106th AMS Annual Meeting
January 25-29, 2026
The Earth Prediction Innovation Center (EPIC) is preparing to take part in the 106th American Meteorological Society (AMS) Annual Meeting, scheduled for January 25–29, 2026, in Houston, Texas, and online.
The AMS Annual Meeting brings together researchers, government agencies, and private industry to exchange knowledge, showcase advancements, and tackle the challenges of integrating cutting-edge research into real-world societal decision-making. This premier event serves as a hub for collaboration across the weather, water, and climate enterprise.
A central focus of this year’s meeting will be the training and development of the next-generation workforce, emphasizing how education, mentorship, and applied research prepare early-career professionals to lead the enterprise forward.
The 2026 theme, “Fast and Slow Thinking: The Human Factor in a Rapidly Changing World,” highlights the critical role of human judgment, decision-making, and adaptability in the face of rapidly evolving environmental and technological challenges.
Register for AMS26:
Advancing Weather Prediction with AI: EPIC Short Course at AMS 2026
As part of its contributions, EPIC will host a Short Course titled Artificial Intelligence in Numerical Weather Modeling scheduled for January 25, 2026, at 8:30 AM – 12:00 PM Central Time (Hybrid) at the 106th AMS Annual Meeting at George R. Brown Convention Center. This hands-on workshop will give participants the opportunity to explore community-developed AI tools that enhance forecasting accuracy, efficiency, and scientific discovery. The course will provide practical training designed for researchers, students, and practitioners looking to integrate AI into weather prediction workflows.
EPIC’s participation will highlight its commitment to advancing community-based modeling, streamlining the Research-to-Operations (R2O) pipeline, and expand accessibility of the Unified Forecast System (UFS). Attendees can expect presentations, live demonstrations, and interactive training sessions that reflect EPIC’s mission to accelerate innovation and strengthen collaboration across sectors.
Register for EPIC short course:
Check back often for updates as more information about EPIC’s planned sessions and activities at AMS 2026 becomes available. We are providing this early communication ahead of full details.
Abstracts
Sergey Frolov (PSL, AI4NWP Team)
Presentation Date: Monday, January 26, 2026; 8:30 AM – 8:45 AM CST
Location: 330A (George R. Brown Convention Center)
This presentation introduces project EAGLE (Experimental AI Global and Limited-area Ensemble forecast system)—NOAA’s environment for demonstrating, testing, and transitioning AI weather forecast models to operations. The current version of EAGLE produces daily global deterministic and ensemble forecasts using GraphCast from Google DeepMind. It is fine-tuned on NOAA’s Global Data Assimilation System (GDAS) data as inputs and training targets.
Once fully implemented, EAGLE will establish an open system for testing and verifying AI-based global and limited-area ensemble forecasts using NOAA’s trusted operational forecast metrics. Project EAGLE will provide a research-to-demonstration pipeline that identifies promising AI forecast innovations across the community, facilitates their inclusion in a near-real-time demonstration environment, and expedites their integration into future operational implementations. This near-real-time environment will enable NOAA to experiment and utilize the best community practices for deploying advanced AI systems and disseminating their outputs to users at scale. We expect that future NOAA AI models will leverage common AI model infrastructure based on the Anemoi open-source framework from the European Centre for Medium-Range Weather Forecasts (ECMWF) and several of its Member States. This presentation will review the state of project EAGLE implementation.
Wei Huang
Presentation Date: Monday, January 26, 2026; 1:45 PM – 2:00 PM CST
Location: 340A (George R. Brown Convention Center)
Global-Workflow (GW) is a software package that supports the research and operations of the National Oceanic and Atmospheric Administration’s (NOAA) Global Forecast System (GFS), Global Ensemble Forecasting System (GEFS), Seasonal Forecast System (SFS), and Global Data Assimilation System (GDAS). With GW, NOAA researchers can use the above systems for their research, and NOAA’s Environmental Modeling Center (EMC) uses the same GW for its operational work, providing weather forecast data to the National Weather Service (NWS). The Earth Prediction Innovation Center (EPIC) provides a software package, spack-stack, that supports GW and enables GW to run on Cloud Service Providers (CSPs) such as AWS, Azure, and GCP, provides user support, and more. When updates to compilers and spack-stack software occur, they must be applied to the GW and to every platform NOAA supports. Such updates are tedious, time-consuming, and repetitive across platforms, compilers, and spack-stack updates. By using Singularity containers, users can package the Operating System (OS), compiler, and spack-stack into a Singularity Image File (SIF), then use that SIF on all platforms. EPIC has created a container with Ubuntu, Intel compiler, and spack-stack, so that users can compile global-workflow and run GFS, SFS, and GEFS on AWS and on NOAA on-premises computers like Gaea-C6 and Ursa.
We have been able to run C48_ATM, C48_S2SW, C48_S2SWA_gefs cases on AWS, Gaea-C6, and Ursa, and run the C768_S2SW case on Gaea-C6 and Ursa. Especially on Ursa, we demonstrated that with a Singularity/Container, we only need shell, Slurm (job scheduler), and Rocoto (GW job management tool) to run GW, with OS, compiler, and required software provided by SIF.
In the future, EPIC will enable GDAS to compile and run in a container, provide tutorials to NOAA and academic users, and enable GW to run on a broader range of platforms.
Zach Shrader
Presentation Date: Tuesday, January 27, 2026; 8:30 AM – 8:45 AM CST
Location: 352A (George R. Brown Convention Center)
The Earth Prediction Innovation Center (EPIC) is dedicated to accelerating contributions to the Unified Forecast System (UFS) by engaging the community and providing community members with the necessary tools and knowledge to contribute innovations to our Nation’s forecasting and modeling systems. The EPIC Community Engagement (ECE) team supports EPIC’s innovation through several initiatives, including community training events and the annual Unifying Innovations in Forecasting Capabilities Workshop (UIFCW). It also publishes and publicizes dynamic content on social media, the EPIC Community Portal (ECP), GitHub, and other platforms to meet the community’s evolving needs and to remove barriers to innovation. The User Support (US) team complements ECE’s efforts by updating UFS application documentation, compiling technical FAQs, and monitoring support requests. The US team provides ECE with immediate feedback on community engagement efforts, which allows ECE to adjust content and outreach strategies based on community needs and requests. This collaboration results in a responsive support environment that rapidly evolves to meet the community’s needs, ensuring that users and developers have the support they require to innovate within our national forecasting systems.
Jong Kim
Presentation Date: Tuesday, January 27, 2026; 1:45 PM – 2:00 PM CST
Location: 362C (George R. Brown Convention Center)
A key aspect of the Research-to-Operations (R2O) transition of the Unified Forecast System (UFS) is ensuring the consolidation of source code bases for both research and operations. The UFS structures continue to build on a modular architecture to add componentization approaches and Hierarchical System Development (HSD). This approach facilitates organized development and helps ensure consistency in code repository management across subcomponents and applications in a collaborative environment: data processing, workflow management, code optimization, model physics and dynamics updates, and data assimilation algorithms. This presentation will focus on progress and updates on the UFS Weather Model and Applications Workflow Systems. As a comprehensive R2O collaboration is underway with the Consortium for Advanced Data assimilation Research and Education (CADRE), we will particularly highlight a community-level effort on expanding analysis configuration of the Land Data Assimilation (Land DA) system and workflow test prototypes of the Short-Range Weather Application (SRW App).
Wei Huang
Presentation Date: Tuesday, January 27, 2026; 2:15 PM – 2:30 PM CST
Location: 352A (George R. Brown Convention Center)
Global-Workflow (GW) is a software package designed to support the research and operational activities of the National Oceanic and Atmospheric Administration’s (NOAA) Global Forecast System (GFS), Global Ensemble Forecasting System (GEFS), Seasonal Forecast System (SFS), and Global Data Assimilation System (GDAS). GW enables NOAA researchers to utilize these systems for their research endeavors, and NOAA’s Environmental Modeling Center (EMC) employs the same GW for operational purposes, providing weather forecast data to the National Weather Service (NWS).
The Earth Prediction Innovation Center (EPIC) provides essential software package support for GW, known as spack-stack. EPIC is also involved in numerous model developments, facilitates GW’s execution on Cloud Service Providers (CSPs) such as AWS, Azure, and GCP, and offers user support, among other contributions. Following the successful implementation of GW on AWS, EPIC has collaborated closely with the SFS developers and interested researchers to leverage GW on AWS for running SFS.
This presentation will detail the setup and compilation procedures for GW SFS on AWS, present benchmark and cost analysis results for the C96mx100 and C192mx025 cases, and demonstrate its execution on various compute node (chip) types. EPIC and the SFS team have conducted two training sessions to assist users in running SFS on AWS and will continue to provide support while exploring more time and cost-effective methods for SFS on AWS.
Kris Booker & Anna Kimball
Presentation Date: Wednesday, January 28, 2026; 9:30 AM – 9:45 AM CST
Location: 352A (George R. Brown Convention Center)
The NOAA Earth Prediction Innovation Center (EPIC) program has enabled an accelerated pace of numerical weather prediction innovation through its collaboration with government, academic, and enterprise sectors. Utilizing a common modeling framework, the UFS (Unified Forecast System), has enabled once-siloed atmospheric modeling groups to collaborate effectively. However, the greater challenge has come from constraints that kept organizations from accessing high-performance computing platforms capable of running new modeling innovations at scale. EPIC has solved this dilemma by providing UFS community contributors with public access to detailed insights from these high-performance computing (HPC) platforms. Every contribution considered for integration is extensively tested through a CI/CD pipeline on various NOAA research and development high-performance computing systems (RDHPCS) platforms, which have performance capabilities similar to those of the Weather and Climate Operational Supercomputing System (WCOSS-2). Systematically capturing data such as walltime and core hours for each model run provides contributors with detailed insights into computational efficiency and resource usage. This enables the community to track the evolution of model updates over time, offering quick insight into how performance and efficiency improve successively. The design supports future integration of scientific forecast skill, allowing users to correlate computational costs with forecast accuracy. The data collected is processed and displayed to contributors through the EPIC Community Portal, creating more transparency between EPIC and the broader community. This openness empowers contributors, researchers, and developers to identify trends, compare model versions, and contribute to continuous model refinement. Ultimately, strengthening the feedback loop between operational teams and the user community accelerates improvements in forecasting quality and operational efficiency.
Mariah Pope
Presentation Date: Wednesday, January 28, 2026; 11:00 AM – 11:15 AM CST
Location: 352A (George R. Brown Convention Center)
The proliferation of data-driven weather prediction has produced several models that are competitive with the skill of traditional numerical weather prediction (NWP) at a fraction of the cost. However, re-training of the published models with custom (NOAA) data can be a cumbersome process due to hardware setup, staging data, and efficiently scaling the model. This burden creates a steep learning curve for users that is a barrier to quick scientific iteration and progress. We present a framework of community tools for developing machine learning weather models using ufs2arco from the NOAA Physical Sciences Laboratory (PSL) and Anemoi from the European Centre for Medium-Range Weather Forecasting (ECMWF) to guide users through training their own models using a combination of NOAA and ECMWF datasets. First, the ufs2arco package enables users to quickly create large training datasets from existing data in the cloud, and is specifically designed to improve access to NOAA data. Anemoi then provides an easily customizable framework for developing and testing graph-based machine learning weather prediction models. Together, ufs2arco and Anemoi establish a comprehensive, data-driven framework that we are currently able to run using on-prem or cloud-based resources. We guide users through environment setup, data preparation, model training, and inference execution. Finally, we demonstrate the efficiency and flexibility of this framework and provide benchmark metrics on computational speed and cost. Overall, this work makes NOAA data easily accessible for machine learning model development, and makes the ECMWF Anemoi framework more readily deployable.
D. Alex Burrows
Presentation Date: Wednesday, January 28, 2026; 11:15 AM – 11:30 AM CST
Location: 352A (George R. Brown Convention Center)
With the recent ubiquitous development of Artificial Intelligence/Machine Learning (AI/ML) algorithms to complement Numerical Weather Prediction (NWP), Earth Prediction Innovation Center (EPIC), along with other NOAA agencies such as the Environmental Modeling Center (EMC), the Physical Sciences Laboratory (PSL), and the Global Systems Laboratory (GSL), has been experimenting with global AI/ML forecast systems. To support this effort within NOAA, EPIC is considering multiple frameworks for forecast verification, including EMC’s Verification System (EVS) and GSL’s weather verification system (wxvx). EVS and wxvx are both wrapper workflows that support the Model Evaluation Toolkit (MET) and METplus.
While EVS is essentially hardcoded on the National Centers for Environmental Prediction’s (NCEP) Weather and Climate Operational Supercomputing System (WCOSS) supercomputer, which requires nearly a dozen spack-type modules installed, and runs 31-day and 90-day verification, wxvx was designed to be flexible, handling output from physically based and AI/ML-based forecast systems through the use of YAML configuration files. Additionally, since it provides pre-built MET/METplus executables for Linux systems (via its sibling met2go package), it eliminates the need for dozens of module loads, making it portable to NOAA, non-NOAA, and cloud provider machines. Additionally, through YAML configurations, the output statistics can be adjusted to accommodate the number of forecast cycles and forecast lengths.
Specifically, EPIC will use wxvx to verify model output from the ECMWF’s Anemoi framework. This talk will focus on verification with wxvx, describing the current state and future plans. The current capability of wxvx includes global and regional verification for grid-to-grid comparison of forecasts. In active development is the extension to grid-to-obs verification. In the future and in support of EPIC’s community framework, various verification statistics will be hosted at EPIC’s web portal (epic.noaa.gov) along with Anemoi forecast output.
Maoyi Huang (NOAA, OAR, WPO)
Jan Ising (NOAA, OAR, WPO)
John E. Ten Hoeve (NOAA, OAR, WPO)
Presentation Date: Wednesday, January 28, 2026; 2:30 PM – 2:45 PM
Location: 352A (George R. Brown Convention Center)
There are still gaps in our capabilities to effectively use data from our global observing systems, and not many people have the expertise needed to fill these gaps, particularly to advance weather forecasts with Data Assimilation. Data assimilation is the backbone of numerical weather prediction (such as NOAA’s operational weather forecasts like those of the Unified Forecast System) that supports the economy, informs critical decisions, and aids in unleashing American energy. Data Assimilation is no longer a luxury but an operational necessity for preparing communities for landfalling hurricanes and severe storms, and other phenomena, to safeguard agriculture, aviation, and military operations, and more. Data assimilation is what makes numerical weather prediction truly work since the “initial value problem” indicates that the problem simply cannot be solved without knowing what the weather looks like today, and therefore cannot be done without Data Assimilation.
Over the last year, Data Assimilation within and around NOAA has been susceptible to many challenges in a changing world. Despite these circumstances, incredible progress has been made with 1.) building the Data Assimilation workforce with knowledge and information exchanges, training opportunities, and career developments, 2.) advancing Data Assimilation science and innovation with respect to operations to research and research to operations activities, 3.) building a broad network of Data Assimilation experts, and 4.) connecting University teams to work with NOAA. While many of these efforts have been undertaken by the Consortium for Advanced Data Assimilation Research and Education, this presentation plans to cover, on a high-level, additional aspects on how Data Assimilation is being maintained to advance weather research, operations, and usefulness through, for example, international activities such as the Transatlantic Data Science Academy and human factors such as future workforce educational material development and sustainability of Data Assimilation. Innovative connections with ongoing technological advances such as artificial intelligence applications are also planned to be briefly covered.
Data Assimilation development efforts stem from policies, regulations, and executive actions such as the Science Advisory Board PWR to Accelerate the earth system modeling approach, the LEGENDS Act to maintain a program to improve the forecasting for extreme weather and impacts, and the Weather Act Reauthorization Act of 2023 (U.S. Weather Research Program) and the Inflation Reduction Act of 2022 for supporting and utilizing technological upgrades including those that improve precipitation predictions (like Data Assimilation research).
Keven Blackman & D. Alex Burrows
Presentation Date: Wednesday, January 28, 2026; 4:45 PM – 5:00 PM CST
Location: 352A (George R. Brown Convention Center)
The Earth Prediction Innovation Center (EPIC) is dedicated to accelerating advancements in the Unified Forecast System (UFS) by actively engaging with the community and equipping its members with the essential tools and knowledge necessary to drive innovations in our nation’s forecasting and modeling systems. The EPIC Community Engagement (ECE) team fosters innovation through various initiatives, including community training events and the annual Unifying Innovations in Forecasting Capabilities Workshop (UIFCW). Additionally, the ECE team disseminates dynamic content via social media, the EPIC Community Portal (ECP), GitHub, and other platforms to meet the community’s evolving needs and eliminate barriers to innovation.
The User Support (US) team complements ECE’s efforts by continuously updating UFS application documentation, compiling technical FAQs, and monitoring support requests. This team provides immediate feedback to ECE on community engagement efforts, enabling ECE to refine its content and outreach strategies based on community needs and requests. This collaboration results in a highly responsive support environment that evolves rapidly to meet the community’s needs, ensuring that users and developers have the necessary support to innovate within our national forecasting systems.
Furthermore, EPIC aims to create an inclusive and collaborative atmosphere where diverse perspectives are valued, and innovative ideas can flourish. By leveraging the strengths and feedback of the community, EPIC is committed to driving continuous improvement and excellence in weather prediction, ultimately enhancing the accuracy and reliability of forecasts that are vital to public safety and economic stability.
Maoyi Huang (NOAA, OAR, WPO)
Jan Ising (NOAA, OAR, WPO)
John E. Ten Hoeve (NOAA, OAR, WPO)
Presentation Date: Wednesday, January 28, 2026; 5:00 PM – 5:15 PM
Location: 352A (George R. Brown Convention Center)
The National Oceanic and Atmospheric Administration (NOAA) established the Earth Prediction Innovation Center (EPIC) with a mission to be the catalyst for community research and modeling focused on informing and accelerating advances in our nation’s operational numerical weather prediction (NWP) forecast modeling systems via the Unified Forecast System (UFS). UFS is a community-based, coupled, comprehensive Earth modeling system. The UFS numerical applications span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. It is designed to support the Weather Enterprise and to be the source system for NOAA‘s operational NWP applications.
Following the EPIC Strategic Plan 2020-2025, EPIC invested in seven areas, namely, software engineering, software infrastructure, user support, cloud-based high-performance computing (HPC), scientific innovation, management and planning, and community engagement to advance EPIC’s mission. In this talk, we will demonstrate the EPIC program’s accomplishments toward the following strategic outcomes:
- Nurture a representative and unifying modeling community;
- Develop a public accessible end-to-end testing and development environment for the Unified Forecast System;
- Bring innovations to improve UFS forecast skill and computational performance.
Built upon those accomplishments, EPIC will continue transforming the landscape of Earth system prediction in the next few years by (1) championing collaborative partnerships across government, academia, and industry; (2) driving innovative infrastructure, integrating groundbreaking technologies like Artificial Intelligence into Numerical Weather Prediction which improves forecasts that help save lives and protect property and the enhancement of the national economy, and (3) training the next generation of atmospheric scientists, and (4) fostering a skilled workforce prepared to tackle complex environmental challenges.
Community members are invited to participate in order to understand how they can leverage EPIC in learning and performing research to advance the UFS, and to provide feedback on how the program can help better support the weather enterprise.
John E. Ten Hoeve (NOAA, OAR, WPO)
Presentation Date: Wednesday, January 28, 2026; 5:15 PM – 5:30 PM
Location: 352A (George R. Brown Convention Center)
Students are a necessary part of a weather modeling community, yet their limited resources and relative lack of expertise means they require special consideration to be able to be involved community members. The Earth Prediction Innovation Center has been a champion of the student experience from its inception, improving code documentation, developing tutorials, and hosting hackathons and training sessions. This presentation reflects on efforts to engage students over the 5-year history of the EPIC program, including through the UFS Student Ambassador program. This past summer, EPIC hosted a UFS Student Ambassador through the NOAA Lapenta Internship Program that revamped the previous UFS graduate student test, now the UFS usability test, while building on the efforts of previous UFS Student Ambassadors.
This presentation will discuss lessons learned from both EPIC-driven and student-driven engagement experiences to build the next generation UFS modeling workforce, with a focus on the work of EPIC’s recent student intern. The presentation will also introduce the UFS useability test, which is a collection of short range weather (SRW) application test cases of the August 10, 2020 derecho allowing users to modify physics parameterizations, initialization times, and domain size. The tutorial for NOAA Tier 1 systems is completed (including for Mississippi State machines Orion and Hercules, which allow access for non-NOAA users), with an additional tutorial using the containerized version of the SRW in development. With the release of this new tutorial, we aspire to show higher first time success rates and satisfaction scores that are on par with other community-based modeling systems, such as the Weather Research and Forecasting Model. The presentation will also include the results of a social media campaign and initial feedback from a survey connected to the tutorial, both of which were led by our Lapenta intern this past summer.
Finally, the presentation will look towards the future to discuss potential opportunities to better engage and accommodate students of all ages. As NCAR’s MPAS dynamical core becomes integrated into the UFS in the coming years, it presents possibly the largest opportunity to also integrate the two largest modeling communities in the United States. This would allow students to work on both research and operational challenges while not having to necessarily learn two distinct modeling systems.
Sunday, January 25, 2026
Event: Annual Meeting Welcome & Annual Awards Ceremony/Business Meeting
- Grand Ballroom B (GRB)
Monday, January 26, 2026
- Chair: Sylwester Arabas, Faculty of Physics and Applied Computer Science, AGH University of Krakow, Krakow, Krakow, PolandCochairs: Nicole Riemer, Department of Atmospheric Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL; Lin Lin, Livermore, CA and Adele Igel
- 372C (GRB)
Break: Daily Weather Briefings
- 360D (GRB)
- Cochairs: Xuejin Zhang, Hurricane Research Division, AOML, Miami, FL; James L. Kinter, Department of Atmospheric, Oceanic & Earth Sciences, COLA, Fairfax, VA and Jose-Henrique G.M. Alves, OAR, Silver Spring, MD
- 362C (GRB)
- Cochairs: Marc E. Cotnoir, GDIT, Fairfax, VA and Krishna V. Kumar, College Park, MD
- 340A (GRB)
Tuesday, January 27, 2026
Session J5: Multi-University Consortium for Advanced Data Assimilation Research and Education
Chair: Zhaoxia Pu, Professor, Department of Atmospheric Science, Univ. of Utah, Salt Lake City, UTCoChair: Xingchao Chen, The Department of Meteorology and Atmospheric Science, The Pennsylvania State Univ., University Park, PA- 320A (GRB)
Session 5: Earth System Model Governance and Community Building
Chair: Alison Gregory, UCAR, Boulder, COOrganizer: Hendrik L. Tolman, Dr. Ir., Room 15114, NOAA, Silver Spring, MDCochairs: Alekya Srinivasan, NOAA, Spring City, PA and Laura Dailey, OAR, Marlton, NJ- 352A (GRB)
Break: Daily Weather Briefings
- 360D (GRB)
Session 7: Community Infrastructure and Architecture for Earth System Models
Chair: Christina R. Holt, NOAA GSL, CIRES, Dillon, COOrganizer: Hendrik L. Tolman, Dr. Ir., Room 15114, NOAA, Silver Spring, MDCoChair: Dan Rosen, Climate & Global Dynamics Lab, NCAR, Boulder, CO- 352A (GRB)
Poster Session: Fifth Symposium on Community Modeling and Innovation Tuesday Posters
Organizer: Hendrik L. Tolman, Dr. Ir., Room 15114, NOAA, Silver Spring, MD- Hall B3 (GRB)
Session 8 Verification and Validation of Earth System Models for Weather and Climate Timescales
Chair: Benjamin A. Cash, AOES/COLA, George Mason Univ., Fairfax, VAOrganizer: Jose-Henrique G.M. Alves, WPO, OAR, Kensington, MDCoChair: Jan F. Dutton, Prescient Weather Ltd, State College, PA- 352A (GRB)
Wednesday, January 28, 2026
Session 9: Community Earth System Model Innovation and Development
Chair: Samantha J Kramer, Spheros Environmental, Denver, COOrganizer: Hendrik L. Tolman, Dr. Ir., Room 15114, NOAA, Silver Spring, MDCoChair: Kelsey Malloy, Department of Geography and Spatial Sciences, University of Delaware, Newark, DE- 352A (GRB)
- Chair: Hendrik L. Tolman, Dr. Ir., Room 15114, NOAA, Silver Spring, MDOrganizer: Jose-Henrique G.M. Alves, WPO, OAR, Kensington, MDCoChair: Stylianos Flampouris, Silurian AI, Kirkland, WA
- 352A (GRB)
Break: Daily Weather Briefings
- 360D (GRB)
Joint Session J11: Progress Updates and Community Innovations in Data Assimilation for Earth Systems
Chair: Jan Ising, WPO/EPIC, NOAA, Broomfield, COCoChair: Man-Yau (Joseph) Chan, Singapore, Singapore- 352A (GRB)
Poster Session: Fifth Symposium on Community Modeling and Innovation Wednesday Posters
Organizer: Hendrik L. Tolman, Dr. Ir., Room 15114, NOAA, Silver Spring, MD- Hall B3 (GRB)
- Chair: Tracy Fanara, Inspector Planet LLC, tampa, FLOrganizer: Jose-Henrique G.M. Alves, WPO, OAR, Kensington, MDCoChair: Nysheema Lett, Axiom Consultants, Rockville, MD
- 352A (GRB)
Thursday, January 29, 2026
Break: Daily Weather Briefings
- 360D (GRB)
Break: Formal Poster Viewing and Coffee Break
- Hall B3 (GRB)
