Topic: Data Assimilation in the New Era of “Data-driven” Earth System Analysis and Prediction (Invited Presentations Only) (105th AMS Annual Meeting)

Monday, 13 January 2025: 10:45 AM-12:00 PM

222 (New Orleans Ernest N. Morial Convention Center)

Host: 29th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS)

Chair:

Xuguang Wang, University of Oklahoma, School of Meteorology, Norman, OK

Organizers:

Steven J. Greybush, Pennsylvania State University, Department of Meteorology, University Park, PA; Kayo Ide, NESDIS, STAR, College Park, MD; Aaron T. Johnson, University of Oklahoma, School of Meteorology, Norman, OK and Cheng-Hsuan Lu, SUNY, ASRC, Albany, NY

CoChair:

Peter Jan van Leeuwen, Colorado State University, Department of Atmospheric Science, Fort Collins, CO

Panelists:

Sen Chiao; Xingchao Chen, The Pennsylvania State University, Dept of Meteorology, State College, PA; Jonathan Poterjoy, NCAR, Boulder, CO; Zhaoxia Pu, University of Utah, Department of Atmospheric Sciences, Salt Lake City, UT; Peter Jan van Leeuwen, Colorado State University, Department of Atmospheric Science, Fort Collins, CO; Massimo Bonavita, ECMWF, Research, Reading; Daryl T. Kleist, NOAA NWS NCEP EMC, College Park, MD; Sergey Frolov, CIRES, Boulder, CO; Sarah Dance, University of Reading, Reading, Berkshire and Maoyi Huang

Speakers:

Xuguang Wang, University of Oklahoma, School of Meteorology, Norman, OK; Massimo Bonavita, ECMWF, Research, Reading; Sarah Dance, University of Reading, Reading, Berkshire; Daryl T. Kleist, NOAA NWS NCEP EMC, College Park, MD and Sergey Frolov, CIRES, Boulder, CO

Data assimilation (DA) plays a critical role in Earth system prediction, and faces significant challenges associated with the high-resolution, multiscale, coupled earth system modeling and a large amount of diverse and complex observations. The recent promises of ML/AI demonstrated in aspects of Earth system prediction also suggest new opportunities for data assimilation. However, serious gaps in DA workforce and the lack of sustained innovative DA research support inhibit addressing these challenges as well as reacting to the new opportunities. A new Multi-University Consortium for Advanced Data Assimilation Research and Education (CADRE) was recently established through funding from NOAA. CADRE will perform innovative DA research towards improving forecasts from short range to S2S scales and will develop the next generation of DA workforce. CADRE will achieve these goals through its integrated research, education, outreach and R2O2R program and close collaboration with NOAA, JCSDA, UK Met MOAP (e.g. University of Reading), TDSA, and other weather enterprise partners. In this town hall, we will introduce the new DA consortium, CADRE, and discuss DA challenges, priorities and opportunities. The following questions are planned to be discussed: How should the DA and ML/AI fields interact and collaborate for their developments? How will the “data driven” prediction model influence DA research? What is the role and opportunity of DA in the Earth system digital twin? What skills should students develop to prepare to work on DA for NOAA and for future opportunities? Time will be given for open questions and discussions between the audience and panelist.

Papers:

10:45 AM

Introductory Remarks

10:50 AM

2.1

Data Assimilation and Machine Learning: Evolution or revolution?

Massimo Bonavita, ECMWF, Reading, United Kingdom

11:05 AM

2.2

Development of skills needed to prepare for work in data assimilation for earth system analysis and prediction

Daryl T. Kleist, NOAA, College Park, MD

11:20 AM