boulder, CO; and N. Sobhani, T. Zhang, and N. Cherukuru

2:15 PM

J7A.3

Metadata Re-curation at NCAR's Research Data Archive Domain Repository

Bob Dattore, NCAR, Boulder, CO; and D. C. Schuster

2:30 PM

J7A.4

Building FAIR and Open Data Access Services for Next Generation Fire Detections at NOAA/NESDIS

Evan McQuinn, NESDIS, Silver Spring, MD; and M. Pavolonis, L. Mayo, C. Esterlein, P. Fricke, A. Caldwell, J. Puerto, and T. Floyd

2:45 PM

Discussion

Ashley Orehek-Rossi,

3:00 PM-4:30 PM: Tuesday, 30 January 2024

Coffee Break & Formal Poster Viewing [Hall E (BCC) & Hall F (BCC)]

Location: Hall E (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the 12th AMS Conference for Early Career Professionals; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

Posters II

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

399

A Purely Data-Driven Transformer Model for Real-Time Predictions of the 2023-24 Climate Condition in the Tropical Pacific

Rong-Hua Zhang, School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China; and C. Gao

398

A Transformer-Based Deep Learning Model for Successful Predictions of the 2021 Second-Year La Niña Condition

400

Geocenter: A New Deep-Learning Ensemble for Determining Tropical-Cyclone Center Locations from Geostationary Satellite Data

Ryan A. Lagerquist, CIRA and NOAA/ESRL/GSL, Boulder, CO; and G. Chirokova, M. DeMaria, R. DeMaria, I. Ebert-Uphoff, J. Knaff, and C. Slocum

401

Development of a Machine Learning-Based Tropical Cyclone Track Prediction Scheme over the Western North Pacific

You-Hyun Baek, KMA, Seogwipo-si, Jeju-do, South korea; and H. Lee, J. R. Lee, S. Won, and S. H. Kim

402

Improving Ensemble Model Tropical Cyclone Track Forecast Using Machine Learning

Nikita Agrawal, Whitney M. Young Magnet High School, Chicago, IL; and B. A. Colle

403

How Far in Advance Can Deep Learning Predict Tropical Cyclone Formation?

Chanh Kieu, Indiana University Bloomington, Bloomington, IN; and Q. Nguyen and N. Tri

405

Machine Learning Quality Control of Lightning Data for Tropical Cyclone Intensity Forecasting

Kyle A. Hilburn, ; and S. N. Stevenson, K. Musgrave, and B. C. Trabing

406

Exploring Tropical Cyclone Structure and Evolution with AI-based Synthetic Passive Microwave Data

Marie McGraw, CIRA, Fort Collins, CO; and K. Haynes, K. D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff

407

Towards Global Fire Radiative Power (FRP) Retrievals from METImage Measurements Using Regional Radiance Machine Learning Models

Yingxin Gu, IMSG at NOAA/NESDIS/STAR, College Park, MD; and I. A. Csiszar, M. Tsidulko, and W. Guo

408

Toward Prediction of Pyrocumulonimbus with Machine Learning

Chuyen T Nguyen, Naval Research Laboratory, Monterey, CA; and E. A. Krell, J. Nachamkin, D. A. Peterson, E. J. Hyer, P. E. Tissot, S. A. King, B. Estrada Jr., and K. J. Tory

404

A Spatially-Aware North Atlantic Tropical Cyclogenesis Index Using Machine Learning

409

Using Grouped Features to Improve Explainable AI Results for Atmospheric AI Models that use Gridded Spatial Data and Complex Machine Learning Techniques

Evan Andrew Krell, Texas A&M Univ. - Corpus Christi, Corpus Christi, TX; and H. Kamangir, W. G. Collins, S. A. King, and P. Tissot

410

Automatic Fog Detection and Visibility Estimation From Camera Images Using Deep Learning Features For Aviation Operations Involving Uncrewed Aircraft Systems (UAS)

Marwa Majdi, Univ. of North Dakota, Grand Forks, ND; and D. Delene and M. Chrit

411

Predicting Winter Fog over Complex Terrain Using Deep Learning

Grace Liu, University of Utah, Salt Lake City, UT; and Z. Pu

412

Transfer Learning for the Canadian Airspace: Leveraging a Globally-Trained UNet Model to Create Enhanced Radar Depictions in Regional Domains

Kiley L. Yeakel, MIT Lincoln Laboratory, Lexington, MA; and P. M. Lamey, D. Morse, H. Iskenderian, and M. S. Veillette

413

Improved Composite Reflectivity Mosaics in Mountain Terrain for Air Traffic Management

William J. Dupree, MIT, Lexington, MA; MIT Lincoln Laboratory, Lexington, MA; and J. Y. N. Cho, M. S. Veillette, and H. Iskenderian

414

Development of Localized Aviation MOS Program for Main Airports in South Korea

Jeonghoe Kim, Seoul National University, Seoul, South korea; and J. H. Kim

415

Utilizing Neural Networks to Predict Water Temperatures in a Thermal Refuge

Andrew DeSimone, Texas A&M University-Corpus Christi, Corpus Christi, TX; and A. Beasley, A. Anand, B. Colburn, S. Dasu, P. E. Tissot, and H. M. Marrero Colominas

4:30 PM-6:00 PM: Tuesday, 30 January 2024

Living in a Changing Environment: Applications of Artificial Intelligence and Machine Learning for Weather Forecasting

Location: Johnson AB (Hilton Baltimore Inner Harbor)

Hosts: (Joint between the Second Symposium on the Future of Weather, Forecasting, and Practice; and the 23rd Conference on Artificial Intelligence for Environmental Science )

Cochairs: Andrei Evbuoma, NOAA; Erik S. Pytlak; Kyle A. Hilburn

4:30 PM

J8.1

The Key Role of AI in the Future of Weather Forecasting

Amy McGovern, NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography Univ. of Oklahoma (AI2ES), Norman, OK

4:45 PM

J8.2

A (Unique) Peak into the Future: Prognosticating the Meteorological Landscape

Cory J. Demko, CACI, Arlington, VA

Handout (5.3 MB)

5:00 PM

J8.3

RainForests: A Machine-Learning Approach to Calibrating NWP Precipitation Forecasts

Belinda Trotta, BoM, Melbourne, VIC, Australia; and B. Owen, J. Liu, G. Weymouth, T. Gale, T. Hume, A. Schubert, J. Canvin, D. Mentiplay, and J. Whelan

5:15 PM

J8.4

Development of a Prediction Method for Weather Types using a Deep Neural Network

Kazuki Tanaka, Japan Meteorological Agency, Tsukuba, Japan; and T. T. Sekiyama

5:30 PM

J8.5

Using Machine Learning to Improve 10-m Wind Forecasts in the Winter – Toward an Improved WSSI Forecast

Marshall Romanick Baldwin, CAPS, Norman, OK; and N. A. Snook, K. A. Brewster, and P. Spencer

5:45 PM

J8.6

FogNet-V2: Multi-view Tensorized Transformer for Coastal Fog Forecasting

Hamid kamangir, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and E. Krell, W. G. Collins, P. Tissot, S. A. King, and D. J. J. Gagne II

Statistics and Machine Learning for Climate Science

Location: 302/303 (The Baltimore Convention Center)

Hosts: (Joint between the 28th Conference on Probability and Statistics; the 37th Conference on Climate Variability and Change; and the 23rd Conference on Artificial Intelligence for Environmental Science )

Cochairs: Julia Velletta; Vanderlei Vargas Jr. Jr.

4:30 PM

J8.1

The Climate Change Deniers are Wrong: Evidence from NOAA's Barrow Atmospheric Observatory in Alaska

Kevin F Forbes, Energy and Environmental Data Science, Malahide, D, Ireland

4:45 PM

J8.2

Prediction of Tropical Pacific Rain Rates with Over-parameterized Neural Networks

Hojun You, Univ. of Houston, Houston, TX; and M. Jun, R. Saravanan, C. J. Schumacher, J. Wang, and R. Wong

5:00 PM

J8.3

Subseasonal-to-Decadal Analog Forecasting Using Spatially-Weighted Masks Learned By a Neural Network

Jamin K. Rader, Colorado State Univ., Fort Collins, CO; and E. A. Barnes, M. Fernandez, and J. Landsberg

5:15 PM

J8.4

A Stochastic Statistical Model for U.S. Outbreak-Level Tornado Occurrence Based on the Large-Scale Environment

Kelsey M. Malloy, Columbia Univ., New York, NY; Columbia Univ., New York, NY; and M. K. Tippett

5:30 PM

J8.5

Building Asymmetry into PCA and Application to ENSO

Erik T. Swenson, George Mason Univ., Fairfax, VA

5:45 PM

J8.6

Constraining the Simulated Radiative Effects of Biomass Burning in Southern Africa

Victor Alejandro Sanchez, Carnegie Mellon Univ., Pittsburgh, PA; Carnegie Mellon Univ., Pittsburgh, PA; and J. Carzon, M. Kuusela, and H. Gordon

J8A

FAIR and Open Data and Software within the Atmospheric and Ocean Sciences to Support Transparent, Reusable and Efficient Research and Operations II

Location: 337 (The Baltimore Convention Center)

Cochairs: Douglas C. Schuster, NCAR; Matthew S. Mayernik, NCAR

4:30 PM

J8A.1

Herbie: A Python Package for Accessing Numerical Weather Prediction Data

Brian Kenneth Blaylock, US Naval Research Laboratory, Marine Meteorology, Monterey, CA

4:45 PM

J8A.2

Promoting Open Science: A Better Tool for Version Controlling Gigantic Binary Data Files - Git-Qing

Guoqing Ge, CIRES and NOAA/GSL, Boulder, CO; and M. Hu, T. T. Ladwig, S. S. Weygandt, S. Liu, and J. R. Carley

5:00 PM

J8A.3

An Open-Source Software Solution for Repeatable and Interpretable Geospatial Evaluations

Gregory Petrochenkov, Lynker, Leesburg, VA; and F. Aristizabal and F. Salas

5:15 PM

J8A.4

The Geolocated Information Processing System (GeoIPS) – A Platform for Collaborative Development

Christopher Camacho, NRL, Monterey, CA; and M. Surratt, S. Yang, A. A. Lambert, and L. Wilson

5:30 PM

J8A.5

AMS Publications Support for Open, Transparent, and Equitable Research

Michael A. Friedman, Rockport, MA; and D. C. Schuster

5:45 PM

Open Discussion

J8A

Machine Learning for Smoke and Wildfires

Location: 345/346 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 15th Conference on Weather, Water, Climate, and the New Energy Economy )

Cochairs: Douglas Rao, Cooperative Institute for Satellite Earth System Studies (CISESS); Lora Koenig

4:30 PM

J8A.1

Leveraging Open-Source Data and Tools to Predict Wildfire Risk Using Machine Learning

Rochelle S Koeberle, Booz Allen Hamilton, Arlington, VA

4:45 PM

J8A.2

Exploring the Role of Weather Forecasts in Predicting Wildfire Occurrence for CONUS Using the Unet3+ Deep Learning Model

Bethany Earnest, CIWRO, Norman, OK; and A. McGovern, C. Karstens, and I. L. Jirak

5:00 PM

J8A.3

Modeling Wildfire Behavior with Forest Machine Learning Models Using the RAVE Dataset

Christina E. Kumler, ; and J. Romero-Alvarez and J. Q. Stewart

5:15 PM

J8A.4

A Machine Learning Rate of Spread Model in WRF-SFIRE

Angel Farguell, San Jose State Univ., San Jose, CA; and J. Drucker, J. Mandel, and A. Kochanski

5:30 PM

J8A.5

Predicting Wildfire Fuel Moisture from Atmospheric Data Using Machine Learning

Pankaj Kumar Jha, LLNL, Livermore, CA; and J. D. Mirocha, A. Farguell, A. Kochanski, and P. Cameron-Smith

Handout (2.0 MB)

5:45 PM

J8A.6

Advancing Wildfire Research Using Large Eddy Simulation (LES) and Machine Learning

Siyuan Wang, CIRES, CU Boulder, Boulder, CO; NOAA, Boulder, CO

J8B

Applications of Artificial Intelligence to the Coastal Environment

Location: 338 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 22nd Symposium on the Coastal Environment )

Cochairs: Gregory Dusek, NWS; Philippe E. Tissot

4:30 PM

J8B.1

An Update on Coastal Artificial Intelligence and the AI2ES NSF AI Institute

Philippe E. Tissot, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX

4:45 PM

J8B.2

Seasonal Forecasts of Bottom Temperatures along the North American West Coast

Tongtong Xu, NOAA, Boulder, CO; Univ. of Colorado Boulder, Boulder, CO; and M. Newman, M. A. Alexander, and A. Capotondi

5:00 PM

J8B.3

Performance and Comparison of Seq2Seq and Transformer Model Architectures for the Prediction of Water Levels from Hours to Days

Marina Vicens Miquel, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and P. Tissot and A. Medrano, PhD

5:15 PM

J8B.4

Application of Machine Learning Methods to Better Quantify Water-Level Anomalies in Annapolis, MD

Joseph P Smith, U. S. Naval Academy, Annapolis, MD; and A. R. Davies

Handout (8.3 MB)

5:30 PM

J8B.5

Machine Learning Water Level Predictions for an Intermediate Location Using Connected Bodies of Water

Jacob Alonzo, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and E. Flores, P. E. Tissot, A. Anand, C. Ehrke, and R. J. Shelly

5:45 PM

J8B.6

AI for Quality Control of Water Level Observations

Lindsay Roupe Abrams, NOAA, Silver Spring, MD; and J. Spore, G. Dusek, P. E. Tissot, E. Krell, and H. Moustahfid

J8B

Cloud Computing for Big Data in Atmosphere, Ocean, and Climate

Location: 336 (The Baltimore Convention Center)

Cochairs: Nazila Merati, NESDIS; Tiffany C. Vance, NOAA

4:30 PM

J8B.1

Arraylake: A Cloud-Native Data Lake Platform for Earth System Science

Ryan Abernathey, Earthmover PBC, New York, NY; and J. J. Hamman

4:45 PM

J8B.2

From Waterfall to Agile: Lessons Learned During the NOAA/NESDIS/OCS Journey to Expedite Transition to Ops in the Cloud

Kelly L. Neely, NESDIS, Silver Spring, MD; and M. Zweng, W. W. Wolf, P. Roy, K. Ali, and K. lassiter

5:00 PM

J8B.3

A Workflow for Serving Model Data in the Cloud to a Broader Community

Jonathan Joyce, RPS Group, South Kingstown, RI; and B. Adams, J. Doyle, K. Fillingham, M. Iannucci, A. Kerney, K. Knee, D. Moretti, J. Quintrell, D. Snowden, T. C. Vance, and M. Wengren

5:15 PM

J8B.4

Adapting to a Changing Environment: Automating the Transition to Operations for Scientific Applications

Yunhui Zhao, GAMA-1 Technologies, Greenbelt, MD; and W. W. Wolf, K. Pinkney, and P. Roy

J8B.5

Data Compression with Deep Neural Network and Principal Component Analysis Machine Learning

5:45 PM

J8B.6

Standardized Drought Indices on Drought.Gov, Produced with Climate Engine and Google Earth Engine, from Multiple Foundational Precipitation and Temperature Datasets

Steve Ansari, NOAA, Asheville, NC; and J. Huntington, M. Shaw, K. Satalino, J. Fain, B. Daudert, J. Hansen, C. Morton, T. Ott, and K. C. Hegewisch

6:00 PM-7:00 PM: Tuesday, 30 January 2024

Exhibit Hall Reception

Location: Hall F/ Swing (The Baltimore Convention Center)

Wednesday, 31 January 2024

8:30 AM-10:00 AM: Wednesday, 31 January 2024

Session 9A

Improvements to Subseasonal-to-Seasonal (S2S) Predictions Using Novel Statistical and Artificial Intelligence/Machine Learning (AI/ML) Methods I

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Marybeth Arcodia, University of Miami RSMAS; Maria J. Molina; Johnna Infanti, NOAA / NWS / NCEP / Climate Prediction Center; Nachiketa Acharya

8:30 AM

9A.1

When Machine Learning Objectives Compete for Improved Subseasonal Bias Correction, Who Wins?

Maria J. Molina, University of Maryland, College Park, MD; and K. Dagon, J. Schreck, J. S. Perez-Carrasquilla, K. J. Mayer, N. Sobhani, D. J. Gagne II, Ph.D., I. Ebert-Uphoff, C. Metzler, and G. A. Meehl

8:45 AM

9A.2

Development of an Improved Week 3-4 Temperature Consolidation First Guess

Cory F. Baggett, CPC, College Park, MD; and E. Burrows, D. Barandiaran, E. LaJoie, D. C. Collins, M. Goss, J. Infanti, J. Hicks, E. Oswald, and J. Gottschalck

9:00 AM

9A.3

Sea Surface Salinity Provides Subseasonal Predictability for Forecasts of Opportunity of U.S. Summertime Precipitation

Marybeth Arcodia, Colorado State University, Fort Collins, CO; and E. A. Barnes, P. J. Durack, P. Keys, and J. Rocha

9:15 AM

9A.4

Applying an Inherently Interpretable Neural Network to Subseasonal-to-Seasonal Climate Prediction

Nicolas J Gordillo, Colorado State Univ., Fort Collins, CO; and E. A. Barnes

9:30 AM

9A.5

Utilizing Machine Learning for Constructing Probabilistic Subseasonal Precipitation Forecasts in California

Nachiketa Acharya, NOAA, State College, CO; and K. J. C. Hall

9:45 AM

9A.6

Leveraging Interpretable Machine Learning Methods for Subseasonal Precipitation Forecasts in Western United States

Agniv Sengupta, SIO, La Jolla, CA; and M. J. DeFlorio, I. Yang, Z. Yang, J. L. Bano Medina, B. Guan, and L. Delle Monache

J9B

Language Transformer, ChatGPT and Other Natural Language Processing Machine Learning in Weather Enterprise

Location: 338 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 14th Conference on Transition of Research to Operations )

CoChair: Anthony J. Wimmers, CIMMS

8:30 AM

J9B.1

Using a Large Language Model to Generate Text Weather Forecasts

Neil David Gordon, ScD MIT, Neil Gordon Consulting, Otaki, WGN, New zealand

Handout (8.9 MB)

8:45 AM

J9B.2

Leveraging Large Language Models for Providing User Support and Access to Forecast Products

Baudouin Raoult, ECMWF, Reading, United kingdom; and M. U. Shirk, P. Ferrarese, S. Lamy-Thepaut, H. Setchell, and G. Bighin

9:00 AM

J9B.3

Large Language Models for Classifying Flash Flood Impacts

Jorge Alberto Duarte, Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), Norman, OK; and J. J. Gourley, H. Vergara, P. E. Kirstetter, and C. D. Nicholson

J9B.4

Developing and Evaluating Meteorologically Fine-Tuned Large Language Models for Weather Safety Education

9:15 AM

J9B.5

AI-Assisted Programming for Environmental Scientists

Myranda Uselton Shirk, Vanderbilt University, Nashville, TN

9:45 AM

J9B.6

A Study on a Weather AI-Search Engine using Speech Recognition and Natural Language Processing

Hayan Shin, KMA, Seogwipo-si, Jeju, South korea; and C. Yang, B. Kim, C. Park, J. Joe, J. Choo, M. Seo, Y. H. Baek, H. Lee, and S. W. Ko

10:00 AM-10:45 AM: Wednesday, 31 January 2024

Coffee Break [Main Terrace (BCC), Camden Lobby (BCC), Hall E (BCC), Hall F (BCC)]

Location: The Baltimore Convention Center

Hosts: (Joint between the Networking and Events; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the 12th AMS Conference for Early Career Professionals; the Presidential Conference; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

10:45 AM-12:00 PM: Wednesday, 31 January 2024

Session 10A

Improvements to Subseasonal-to-Seasonal (S2S) Predictions Using Novel Statistical and Artificial Intelligence/Machine Learning (AI/ML) Methods II

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Johnna Infanti, NOAA / NWS / NCEP / Climate Prediction Center; Nachiketa Acharya; Marybeth Arcodia; Maria J. Molina, AccuWeather, Inc.

10:45 AM

10A.1

The Context of Machine Learning in Prediction Models (Invited Presentation)

Christina E. Kumler,

11:00 AM

10A.2

An Earth-System-Oriented View of the S2S Predictability of Weather Regimes

Jhayron S Perez-Carrasquilla, Univ. of Maryland, College Park, MD; and M. J. Molina

11:15 AM

10A.3

A Deep Learning Approach to Severe Weather Subseasonal Forecasting over the United States

Maria M. Madsen, AI2ES & University of Oklahoma, Norman, OK; and A. McGovern

11:30 AM

10A.4

Identifying Tropical State-Dependent Biases Relevant to Midlatitude Subseasonal Predictability with Explainable Neural Networks

Kirsten J Mayer, NCAR, Boulder, CO; and K. Dagon and M. J. Molina

Improving the Subseasonal Forecasting of the Western U.S. Precipitation with A Causality-Based Statistical Model

J10B

From Data to Decision: AI at the Intersection of Meteorology, Climate, and Society

Location: 338 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the 19th Symposium on Societal Applications: Policy, Research and Practice )

Cochairs: Jacob T. Radford; John K. Williams

10:45 AM

J10B.1

Identifying Societal Vulnerabilities and Resilience Related to Weather Using Newspapers and Artificial Intelligence

Renee Sieber, McGill Univ., Montreal West, QC, Canada; and F. Fabry, V. Slonosky, M. Wang, and Y. Zhang

11:00 AM

J10B.2

Analyzing Socio-Geographic Disparities Through Geolocated Social Media Content During Hurricane Ida

Achraf Tounsi, iSMART Lab, Stevens Institute of Technology, Hoboken, NJ

11:15 AM

J10B.3

Improving Generalizability of Road Condition Classification Models for Department of Transportation Camera Images

Carly Sutter, Univ. at Albany, SUNY, Albany, NY; and K. J. Sulia, N. P. Bassill, C. D. Thorncroft, V. Przybylo, C. D. Wirz, M. G. Cains, J. T. Radford, and D. A. Evans

11:30 AM

J10B.4

NWS Forecaster Perceptions of New AI Guidance for Coastal Fog Prediction

Christopher D. Wirz, NSF NCAR, Boulder, CO; and J. L. Demuth, H. M. Marrero Colominas, J. T. Radford, P. E. Tissot, M. G. Cains, H. Kamangir, E. A. Krell, A. Bostrom, S. A. King, W. G. Collins, and J. K. Williams

11:45 AM

J10B.5

Forecaster Perceptions of Trustworthiness, Explainability, and Interpretability in the Context of AI-Derived Guidance

Mariana Goodall Cains, NCAR, Boulder, CO; and C. D. Wirz, J. L. Demuth, A. Bostrom, M. C. White, and J. T. Radford

12:00 PM-1:45 PM: Wednesday, 31 January 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Wednesday, 31 January 2024

Session 11A

LSTM and Other Time Series Machine Learning Methods for Time Series Prediction

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Ryan Lagerquist; Jebb Q. Stewart, ESRL

1:45 PM

11A.1

Enhancing Regional Weather Forecasts over Complex Terrain: A Hybrid Physics-Machine Learning Approach

Ming Fan, ORNL, Oak Ridge, TN; and W. Zhang, H. G. Kang, K. R. Birdwell, and K. J. Evans

2:00 PM

11A.2

Predicting Forecast Error of Numerical Weather Prediction Models using an LSTM

David Aaron Evans, University at Albany, SUNY, Albany, NY; and K. J. Sulia, N. P. Bassill, C. D. Thorncroft, L. Gaudet, Ph.D., and J. C. Rothenberger

2:15 PM

11A.3

Gap-Filling AOD Data Using Deep Learning Techniques in Satellite Imagery

Yi Wang, Science and Technology Corporation (STC), Columbia, MD; and M. R. Schoeberl, R. B. Esmaili, and J. Liu

2:30 PM

11A.4

A Fuel Moisture Model for WRF-SFIRE from HRRR and RAWS Data by a Physics-Initialized Recurrent Neural Network

Jan Mandel, Univ. of Colorado Denver, Denver, CO; and J. Hirschi, A. Kochanski, A. Farguell, D. V. V. Mallia, B. Shaddy, A. A. Oberai, K. A. Hilburn, and J. Haley

2:45 PM

11A.5

Comparison of Multivariate Time Series Prediction Techniques for Emulating Noah-LSM Soil Moisture Outputs.

Mitchell Dodson, MSFC, Huntsville, AL; University of Alabama in Huntsville, Huntsville, AL

Session 11B

Leveraging Unsupervised Machine Learning in Environmental Science

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Kyle A. Hilburn; Kirsten J Mayer, Colorado State University

1:45 PM

11B.1

Vegetation Mapping of Africa Using Machine Learning

Ismail Adewale Olumegbon, Univ. of Maryland, Baltimore County, Greenbelt, MD; and H. M. Barbosa

Handout (3.3 MB)

2:00 PM

11B.2

Three-Dimensional Convective Cell Database for Cloud Process Study – Segmentation and Validation

Xiaowen Li, Morgan State University, Baltimore, MD; Morgan State Univ., Baltimore, MD; and M. Rafsan Jani and C. Padilla

2:15 PM

11B.3

Classification of Cloud Particle Imagery Using Variational Autoencoders and Unsupervised Clustering

Joseph Ko, Columbia Univ., New York, NY; and K. J. Sulia, V. Przybylo, M. van Lier-Walqui, and K. D. Lamb

2:30 PM

11B.4

The Utility of Domain Knowledge When Developing Deep Learning Models to Predict Coastal Fog

Waylon G. Collins, NOAA/National Weather Service, Corpus Christi, TX; and B. Colburn, P. E. Tissot, S. A. King, E. Krell, and J. K. Williams

2:45 PM

11B.5

Cloud and Aerosol Variability across Different Synoptic Regimes Identified with a Deep Learning-Based Unsupervised Clustering Model

Xue Zheng, LLNL, Livermore, CA; and S. Qiu, Faruque, and J. Wang

3:00 PM-3:40 PM: Wednesday, 31 January 2024

23rd Conference on Artificial Intelligence for Environmental Science ePosters (Wednesday)

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

E69

Physics-Informed Machine Learning Methods for Post-Processing Weather Elements

Gurvir Kaur Bawa, Embry-Riddle Aeronautical University, Daytona Beach, FL; Embry-Riddle Aeronautical University, Daytona Beach, FL; and R. S. Stansbury and C. G. Herbster

E70

Exploring Severe Weather Nowcasting Predictions Using Atmospheric Observations and Deep Learning

John L. Cintineo, NSSL, Madison, WI; and M. Pavolonis and J. Sieglaff

E71

High Spatiotemporal Resolution Quantitative Precipitation Estimation over the United Arab Emirates

Vesta Afzali Gorooh, SIO, La Jolla, CA; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA; and M. Ghazvinian, PhD, W. Hu, Q. Cao, M. Pan, B. Hassan Banimfreg, E. Damiani, A. Sengupta, D. Axisa, and L. Delle Monache

3:00 PM-4:30 PM: Wednesday, 31 January 2024

Coffee Break & Formal Poster Viewing [Hall E (BCC) & Hall F (BCC)]

Location: Hall E (The Baltimore Convention Center)

Posters III

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

664

Predicting Surface Temperatures Using Compound Geopotential Heights in a Deep Learning Framework

Jahangir Ali, University of Arkansas, Fayetteville, AR; and L. Cheng

665

Discovering New Representations of Near-Surface Exchange Using Symbolic Regression

Andrew V. Geiss, PNNL, Richland, WA; and L. K. Berg

666

Impact of Latent Heating and Cloud Radiative Effects on the Variability of Jet Streams in Observations

Xinhuiyu Liu, Univ. of Virginia, CHARLOTTESVILLE, VA; and K. M. Grise and Y. Rao

667

Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice

Emam Hossain, University of Maryland Baltimore County, Baltimore, MD; and S. Ali, M.S., Y. Huang, N. J. Schlegel, J. Wang, A. C. Subramanian, and M. O. Gani

668

Estimating Causal Effects of Greenland Blocking on Arctic Sea Ice Melt using Deep Learning Technique

Sahara Ali, M.S., UMBC, Baltimore, MD; UMBC, Baltimore, MD; and Faruque, Y. Huang, M. O. Gani, N. J. Schlegel, A. Subramanian, and J. Wang

Handout (4.5 MB)

669

Assessing Radiation Emulator of Numerical Weather Prediction Models for Operational Application in Korea Meteorological Administration

670

Parameterizing Cloud Microphysics with Machine Learning-Enabled Bayesian Parameter Inference

Kaitlyn Loftus, Columbia University, New York, NY; and M. van Lier-Walqui, H. Morrison, K. K. Chandrakar, PhD, M. A. Bhouri, and S. P. Santos

671

Explainable Offline-Online Training of Neural Networks for Parameterization: A 1D Gravity Wave-QBO Testbed

Hamid A. Pahlavan, Rice University, Houston, TX; and P. Hassanzadeh and M. J. Alexander

672

Machine Learning Emulation of a Quasi Two-Moment Microphysical Scheme with Diagnosed Particle Sizes

Mircea Grecu, Morgan State University, Greenbelt, MD; and X. Li, M. L. Rilee, M. P. Bauer, and K. S. Kuo

Handout (979.5 kB)

673

Correcting Errors in MCS Quantitative Precipitation Forecasts of Intensity, Timing, and Location Using Machine Learning Algorithms

Tyreek JaQuan Frazier, Iowa State University, Ames, IA

Handout (1.1 MB)

674

Evaluation of Flash Drought Identification with Machine Learning Techniques, Part 1: Standard Machine Learning Algorithms

Stuart Galen Edris, University of Oklahoma, Norman, OK; and J. B. Basara, J. I. Christian, J. C. Furtado, A. McGovern, and X. Xiao

Handout (10.6 MB)

675

Evaluation of Flash Drought Identification with Machine Learning Techniques, Part 2: Common Deep Learning Algorithms

Stuart Galen Edris, University of Oklahoma, Norman, OK; and J. B. Basara, J. I. Christian, J. C. Furtado, A. McGovern, and X. Xiao

Handout (10.6 MB)

676

Enhancing Snowfall Accumulation Prediction in the Northeastern United States: Integrating NWP, Probabilistic Forecast and Machine Learning

Ummul Khaira, University of Connecticut, Storrs, CT; and M. Astitha

677

Bias Correction of Global Ensemble Forecast System (GEFS) Precipitation Forecasts using Long Short Term Memory (LSTM) Model over Complex Terrain Regions

Md Abul Ehsan Bhuiyan, National Oceanic and Atmospheric Administration, College Park, MD; and W. M. Thiaw and E. B. Bekele

678

Exploration of Machine Learning Methods from Decision Trees to Long-Short Term Memory Operators to Quality Control Soil Moisture Observations.

Ronald D. Leeper, MA in Geoscience and GIS Certificate, NCState & Cooperative Institute for Satellite Earth System Studies, Asheville, NC; and G. Graham, J. Alexander, V. Sudhakar, and M. A. Palecki

679

Weather Intelligence in Support of Developing Enhanced Decision Capabilities and the Modernization of Smart Military Installations

Brendon Hoch, US Army Corps of Engineers (USACE) Engineering Research Development Center (ERDC), Hanover, NH; and H. Bastian, I. Obiako, G. E. Gallarno, C. Rinaudo, J. Richards, R. Buchanan, N. Myers, E. Specking, G. S. Parnell, and M. Marufuzzman

Handout (4.3 MB)

680

Estimating Uncertainty of Water Temperature Predictions for Cold-Stunning Events in the Laguna Madre

Hector Miguel Marrero-Colominas, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and M. Shotande, A. H. Fagg, M. C. White, P. Tissot, and A. McGovern

681

The Application of "Deep Learning" Neural Networks for Sensor Failure Identification in the Kansas Mesonet

Cameron Howard Cousino, Ohio University, Athens, OH; and C. A. Redmond

4:30 PM-6:00 PM: Wednesday, 31 January 2024

Session 12B

AI for Radars: Observations, Data Processing, Classification, Estimation, and Nowcasting

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: David Ryglicki, NRL; Kyle A. Hilburn

4:30 PM

12B.1

Partial Beam Blockage Correction for Improving Radar Quantitative Precipitation Estimation

Songjian Tan, Colorado State University, Fort Collins, CO; and H. Chen

4:45 PM

12B.2

Evaluating and Optimizing the MRMS Machine Learning QPE Performance over the Western CONUS

Andrew P. Osborne, CIWRO/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and J. Zhang, R. A. Clark III, and K. Howard

5:00 PM

12B.3

Tornado Detection using Deep Neural Networks and Full-Resolution Polarimetric Weather Radar Data

Mark Sanford Veillette, MIT Lincoln Laboratory, Lexington, MA; and J. M. Kurdzo, S. Samsi, P. M. Stepanian, J. McDonald, and J. Y. Cho

5:15 PM

12B.4

Automatic AI Severe Weather Alerting from Radar Data

Matej Choma, Meteopress, Prague, Czech republic; and M. Murín, J. Bartel, M. Troller, and M. Najman

5:30 PM

12B.5

GeoRain v2.0: Enhancing Radar Rainfall Maps Using a Diffusion-Based Generative AI Approach with Satellite Imagery

Doyi Kim, SI Analytics, Daejeon, Yuseong-gu, South korea; and Y. Choi

5:45 PM

12B.6

Data Fusion Approach for Precipitation Nowcasting with ConvLSTM

Otavio Medeiros Feitosa, INPE, São José dos Campos, SP, Brazil; INPE, São José dos Campos - SP, SP, Brazil; and S. Freitas, H. F. D. C. Velho, and A. D. Chovert

J12A

Artificial Intelligence for Actionable Insights and Applications in Climate Science

Location: 345/346 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; the 37th Conference on Climate Variability and Change; and the Presidential Conference )

Cochairs: Marybeth Arcodia, University of Miami RSMAS; Eleanor A. Middlemas, PhD; Zane Martin; Katie Dagon

4:30 PM

J12A.1

ML to Accelerate Actionable Climate Information: The Importance of Uncertainty Quantification (Invited Presentation)

Hannah Christensen, Univ. of Oxford, Oxford, OXF, United kingdom

4:45 PM

J12A.2

A Data-Driven Approach to Identifying Key Regions of Change Associated with Future Climate Scenarios

Zachary Michael Labe, PhD, Princeton University, Princeton, NJ; and T. L. Delworth, N. Johnson, and W. Cooke

Handout (10.5 MB)

5:00 PM

J12A.3

Understanding Predictability of Extreme SEUS Precipitation Using Explainable Machine Learning

Kathleen Pegion, University of Oklahoma, Norman, OK; University of Oklahoma, Norman, OK; and E. J. Becker and B. Kirtman

5:15 PM

J12A.4

Physics-Informed Prediction of Arctic Sea Ice Drift

Heather Hunter, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD; and M. R. Keller, P. Sicurello, and C. Piatko

5:30 PM

J12A.5

Assessing Global Human Impact and Climate Hazards with Landsat Data and Convolutional Neural Networks

Bryam Orihuela-Pinto, Colorado State University, Fort Collins, CO; and P. Keys, E. A. Barnes, and F. V. Davenport

5:45 PM

J12A.6

A Data-Driven Model of Urban Carbon Emissions at the Human Scale

Constantine Kontokosta, New York University, BROOKLYN, NY; and B. Bonczak

6:00 PM-7:30 PM: Wednesday, 31 January 2024

Awards Reception and Ceremony

Location: Ballroom III/ IV (The Baltimore Convention Center)

Hosts: (Joint between the Networking and Events; the Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities; the 12th AMS Conference for Early Career Professionals; the Presidential Conference; the Daniel Keyser Symposium; the Kuo-Nan Liou Symposium; the 40th Conference on Environmental Information Processing Technologies; the 38th Conference on Hydrology; the 37th Conference on Climate Variability and Change; the 33rd Conference on Education; the 28th Conference on Applied Climatology; the 28th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Ocean, and Land Surface; the 28th Conference on Probability and Statistics; the 27th Conference of Atmospheric Science Librarians International; the 26th Conference on Atmospheric Chemistry; the 26th Conference on Satellite Meteorology, Oceanography, and Climatology; the 24th Conference on Aviation, Range, and Aerospace Meteorology; the 24th Symposium on Meteorological Observation and Instrumentation; the 24th Conference on Planned and Inadvertent Weather Modification; the 23rd Conference on Artificial Intelligence for Environmental Science; the 23rd Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA; the 22nd Symposium on the Coastal Environment; the 22nd History Symposium; the 21st Conference on Space Weather; the 20th Annual Symposium on Operational Environmental Satellite Systems; the 19th Symposium on Societal Applications: Policy, Research and Practice; the 16th Symposium on Aerosol Cloud Climate Interactions; the 15th Conference on Environment and Health; the 15th Conference on Weather, Water, Climate, and the New Energy Economy; the 14th Conference on Transition of Research to Operations; the 14th Symposium on Advances in Modeling and Analysis Using the Programming Languages of Open Science; the 12th Symposium on the Weather, Water, and Climate Enterprise; the 12th Symposium on Building a Weather-Ready Nation; the 12th AMS Symposium on the Joint Center for Satellite Data Assimilation; the 12th Symposium on the Madden-Julian Oscillation and Sub-Seasonal Monsoon Variability; the 10th Symposium on High Performance Computing for Weather, Water, and Climate; the Ninth Symposium on US-International Partnerships; the Sixth Special Symposium on Tropical Meteorology and Tropical Cyclones; the Fifth Symposium on Diversity, Equity, and Inclusion; the Third Symposium on Community Modeling and Innovation; the Second Symposium on Environmental Security; the Second Symposium on the Future of Weather, Forecasting, and Practice; the First Symposium on Cloud Physics; the Estimating Wind Speeds of Tornadoes and Other Windstorms; the Polar Meteorology and Oceanography Special Symposium; the Forum on Climate Linked Economics; and the 20th Conference on Major Weather Impacts )

7:30 PM-9:30 PM: Wednesday, 31 January 2024

Wednesday Night Live

Location: Key Ballroom Salon 1-8 (Hilton Baltimore Inner Harbor)

Thursday, 1 February 2024

8:30 AM-10:00 AM: Thursday, 1 February 2024

Session 13A

Applications of Artificial Intelligence for Improved Estimation and Prediction of Extreme Precipitation and/or Drought in Weather and Climate I

Location: 345/346 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the Presidential Conference )

Cochairs: Philippe E. Tissot; Alexandra Mazurek, Colorado State University

8:30 AM

13A.1

Identifying Dominant Southeastern United States Warm-Season Precipitation Features from Numerical Weather Prediction Forecasts

Andrew E. Mercer, Mississippi State Univ., Mississippi State, MS; and J. Dyer

8:45 AM

13A.2

A Machine Learning Emulator for West African Monsoon Prediction

Charlotte Merchant, Princeton University, Princeton, NJ; and W. Yang and G. A. Vecchi

9:00 AM

13A.3

Enhancing Regional Quantitative Precipitation Forecasts Using Machine Learning in Western US Watersheds

Weiming Hu, Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, UC San Diego, La Jolla, CA; James Madison University, Harrisonburg, VA; and M. Ghazvinian, PhD, A. Sengupta, M. Pan, and L. Delle Monache

9:15 AM

13A.4

Bias Correction of Global Blended Satellite Precipitation Products over Taiwan with Deep Learning

Liping Wang, Colorado State University, Fort Collins, CO; Colorado State University, Fort Collins, CO; and H. Chen, Y. Chen, P. Xie, C. R. Chen, and J. Q. Stewart

9:30 AM

13A.5

Radar Based Quantitative Precipitation Estimation Using Deep Learning over the UAE region

EunYeol Kim, Colorado State University, Fort Collins, CO; and V. Chandrasekar, C. Radhakrishnan, L. Delle Monache, and D. Axisa

9:45 AM

13A.6

Evaluation of a Convolutional Neural Network to Predict Wintertime Orographic Precipitation Gradients of the Western CONUS

Savanna Wolvin, Univ. of Utah, Salt Lake City, UT; and C. Strong, S. Brewer, and J. Steenburgh

Session 13B

Applications of Machine Learning to Remote Sensing of Aerosol, Cloud, and Precipitation Properties

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Christina E. Kumler, CIRA; Ryan A. Sobash

8:30 AM

13B.1

A Generalized Deep Neural Network for Estimating Severe Hail Likelihood from Satellite Infrared Cloud Top Patterns and Microwave Radiances

Benjamin Scarino, NASA, Hampton, VA; and K. M. Bedka, S. D. Bang, K. Itterly, and D. J. Cecil

8:45 AM

13B.2

Generating Synthetic Visible Satellite Data with Machine Learning

Tim W Reid, MIT Lincoln Laboratory, Lexington, MA; and P. Khorrami, O. Simek, and M. S. Veillette

9:00 AM

13B.3

Exploring Texture Analysis to Aid Classification of Meteorological Phenomena in Satellite Imagery

Kristina Moen, Colorado State University, Fort Collins, CO; and N. J. Mitchell, Y. Lee, L. Ver Hoef, E. J. King, I. Ebert-Uphoff, K. A. Hilburn, and W. Line

9:15 AM

13B.4

Three-Dimensional Convective Updraft Cell Segmentation Using Deep Learning

Md. Rafsan Jani, Morgan State University, Baltimore, MD; and C. Padilla, M. R. Hasan, A. O. Ajala, X. Li, and M. M. Rahman

13B.5

Forecast Correction Models for Systematic Errors in Different Atmospheric Cloud Regimes.

9:45 AM

13B.6

Near-Real-Time Aerosol Retrievals Via Neural Networks: Application to OMPS Limb Profiler Measurements

Michael D. Himes, NASA Postdoctoral Program, Greenbelt, MD; and G. Taha, T. Zhu, D. Kahn, and N. A. Kramarova

J13C

Towards Operationalizing AI/ML Weather Forecast and Decision Support Products I

Location: 327 (The Baltimore Convention Center)

Hosts: (Joint between the 14th Conference on Transition of Research to Operations; and the 23rd Conference on Artificial Intelligence for Environmental Science )

Cochairs: Chandra R. Kondragunta, NWS; Thanh Vo, 1970

8:30 AM

J13C.1

Aphelion Wind: A Decision-Support Tool for Wind Farm Operators

Juan Manuel González Sopeña, SOLUTE, San Sebastián de los Reyes, Spain; and R. Canito Rubio, A. de la Iglesia Núñez, F. J. Díaz Guadamillas, J. J. Espinosa Martínez, I. García Llona, E. Sánchez, and H. Torres López

8:45 AM

J13C.2

Joint Technology Transfer Initiative: Transitioning AI/ML Research to the NWS Operations

Chandra R. Kondragunta, OAR, Silver Spring, MD; and A. Pratt, R. S. Schumacher, and J. A. Nelson Jr.

9:00 AM

J13C.3

Operationalizing ML-Based S2S Forecasts at Salient

Sam Levang, Salient Predictions, Cambridge, MA; and R. Schmitt and B. Zimmerman

9:15 AM

J13C.4

Implementing Machine Learning to Support National-Level Flood Impact Forecasting for the U.S.

David C. Smith, PhD., NWS, Tuscaloosa, AL

9:30 AM

J13C.5

Building Scalable, Cloud-Native Systems for Low-Latency Weather Forecasting at Zeus AI

Kate Duffy, Zeus AI, Arlington, MA; and T. Vandal and D. McDuff

9:45 AM

J13C.6

Visualizing Data-Driven AI Models to Engage Operational Forecasters

Jacob T. Radford, CIRA, Fort Collins, CO; and I. Ebert-Uphoff, J. Q. Stewart, R. T. DeMaria, T. Wilson, J. L. Demuth, M. S. Wandishin, J. Duda, A. McGovern, C. D. Wirz, and M. G. Cains

10:00 AM-10:45 AM: Thursday, 1 February 2024

Exhibit Hall Breakfast

Location: Hall F/ Swing (The Baltimore Convention Center)

12:00 PM-1:45 PM: Thursday, 1 February 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Thursday, 1 February 2024

Session 15A

Applications of Artificial Intelligence for Improved Estimation and Prediction of Extreme Precipitation and/or Drought in Weather and Climate II

Location: 345/346 (The Baltimore Convention Center)

Hosts: (Joint between the 23rd Conference on Artificial Intelligence for Environmental Science; and the Presidential Conference )

Cochairs: Philippe E. Tissot; Jacob T. Radford

1:45 PM

15A.1

A Novel Quantile Regression Neural Network Scheme for Postprocessing Medium-range Ensemble Quantitative Precipitation Forecasts

Mohammadvaghef Ghazvinian, PhD, Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, Univ. of California, San Diego, La Jolla, CA; and S. Candido, L. Delle Monache, V. Afzali Gorooh, A. Sengupta, W. Hu, and M. M. Ralph

15A.2

North American Monsoon Prediction Using Causality Informed Machine Learning

2:00 PM

15A.3

Comparison of Regression Algorithms in Multivariable Drought Modeling and Analysis

Maria Chzhen, University of Toronto, Toronto, ON, Canada

2:30 PM

15A.4

Diagnosing Extreme Precipitation from Atmospheric Fields with a UNet Architecture on a HEALPix Mesh

Raul Antonio Moreno, University of Washington, Seattle, WA; and N. A. Cresswell-Clay and D. R. Durran

2:45 PM

15A.5

Exploring the Treatment of Predictors for Forecasting Excessive Rainfall with Random Forests Based on a Deterministic Convection-Allowing Model

Eric P. James, GSD, Boulder, CO; and R. S. Schumacher

Session 15B

Explainable Artificial Intelligence (XAI) for Environmental Science II

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Christina E. Kumler, CIRA; Aaron J. Hill, Colorado State University

1:45 PM

15B.1

Assessing the Predictability of Downslope Windstorms in the Rocky Mountain Front Range Using Explainable Convolutional Neural Networks

Casey L Zoellick, M.S. in Atmospheric Sciences, Colorado State Univ., Fort Collins, CO; and R. S. Schumacher

2:00 PM

15B.2

Explaining the Role of Lightning Data in Hail Nowcasting

Jay Calder Rothenberger, Vaisala, Louisville, CO; National Science Foundation Trustworthy AI Institute for Weather Climate and Coastal Oceanography, Norman, OK; and E. P. Grimit, M. J. Murphy, and R. Wallace, PhD

2:15 PM

15B.3

Distinguishing the Regional Emergence of United States Summer Temperatures Between Observations and Climate Model Large Ensembles

Zachary Michael Labe, PhD, Princeton University, Princeton, NJ; and N. Johnson and T. L. Delworth

Handout (18.0 MB)

2:30 PM

15B.4

Comparing Predictor Influence for a Machine Learning Model in the Southwest United States for Three Different Excessive Rainfall Events

Jacob A Escobedo, Colorado State Univ., Fort Collins, CO; and R. S. Schumacher

2:45 PM

15B.5

Ingredients-Based Explainability: Using Tree Interpreter to Disaggregate a Random Forest's Severe Weather Predictions

Alexandra Mazurek, Colorado State Univ., Fort Collins, CO; and A. J. Hill, R. S. Schumacher, and H. J. McDaniel

Session 15B

New Concepts in Advanced Modeling of Earth System and Digital Twin Technology

Location: 316 (The Baltimore Convention Center)

Hosts: (Joint between the 20th Annual Symposium on Operational Environmental Satellite Systems; and the 23rd Conference on Artificial Intelligence for Environmental Science )

Cochairs: Beau Backus, NOAA; Ramesh Rangachar

1:45 PM

15B.1

Overview and Status of of the Digital Twin for Earth Observations Using Artificial Intelligence Broad Agency Announcement Activity

Ramesh Rangachar, The Aerospace Corporation, Silver Spring, MD; and L. Mayo, D. Spencer, B. Backus, H. Patel, S. L. Bunin, S. Wright, K. Hayes, and M. Johnson

2:00 PM

15B.2

Lessons Learned from Building an Earth Observation Digital Twin in One Year

Rebekah Esmaili, Science and Technology Corp., Columbia, MD; and K. Narasimhan, MSEE, M. R. Schoeberl, and Y. Wang

2:15 PM

15B.3

AI-Based Earth and Space Observing Digital Twin Prototype

Lynn Montgomery, Lockheed Martin Space, Boulder, CO; and P. Davis, G. Hoffmann, B. Ballance, J. Stone, J. Lance, M. Fingerhut, A. Linenfelser, T. Kaye, and M. Wear

2:30 PM

15B.4

Orion Space Solutions Prototype Earth Observations Digital Twin System

Jeff Steward, Orion Space, Louisville, CO

Handout (4.1 MB)

2:45 PM

15B.5

NASA Earth System Digital Twins

Jacqueline Le Moigne, Earth Science Technology Office, Greenbelt, MD

3:00 PM-3:40 PM: Thursday, 1 February 2024

23rd Conference on Artificial Intelligence for Environmental Science ePosters (Thursday)

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

E99

Improving the Forecast of Extreme Rainfall Event “21.7” in Henan Province Base on a Physical Guided RainNet

Qi Zhong, China Meteorological Administration, Beijing, 11, China; and Z. Zhang and S. Hou

E100

Explainable Deep Learning for Climate Applications Using the Spectral Analysis of Regression Activations and Kernels (SpARK) Framework

Yifei Guan, Rice University, Houston, TX; and A. K. Chattopadhyay, A. Subel, H. A. Pahlavan, and P. Hassanzadeh

3:00 PM-4:30 PM: Thursday, 1 February 2024

Coffee Break & Formal Poster Viewing

Location: Hall E (The Baltimore Convention Center)

Posters IV

Location: Hall E (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

893

Exploring Cross-Validation Techniques for ML Predictions of Rare Cold-Stunning Events

Jarett Woodall, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and M. C. White, H. Marrero, M. Vicens-Miquel, and P. E. Tissot

894

A Performance Comparison Of Clustering Techniques For Obtaining Freshwater Extents Of Rangeland Ponds And Lakes In Africa Using Earth Observations

Soni Yatheendradas, The University of Maryland, College Park, MD; and K. Slinski

895

Decoding Climate Complexity: Novel Approaches to S2S Forecasting through Advanced AI Technology

Pratik Shukla, Univ. of Maryland Baltimore County, Baltimore, MD; and M. Halem

896

Detecting and Tracking Iceberg A-76A from VIIRS Observations with U-Net Deep Learning Model

Tiancheng Steven Shao, Montgomery Blair High School, Silver Spring, MD; and B. Zhang, S. Uprety, and J. Dong

Deep Learning Enhancements for Global Synthetic Radar Analyses

897

Can Neural Networks Learn to See Airborne Dust and Sand in Thermal Satellite Imagery?

Micah Wallace, GESTAR II, Baltimore, MD; and I. T. Carroll and A. M. Sayer

898

Development of U.S. Based 20-Year Daily, 1-Km-Resolution, Multi-Species (PM_2.5, O₃, NO₂, Dust) Surface Air Pollution Estimates By Machine Learning Data Fusion: Incorporating 1-Km Emission and 12-Km CMAQ

Beiming Tang, NOAA, fairfax, VA; and D. Tong, Y. Li, S. Ma, C. Chang, B. H. Baek, Y. Tang, and B. Baker

899

Improving the Sharpness of Deep Learning Generated Weather Predictions

Michael Yu, AI2ES & University of Oklahoma, Norman, OK; and M. M. Madsen and A. McGovern

900

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

Jared Daniel Willard, LBNL, Berkeley, CA; and P. Harrington, S. Subramanian, A. Mahesh, T. A. O'Brien, and W. D. Collins

Manuscript (320.2 kB)

901

Advancing Global Land Surface Albedo Parameterization with Physics-Informed Machine Learning Methods

Akarsh Ralhan, University of Maryland, College Park, College Park, MD; and C. Sun and X. Z. Liang

902

Exploring Data-Driven Equation Discovery to Model Moisture Flux

Rebecca Z Porter, UCAR, Olathe, KS; and Y. Huang and P. Gentine

903

Testing Machine Learning Methods for Downscaling in the 3D RTMA project

Miodrag Rancic, Lynker, College Park, MD; and A. M. Gibbs, M. Pondeca, R. J. Purser, T. lei, E. Colon, M. T. Morris, and G. Zhao

904

Using Machine Learning to Produce Watch-to-Warning Severe Weather Guidance

Sam Varga, University of Oklahoma, Norman, OK; Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, OK; National Severe Storms Laboratory, Norman, OK; and M. L. Flora and C. K. Potvin

Handout (3.2 MB)

905

Educational Project on Machine Learning-Based Prediction of Tornados Under DHS STEM Enhancement Program

Francis Tuluri, Jackson State University, Jackson, MS; and R. S. Reddy Sr., E. Reddy, and B. Blanton

4:30 PM-6:00 PM: Thursday, 1 February 2024

Session 16A

Pure AI and Data-Driven Weather Forecasts III: Operations and Applications with AI Forecast Models

Location: 345/346 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Christina E. Kumler, CIRA; Daniel Rothenberg

4:30 PM

16A.1

Operational Data-Driven Monthly Forecasting at Microsoft

Jonathan A Weyn, Microsoft, Redmond, WA; and D. Kumar, S. Klocek, N. Kazmi, R. Zhang, P. Luferenko, and K. Thambiratnam

4:45 PM

16A.2

An Operational, Purely Data-Driven Seasonal Forecast System

Benjamin A. Toms, Intersphere, Fort Collins, CO; and J. Antic and J. Cahill

5:00 PM

16A.3

Harnessing Data-driven Neural Weather Models for Climate Attribution: A Case Study of the Oroville Dam Atmospheric River Episode of February 2017

Jorge Luis Bano Medina, SIO, San Diego, CA; Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, Univ. of California, San Diego, La Jolla, CA; and A. Sengupta, L. Delle Monache, W. Hu, and D. Watson-Parris

5:15 PM

16A.4

Exploring the Potential of Data-Driven AI Models for Operational Weather Forecasting in Finland

Leila Hieta, Finnish Meteorological Institute, Helsinki, Finland; and M. Partio, M. Rauhala, and T. Riihisaari

5:30 PM

16A.5

Regional Down-Scaling with Generative Diffusion Models

Noah D. Brenowitz, NVIDIA, Seattle, WA; and M. Mardani, Y. Cohen, J. Pathak, J. Chen, A. Vahdat, K. Kashinath, J. Kautz, and M. Pritchard

5:45 PM

16A.6

Rapid Update Nowcasting with AI fusion of NWP and Other Data Sources

Matej Murín, Meteopress, Prague, Czech republic; and M. Choma, J. Bartel, M. Troller, P. Šimánek, and M. Najman

Session 16B

Artificial Intelligence for Environmental Science III

Location: 338 (The Baltimore Convention Center)

Host: 23rd Conference on Artificial Intelligence for Environmental Science

Cochairs: Philippe E. Tissot; Rob Redmon; Ryan Lagerquist

4:30 PM

16B.1

NOAA Operational Air Quality Forecasting Guidance - Using Artificial Intelligence to Achieve Faster and Adaptive AQ Forecasting

Jennifer Sleeman, APL, Laurel, MD; and I. Stajner, C. Ribaudo, D. Chung, C. Ashcraft, R. Chen, C. Tang, C. Kofroth, Q. H. Dang, M. Halem, K. Wang, J. Huang, H. C. Huang, R. Montuoro, C. A. Keller, J. M. Wilczak, I. V. Djalalova, J. McQueen, B. Baker, V. Krasnopolsky, W. Putman, and M. Hughes

4:45 PM

16B.2

Machine Learning for Predicting Air Quality Using Land Images

Jing Wang, Princeton University, Princeton, NJ; and F. Gerges and E. Bou-Zeid

5:00 PM

16B.3

Generalized Visibility Estimation from Camera Images Using Deep Learning

Melissa Wilson Reyes, University of Oklahoma, Norman, OK; and A. Kurbanovas, A. H. Fagg, C. D. Thorncroft, K. J. Sulia, and J. Brotzge

5:15 PM

16B.4

Machine Learning applied to Urban Heat Island Intensity prediction at the Metropolitan Region of Sao Paulo, Brazil

Pedro Luis B. Almeida, University of Sao Paulo, São Paulo, SP, Brazil; and F. N. D. Ribeiro

5:30 PM

16B.5

Automated Treefall Detection using Zero-Shot Deep Learning

Elizabeth Tirone, CIMMS, Norman, OK; NSSL, Norman, OK; and M. A. Wagner, Z. Chen, D. Candela, E. Rasmussen, and M. C. Coniglio

5:45 PM

16B.6

Flood Detection and Mapping Through Sensor Fusion: A Comparative Study of Multi-Sensor Data Integration with UAV Optical Imagery

Mulham Fawakherji, north carolina agricultural and technical state university, Greensboro, NC; and L. Hashemi Beni

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Tuesday, 23 January 2024

1:00 PM-2:00 PM: Tuesday, 23 January 2024

Virtual Poster Slam Session #1

Presentations from 20OESS, 14R2O, 24ARAM, 23AI, 6TROPICAL, and 26SATMET

Sunday, 28 January 2024

4:00 PM-5:15 PM: Sunday, 28 January 2024

Presidential Forum: Climate Science as Service to Society

Location: Ballroom III/ IV (The Baltimore Convention Center)

Click here to view the recorded event.

Join us for a compelling fireside chat at the AMS 104th Annual Meeting’s Presidential Forum, exploring the multifaceted challenges and opportunities presented by climate change and emphasizing the importance of scientific advances, policy development, and public engagement.

Climate and environmental change are now altering our daily lives, and will dramatically affect our futures. Climate science is key to how we will all live in this changing environment, helping us understand what is happening and anticipate what is to come. But what can we do as scientists and professionals to ensure that our science is communicated and understood as a public service, and see that it is put to work to help build a solid future? Our 2024 AMS Presidential Forum will discuss how we can work to overcome significant barriers that limit the reach and impact of climate science, and of science-based responses to environmental change.

Moderated by distinguished and broadly recognized atmospheric scientist Dr. Kerry Emanuel, an expert on climate change and hurricane physics, a fireside chat will feature Monica Medina, President and CEO of the Wildlife Conservation Society, and Bob Inglis, Executive Director of RepublicEn.org. Both have fascinating stories to tell and are renowned for their broad and critical impact on advancing the societal response to climate change. AMS President Brad Colman will provide introductory comments.

Have a question during the event?

Ask via slido.com with code #3053080

6:30 PM-8:30 PM: Sunday, 28 January 2024

AMS Career Resource & Graduate School Fair

Location: Hall D (The Baltimore Convention Center)

Sponsored by Campbell Scientific, AccuWeather, Millersville University Department of Earth Sciences, Oklahoma University: School of Meteorology/College of Atmospheric and Geographic Sciences

Annual Meeting Welcome Reception

Location: Hall E (The Baltimore Convention Center)

Monday, 29 January 2024

8:30 AM-10:00 AM: Monday, 29 January 2024

Pure AI and Data-Driven Weather Forecasts I: Innovative Model Architectures and Applications

Location: 345/346 (The Baltimore Convention Center)

J1B

AI Advances in Tropical Meteorology: Tropical Cyclones, Sub-Seasonal Phenomena, and More

Location: 338 (The Baltimore Convention Center)

This covers topics in which AI is advancing the science of tropical cyclones, sub-seasonal phenomena, and other areas in tropical meteorology.

J1B

Tending the Treasure Trove: Advancing Stewardship for Nonsatellite Earth Observations

Location: 336 (The Baltimore Convention Center)

While satellite platforms enable critical operational, research, and discipline-bridging Earth observations, non-satellite platforms have also proven instrumental in each of these areas. In fact, many satellite-borne instruments begin development as ground- and aircraft-based analogs, and essential satellite calibration/validation work relies on observations collected by sensors on non-satellite platforms. We use the term non-satellite, or suborbital, platforms to include anything not in space that hosts instruments - this includes aircraft, ships/vessels, balloons, buoys, vehicles, and stationary sites. Data obtained from instruments on these platforms are typically used initially for a specific, relatively focused purpose, whether operational like weather forecasting and environmental monitoring, or for research in a variety of disciplines, including physical process studies, validation of satellite observations and algorithms, assessment of numerical model output, and more.

The heterogeneity of collections of non-satellite observations is as vast as the science applications they serve, and this complexity creates significant challenges for data stewardship. Agencies are addressing these challenges in different ways. Publicly funded agencies have a responsibility to ensure access to data for emerging science questions and applications perhaps not envisioned or even possible at the time of data collection. As scientific studies now look to document and analyze changes to and variability in Earth’s natural processes, the treasure trove of suborbital data serve as irreplaceable records of the state of our planet at specific points in time and space. This session welcomes contributions relating to all aspects of non-satellite data stewardship, including data discovery, access, metadata, archival, formats and transformations, care of historical data, cloud optimization, and supporting FAIR and Open Science. Additionally, data user perspectives on challenges with locating, accessing, and using suborbital data, as well as the critical role these data can and will continue to play in assessing and documenting climate variability and change are encouraged.

The goal of the session is to advance inner- and cross-agency discussions and practices to protect and enhance the scientific and economic returns on the investment made in collecting these unique and valuable observations.

Potential Invited Presentations:

Overview of NASA’s Airborne and Field Resource Center
Promote the second Airborne and Field Data Workshop - which will be held in 2024 - and solicit for community interest/involvement
One or more current/recent field campaign data representative (a PI, data manager, Science Team member/SME data user, etc)

10:00 AM-10:45 AM: Monday, 29 January 2024

Coffee Break [Main Terrace (BCC), Camden Lobby (BCC), Hall E (BCC)]

Location: The Baltimore Convention Center

10:45 AM-12:00 PM: Monday, 29 January 2024

Artificial Intelligence for Environmental Science I

Location: 345/346 (The Baltimore Convention Center)

This session is open to a wide variety of research, active work, and advancements in applications of artificial intelligence or machine learning methods and models to environmental science.

Applying Uncertainty Quantification Methods to Environmental Artificial Intelligence Models

Location: 338 (The Baltimore Convention Center)

We invite abstracts that have experimented with AI uncertainty quantification methods applied to a variety of environmental science topics. In particular, we encourage abstracts highlighting what comes next after the uncertainty quantification and how to use the information gathered in model application.

12:00 PM-1:45 PM: Monday, 29 January 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Monday, 29 January 2024

Explainable Artificial Intelligence (XAI) for Environmental Science I

Location: 345/346 (The Baltimore Convention Center)

This session invites abstracts on projects utilizing explainable artificial intelligence methods in their models and/or post processing applications. We particularly encourage abstracts that describe how the XAI methods impacted the final model development and analysis and/or were utilized with model users in mind.

J3C

Using Artificial Intelligence to Analyze Satellite Earth Observations I

Location: 338 (The Baltimore Convention Center)

This session features applications of AI to analyzing satellite Earth Observation data.

3:00 PM-3:40 PM: Monday, 29 January 2024

23rd Conference on Artificial Intelligence for Environmental Science Eposters (Monday)

Location: Hall E (The Baltimore Convention Center)

3:00 PM-4:30 PM: Monday, 29 January 2024

Coffee Break & Formal Poster Viewing

Location: Hall E (The Baltimore Convention Center)

Posters I

Location: Hall E (The Baltimore Convention Center)

4:30 PM-5:45 PM: Monday, 29 January 2024

J4B

Developing Cloud-Based Tools for Data Analysis and Archiving I

Location: 336 (The Baltimore Convention Center)

Cloud-based technologies continue to evolve and mature in their use and application. This session will delve into the growing use of cloud hosting solutions applied to benefit the environmental sciences and specifically applications for data analysis, visualization and archiving of environmental information. This session encourages submissions on these topics, focusing on how submitters are incorporating cloud-based applications into their work, what they have done, and what advantages this approach has provided to their efforts.

4:30 PM-6:00 PM: Monday, 29 January 2024

Pure AI and Data-Driven Weather Forecasts II: Highlights on AI Forecast Performance and Evaluation

Location: 345/346 (The Baltimore Convention Center)

Recent advances in AI modeling techniques - particularly deep neural network architectures and infrastructure suitable for training on massive-scale datasets – have brought a renewed interest in developing “pure” AI-driven weather forecasts. Such a forecast would involve assembling some initial representation of the state of the atmosphere, feeding it into an AI model, and producing a rich set of dense, spatiotemporal output fields similar to the output of a numerical weather prediction (NWP) system; some researchers refer to such models as “AI NWP” (e.g. [1]). Unlike traditional NWP, pure AI forecasts are optimized end-to-end for forecast accuracy based on historical data. A proliferation of such models has been published in the early 2020’s, many of which perform favorably using typical large-scale forecast quality metrics relative to state of the art numerical forecasts for precipitation nowcasting [2] and medium-range [3, 4, 5, 6] prediction.

The rapid pace of development of these models, backed by non-traditional players in the weather enterprise (specifically “big tech” companies which may not have developed proprietary weather forecasts or data products in the past but have significant experience with very large data and AI applications) suggests that a new frontier in weather prediction is rapidly opening up. In this session, we invite perspectives, commentaries, and demonstrations of new models from this new frontier, and encourage thoughts on questions including the following:

What are the remaining barriers towards developing – and operationalizing – “pure” AI or “AI NWP” forecast systems?
Given the rapidly changing frontier of AI research and development, are there novel model architectures or problem formulations that we may see emerge as a new paradigm for developing pure AI forecast systems?
How will AI NWP products co-exist with the extant and continually evolving catalog of operational NWP systems?
Pure AI systems at different levels of completeness and maturity have been developed for precipitation nowcasting, medium-range weather and even S2S forecasts. Which weather forecasting timescales and applications will be hardest for pure AI approaches?
Are traditional skill metrics still adequate for evaluating “non-physical” AI NWP forecasts? If not, how should these forecasts instead be evaluated?

We especially welcome perspectives on how the broader AI and NWP communities might come together to further accelerate this weather forecasting revolution. Prospective authors may consider submitting abstracts that provide a performance assessment of predictions from their data-driven ML models which include comparisons to benchmark predictions from single deterministic NWP models, and/or an ensemble of NWP models. This session will encourage research into the use of purely data-driven ML models to generate predictions with skill comparable to that from state-of-the-art NWP models used by operational meteorologists worldwide.

[1] Chen, Kang, et al. "FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead." arXiv preprint arXiv:2304.02948 (2023).

[2] Espeholt, Lasse, et al. "Deep learning for twelve hour precipitation forecasts." Nature communications 13.1 (2022): 5145.

[3] Pathak, Jaideep, et al. "FourCastNet: A global data-driven high-resolution weather model using adaptive fourier neural operators." arXiv preprint arXiv:2202.11214 (2022).

[4] Lam, Remi, et al. "GraphCast: Learning skillful medium-range global weather forecasting." arXiv preprint arXiv:2212.12794 (2022).

[5] Keisler, Ryan. "Forecasting global weather with graph neural networks." arXiv preprint arXiv:2202.07575 (2022).

[6] Bi, Kaifeng, et al. "Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast." arXiv preprint arXiv:2211.02556 (2022)

AI Applications in the Energy Sector

Location: 347/348 (The Baltimore Convention Center)

J4C

Using Artificial Intelligence to Analyze Satellite Earth Observations II

Location: 338 (The Baltimore Convention Center)

6:00 PM-7:30 PM: Monday, 29 January 2024

Exhibit Hall Opening Reception

Location: Hall F/ Swing (The Baltimore Convention Center)

6:30 PM-9:00 PM: Monday, 29 January 2024

Technology and Data-science Mixer

Location: Key Ballroom Salon 3-4 (Hilton Baltimore Inner Harbor)

The Technology and Data-science Mixer is being co-hosted by four conferences (AI, Python, High-performance Computing, and ProbStat), so this will be a great place to network with other folks in tech / data science, including some less AI-focused people that you might not see all the time.

7:00 PM-9:00 PM: Monday, 29 January 2024

ESWN Reception

Location: Pratt Street Ale House

7:30 PM-10:00 PM: Monday, 29 January 2024

Coriolis (LGBTQ+ and Allies) Reception

Location: Pisces (Hyatt Regency Baltimore)

The 15th annual Coriolis Reception will be held in the Pisces Room at the Hyatt Hotel on Monday, January 29, 2024 from 7:30-10:00PM EST. This reception provides opportunities to mentor, network, and connect with individuals who share common professional and personal interests. Students and professionals at all stages of their careers and their spouses or partners are encouraged to attend. Light refreshments and a cash bar will be provided.

Tuesday, 30 January 2024

8:30 AM-9:45 AM: Tuesday, 30 January 2024

J5B

Developing Cloud-Based Tools for Data Analysis and Archiving II

Location: 336 (The Baltimore Convention Center)

8:30 AM-10:00 AM: Tuesday, 30 January 2024

J5A

AI for Statistical Parameterization of Unresolved Processes in Earth System Models I

Location: 345/346 (The Baltimore Convention Center)

Earth Systems Models (ESMs) are the primary tool used by scientists to understand earth system dynamics and variability and make future climate projections. The accuracy of ESMs has increased significantly in recent years due to advances in numerical modeling and computational tools, allowing them to be run with more precision at increasingly higher resolutions on high performance computers (HPC). Earth system processes are primarily described by non-linear differential equations that interact with other complex and non-linear components on differing spatial and temporal, resolved and unresolved scales. The increase in the degrees of freedom with resolution implies that solving these multi-scale equations for high resolution models becomes computationally prohibitive, even on leadership-class HPC. Moreover, the unresolved processes also account for a large source of uncertainty in ESMs, as they are approximated from the resolved scales through parameterization schemes.

Recently, researchers have been exploring the use of AI to aid parametrizations of a wide range of sub-grid scale processes including but not limited to radiation, convective processes, aerosol and cloud microphysics, atmospheric chemistry, ocean physics, and land processes. ML can be used to create new parametrizations, or improve current ones, by training a statistical model to effectively emulate a complex physical phenomenon. ML techniques are capable of learning and accurately simulating nonlinear functions when trained on large datasets. Once trained, these models can provide fast calculation of sub-grid scale processes, particularly on hybrid architectures. Current ML tools used for parametrization tasks include neural networks (NN), deep learning, random forests (RF), and general adversarial networks (GAN). Several studies have also shown the usefulness of hybrid approaches called physics informed ML, also known as knowledge guided ML, for incorporating domain knowledge and physical principles into the ML techniques mentioned above. The main challenges of utilizing these ML approaches include the limited explainability and uncertainty quantification of the resulting models. Alleviating these limitations would boost our confidence in the applicability of the ML models to future projections.

We aim to explore different ways ML techniques can assist in defining and representing parametrization processes across earth system modeling. Submissions related to the use of ML techniques for ESM parametrization including emulation, replacement and acceleration of current parameterization schemes are sought to further our understanding of advanced ways to address model run-time expense, accuracy, sensitivity and uncertainty.

J5B

Advances in the Use of Artificial Intelligence in Support of Aviation, Range, and Aerospace Meteorology

Location: 338 (The Baltimore Convention Center)

This session will focus on applications of artificial intelligence (AI) and machine learning (ML) to problems in ARAM research and operations. Topics may include the use of AI and ML algorithms in conjunction with data from observing systems and numerical weather models that can be applied to a wide variety of aviation and aerospace research and operational areas. Themes could include the use of AI/ML in current research endeavors, as well as the integration of AI/ML is operations for commercial and military aviation and range activities.

10:00 AM-10:45 AM: Tuesday, 30 January 2024

Coffee Break [Main Terrace (BCC), Camden Lobby (BCC), Hall E (BCC), Hall F (BCC)]

Location: The Baltimore Convention Center

12:00 PM-1:45 PM: Tuesday, 30 January 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Tuesday, 30 January 2024

Artificial Intelligence for Environmental Science II

Location: 338 (The Baltimore Convention Center)

J7A

AI for Statistical Parameterization of Unresolved Processes in Earth System Models II

Location: 345/346 (The Baltimore Convention Center)

J7A

FAIR and Open Data and Software within the Atmospheric and Ocean Sciences to Support Transparent, Reusable, and Efficient Research and Operations I

Location: 337 (The Baltimore Convention Center)

The need to understand linkages between climatic extremes and security outcomes such as displacement/migration, public health, and food/electricity production has never been higher. Analysis of these linkages requires integration of the Earth, social, economic, and political sciences as well as equitable access to community datasets, data proximate compute, community developed data analysis workflows and open source software tools. In this session, we invite submissions that discuss research, applications and initiatives that build upon the principles of FAIR (Findable, Accessible, Interoperable, and Reusable) Open Data and Software within the atmospheric and related sciences to drive new discoveries. We welcome technical, policy, and community-focused submissions on a range of topics related to facilitating transparency of science and leveraging community supported open science capabilities to drive research and operational outcomes. Examples include:

- Research, operations and industry uses that employ a combination of public cloud compute, cloud hosted datasets such as those provided through NOAA’s Open Data Dissemination Program, and open source data analysis tools and workflows. With the increasing use of constellations of sensors and more and more observations being processed at the point of collection, this session explores the use of the Internet of Things to gather data and edge computing to process data. An example of this kind of technology is the Smart Great Lakes project (https://www.glos.us/smartgreatlakes/). Topics in this session might include: the deployment of IoT sensors for the collection of environmental data, techniques for processing data at the point of collection, integrating new data sources with existing data architectures, AI and the use of IoT sensors, the use of the cloud to gather and disseminate data, and information ecosystems based on smart technologies.
- Solutions for data and software discoverability, metadata creation and management, provenance tracking, and other relevant topics.

Platforms for reproducible research and reusable tools will accelerate the analytics enterprise and build the salience, credibility, and legitimacy required to effectively inform policy. This session will highlight emerging open-science tools and platforms for weather and climate-security analysis.

3:00 PM-4:30 PM: Tuesday, 30 January 2024

Coffee Break & Formal Poster Viewing [Hall E (BCC) & Hall F (BCC)]

Location: Hall E (The Baltimore Convention Center)

Posters II

Location: Hall E (The Baltimore Convention Center)

4:30 PM-6:00 PM: Tuesday, 30 January 2024

Living in a Changing Environment: Applications of Artificial Intelligence and Machine Learning for Weather Forecasting

Location: Johnson AB (Hilton Baltimore Inner Harbor)

This session topic will include joint sessions on AI and weather forecasting. How will AI advance weather forecasting capabilities across the weather enterprise? We are soliciting an array of abstracts on the topic and welcome submissions related to, but not limited to, purely data-driven forecasting, AI-NWP hybrid applications, and AI-based post processing for forecasting applications.

Statistics and Machine Learning for Climate Science

Location: 302/303 (The Baltimore Convention Center)

This session seeks presentations related to the use of statistics an machine learning in Climate Science. It is Joint between the 28th Conference on Probability and Statistics, 23st Conference on Artificial Intelligence for Environmental Science, and the 35th Conference on Climate Variability and Change

J8A

FAIR and Open Data and Software within the Atmospheric and Ocean Sciences to Support Transparent, Reusable and Efficient Research and Operations II

Location: 337 (The Baltimore Convention Center)

Research, operations and industry uses that employ a combination of public cloud compute, cloud hosted datasets such as those provided through NOAA’s Open Data Dissemination Program, and open source data analysis tools and workflows. With the increasing use of constellations of sensors and more and more observations being processed at the point of collection, this session explores the use of the Internet of Things to gather data and edge computing to process data. An example of this kind of technology is the Smart Great Lakes project (https://www.glos.us/smartgreatlakes/). Topics in this session might include: the deployment of IoT sensors for the collection of environmental data, techniques for processing data at the point of collection, integrating new data sources with existing data architectures, AI and the use of IoT sensors, the use of the cloud to gather and disseminate data, and information ecosystems based on smart technologies.
Solutions for data and software discoverability, metadata creation and management, provenance tracking, and other relevant topics.

J8A

Machine Learning for Smoke and Wildfires

Location: 345/346 (The Baltimore Convention Center)

This session will highlight abstracts utilizing machine learning methods in applications to aerosols and wildfires. This can range from short- to long term- forecasts.

J8B

Applications of Artificial Intelligence to the Coastal Environment

Location: 338 (The Baltimore Convention Center)

The session will discuss the development, adaptation, application of AI models and AI based methods to the coastal environment including stakeholders needs and feedback.

J8B

Cloud Computing for Big Data in Atmosphere, Ocean, and Climate

Location: 336 (The Baltimore Convention Center)

Weather imposes constraints on human activity. As a consequence, most decision-makers/planners seek awareness to mitigate or eliminate weather impacts. As datasets become larger and larger, new and improved tools to work with Big Data are critical. This session welcomes contributions from research fields such as scientific visualization, information visualization or visual analytics that are applicable to large data sets from climatology, meteorology or related disciplines. Presentations on using cloud computing for analyzing satellite and model data for weather, ocean, or climate relevant applications will also be welcomed.

6:00 PM-7:00 PM: Tuesday, 30 January 2024

Exhibit Hall Reception

Location: Hall F/ Swing (The Baltimore Convention Center)

Wednesday, 31 January 2024

8:30 AM-10:00 AM: Wednesday, 31 January 2024

Improvements to Subseasonal-to-Seasonal (S2S) Predictions Using Novel Statistical and Artificial Intelligence/Machine Learning (AI/ML) Methods I

Location: 345/346 (The Baltimore Convention Center)

Subseasonal-to-Seasonal (S2S) forecasting (between two weeks and a season ahead) is a rapidly developing area of forecasting, with the potential to provide valuable information for the development of climate services. Although S2S climate predictions have a comparative lack of skill beyond two-week lead times, over the past decade there has been a substantial research effort to improve prediction skill via novel advanced statistical and Artificial Intelligence/Machine Learning (AI/ML) methods either in terms of post-processing of the dynamical model output or data-driven models based on teleconnections. Additionally, there is a strong interest in understanding predictability on S2S scales using eXplainable Artificial Intelligence (XAI) which could help to improve forecast skill.

This session welcomes all aspects of improving forecasting on S2S scales including advanced statistical and Artificial Intelligence/Machine Learning (AI/ML) based post-processing (bias correction, multi-model ensemble) of the dynamical model output, and ML models based on teleconnections (empirical/data driven). Abstracts that explore XAI for predictability are also encouraged.

J9B

Language Transformer, ChatGPT and Other Natural Language Processing Machine Learning in Weather Enterprise

Location: 338 (The Baltimore Convention Center)

Have you used chatGPT in your environmental related projects or applications? Have you experimented with applying language models to your field of work? This session invites abstract submissions that have used ChatGPT or other language models in their line of work. Examples can include but are not limited to using ChatGPT to pull data, write code, issue statements, etc.

10:00 AM-10:45 AM: Wednesday, 31 January 2024

Coffee Break [Main Terrace (BCC), Camden Lobby (BCC), Hall E (BCC), Hall F (BCC)]

Location: The Baltimore Convention Center

10:45 AM-12:00 PM: Wednesday, 31 January 2024

10A

Improvements to Subseasonal-to-Seasonal (S2S) Predictions Using Novel Statistical and Artificial Intelligence/Machine Learning (AI/ML) Methods II

Location: 345/346 (The Baltimore Convention Center)

J10B

From Data to Decision: AI at the Intersection of Meteorology, Climate, and Society

Location: 338 (The Baltimore Convention Center)

We propose a joint session between the conference on artificial intelligence for environmental science and the symposium on societal applications: policy, research and practice.

AI is permeating nearly ever aspect of society. Language models like ChatGPT have dominated headlines, while in the realm of environmental sciences data-driven models are already performing comparably to traditional numerical weather prediction models. Still, the emphasis of AI at AMS has been largely technical, focusing on the development and verification of AI models rather than the deployment and implications of these models.

The topics for this session will fall at this critical intersection of AI and society. We invite presentations on user-centered development and deployment of AI models, applications of AI to societal data, the communication, visualization, and use of AI-derived information, or other related research.

12:00 PM-1:45 PM: Wednesday, 31 January 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Wednesday, 31 January 2024

11A

LSTM and Other Time Series Machine Learning Methods for Time Series Prediction

Location: 345/346 (The Baltimore Convention Center)

With increasing advancements in time series machine learning research and models, this session will highlight research using time series specific models, such as LSTMs, in a variety of earth science applications. This includes but is not limited to topics in weather, climate, aerosol, and energy.

11B

Leveraging Unsupervised Machine Learning in Environmental Science

Location: 338 (The Baltimore Convention Center)

We invite abstract submissions for projects that have programmed unsupervised machine learning models for use in environmental science research and project pipelines. Examples include but are not limited to pattern recognition and object detection, classification, etc.

3:00 PM-3:40 PM: Wednesday, 31 January 2024

23rd Conference on Artificial Intelligence for Environmental Science ePosters (Wednesday)

Location: Hall E (The Baltimore Convention Center)

3:00 PM-4:30 PM: Wednesday, 31 January 2024

Coffee Break & Formal Poster Viewing [Hall E (BCC) & Hall F (BCC)]

Location: Hall E (The Baltimore Convention Center)

Posters III

Location: Hall E (The Baltimore Convention Center)

4:30 PM-6:00 PM: Wednesday, 31 January 2024

12B

AI for Radars: Observations, Data Processing, Classification, Estimation, and Nowcasting

Location: 338 (The Baltimore Convention Center)

This session is devoted to data sciences and machine learning techniques in weather radar observations and quantitative applications. Topics include but are not limited to weather radar signal and data processing, physical sciences in radar meteorology and hydrology, and radar applications such as precipitation classification, estimation and nowcasting. This session will also feature presentations about combining radar measurements in the context of numerical model assimilation and short-term forecasts, as well as multiscale data fusion and regularization.

J12A

Artificial Intelligence for Actionable Insights and Applications in Climate Science

Location: 345/346 (The Baltimore Convention Center)

Artificial intelligence (AI) applied to the earth sciences has recently been a rapidly expanding field in both academic and industry spaces, due in part to its ability to extract nonlinear relationships from noisy data. The use of AI, particularly machine learning, can lead to identification of predictable signals from purely data-driven methods. Further, explainable AI techniques allow for opening of the AI “black box” to understand the model’s decision-making strategy. AI-driven advancements in climate predictability on subseasonal through multidecadal timescales, today and in a changing climate, allows for increased predictive skill and lead time, which can improve preparation. The use of AI in climate data analysis has cultivated actionable insights for the purpose of both scientific discovery and for managing climate risk.

We invite abstracts that discuss the use of AI for actionable insights, including high resolution climate forecasting and informing of adaptation and mitigation strategies, as well as using AI to isolate at-risk areas exposed to various climate impacts. This session also welcomes AI approaches applied to climate models and observational data that can be used by decision-makers and stakeholders for planning purposes. Equal consideration will be given to reproducible novel AI techniques, explainable and interpretable AI methods for exploring the climate system, sources of predictability on subseasonal-to-multidecadal timescales, and forecasts of opportunity.

6:00 PM-7:30 PM: Wednesday, 31 January 2024

Awards Reception and Ceremony

Location: Ballroom III/ IV (The Baltimore Convention Center)

AMS is pleased to recognize outstanding individuals and organizations of the weather, water, and climate community through its Awards and Honors program. Join us in congratulating the 2024 recipients at a reception in their honor on Wednesday, 31 January at 6:00 p.m., followed by the AMS Awards Ceremony at 6:30 p.m.

Click Here to watch the AMS Awards Ceremony!

7:30 PM-9:30 PM: Wednesday, 31 January 2024

Wednesday Night Live

Location: Key Ballroom Salon 1-8 (Hilton Baltimore Inner Harbor)

Thursday, 1 February 2024

8:30 AM-10:00 AM: Thursday, 1 February 2024

13A

Applications of Artificial Intelligence for Improved Estimation and Prediction of Extreme Precipitation and/or Drought in Weather and Climate I

Location: 345/346 (The Baltimore Convention Center)

This session invites abstracts leveraging AI tools to improve understanding and/or forecasting abilities of extreme rainfall and drought events.

13B

Applications of Machine Learning to Remote Sensing of Aerosol, Cloud, and Precipitation Properties

Location: 338 (The Baltimore Convention Center)

This session invites submissions that have utilized AI methods for remote sensing applications. Additionally, we invite those who have used AI in atmospheric applications involving clouds and aerosols. Abstracts can include model development for retrieval, classification, and/or numerical weather prediction.

J13C

Towards Operationalizing AI/ML Weather Forecast and Decision Support Products I

Location: 327 (The Baltimore Convention Center)

and Aaron Hill, NOAA/OAR Affiliate, Colorado State Univ.

10:00 AM-10:45 AM: Thursday, 1 February 2024

Exhibit Hall Breakfast

Location: Hall F/ Swing (The Baltimore Convention Center)

12:00 PM-1:45 PM: Thursday, 1 February 2024

Lunch Break

Location: The Baltimore Convention Center

1:45 PM-3:00 PM: Thursday, 1 February 2024

15A

Applications of Artificial Intelligence for Improved Estimation and Prediction of Extreme Precipitation and/or Drought in Weather and Climate II

Location: 345/346 (The Baltimore Convention Center)

15B

Explainable Artificial Intelligence (XAI) for Environmental Science II

Location: 338 (The Baltimore Convention Center)

15B

New Concepts in Advanced Modeling of Earth System and Digital Twin Technology

Location: 316 (The Baltimore Convention Center)

3:00 PM-3:40 PM: Thursday, 1 February 2024

23rd Conference on Artificial Intelligence for Environmental Science ePosters (Thursday)

Location: Hall E (The Baltimore Convention Center)

3:00 PM-4:30 PM: Thursday, 1 February 2024

Coffee Break & Formal Poster Viewing

Location: Hall E (The Baltimore Convention Center)

Posters IV

Location: Hall E (The Baltimore Convention Center)

4:30 PM-6:00 PM: Thursday, 1 February 2024

16A

Pure AI and Data-Driven Weather Forecasts III: Operations and Applications with AI Forecast Models

Location: 345/346 (The Baltimore Convention Center)

16B

Artificial Intelligence for Environmental Science III

Location: 338 (The Baltimore Convention Center)

» Expand View

Session 16B 23rd Conference on Artificial Intelligence for Environmental Science

Tuesday, 23 January 2024

1:00 PM-2:00 PM: Tuesday, 23 January 2024

Sunday, 28 January 2024

4:00 PM-5:15 PM: Sunday, 28 January 2024

6:30 PM-8:30 PM: Sunday, 28 January 2024

Monday, 29 January 2024

8:30 AM-10:00 AM: Monday, 29 January 2024

10:00 AM-10:45 AM: Monday, 29 January 2024

10:45 AM-12:00 PM: Monday, 29 January 2024

12:00 PM-1:45 PM: Monday, 29 January 2024

1:45 PM-3:00 PM: Monday, 29 January 2024

3:00 PM-3:40 PM: Monday, 29 January 2024

3:00 PM-4:30 PM: Monday, 29 January 2024

4:30 PM-5:45 PM: Monday, 29 January 2024

4:30 PM-6:00 PM: Monday, 29 January 2024

6:00 PM-7:30 PM: Monday, 29 January 2024

6:30 PM-9:00 PM: Monday, 29 January 2024

7:00 PM-9:00 PM: Monday, 29 January 2024

7:30 PM-10:00 PM: Monday, 29 January 2024

Tuesday, 30 January 2024

8:30 AM-9:45 AM: Tuesday, 30 January 2024

8:30 AM-10:00 AM: Tuesday, 30 January 2024

10:00 AM-10:45 AM: Tuesday, 30 January 2024

12:00 PM-1:45 PM: Tuesday, 30 January 2024

1:45 PM-3:00 PM: Tuesday, 30 January 2024

3:00 PM-4:30 PM: Tuesday, 30 January 2024

4:30 PM-6:00 PM: Tuesday, 30 January 2024

6:00 PM-7:00 PM: Tuesday, 30 January 2024

Wednesday, 31 January 2024

8:30 AM-10:00 AM: Wednesday, 31 January 2024

10:00 AM-10:45 AM: Wednesday, 31 January 2024

10:45 AM-12:00 PM: Wednesday, 31 January 2024

12:00 PM-1:45 PM: Wednesday, 31 January 2024

1:45 PM-3:00 PM: Wednesday, 31 January 2024

3:00 PM-3:40 PM: Wednesday, 31 January 2024

3:00 PM-4:30 PM: Wednesday, 31 January 2024

4:30 PM-6:00 PM: Wednesday, 31 January 2024

6:00 PM-7:30 PM: Wednesday, 31 January 2024

7:30 PM-9:30 PM: Wednesday, 31 January 2024

Thursday, 1 February 2024

8:30 AM-10:00 AM: Thursday, 1 February 2024

10:00 AM-10:45 AM: Thursday, 1 February 2024

12:00 PM-1:45 PM: Thursday, 1 February 2024

1:45 PM-3:00 PM: Thursday, 1 February 2024

3:00 PM-3:40 PM: Thursday, 1 February 2024

3:00 PM-4:30 PM: Thursday, 1 February 2024

4:30 PM-6:00 PM: Thursday, 1 February 2024

Tuesday, 23 January 2024

1:00 PM-2:00 PM: Tuesday, 23 January 2024

Sunday, 28 January 2024

4:00 PM-5:15 PM: Sunday, 28 January 2024

6:30 PM-8:30 PM: Sunday, 28 January 2024

Monday, 29 January 2024

8:30 AM-10:00 AM: Monday, 29 January 2024

10:00 AM-10:45 AM: Monday, 29 January 2024

10:45 AM-12:00 PM: Monday, 29 January 2024

12:00 PM-1:45 PM: Monday, 29 January 2024

1:45 PM-3:00 PM: Monday, 29 January 2024

3:00 PM-3:40 PM: Monday, 29 January 2024

3:00 PM-4:30 PM: Monday, 29 January 2024

4:30 PM-5:45 PM: Monday, 29 January 2024

4:30 PM-6:00 PM: Monday, 29 January 2024

6:00 PM-7:30 PM: Monday, 29 January 2024

6:30 PM-9:00 PM: Monday, 29 January 2024

7:00 PM-9:00 PM: Monday, 29 January 2024

7:30 PM-10:00 PM: Monday, 29 January 2024

Tuesday, 30 January 2024

8:30 AM-9:45 AM: Tuesday, 30 January 2024

8:30 AM-10:00 AM: Tuesday, 30 January 2024

10:00 AM-10:45 AM: Tuesday, 30 January 2024

12:00 PM-1:45 PM: Tuesday, 30 January 2024

1:45 PM-3:00 PM: Tuesday, 30 January 2024

3:00 PM-4:30 PM: Tuesday, 30 January 2024

4:30 PM-6:00 PM: Tuesday, 30 January 2024

6:00 PM-7:00 PM: Tuesday, 30 January 2024

Wednesday, 31 January 2024

8:30 AM-10:00 AM: Wednesday, 31 January 2024

10:00 AM-10:45 AM: Wednesday, 31 January 2024

10:45 AM-12:00 PM: Wednesday, 31 January 2024