S2S Community Workshop

Toward Minimizing Early Model Biases and Errors in S2S Predictions

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Jun. 5 to Jun. 7, 2024

8:00 am – 5:00 pm MDT

Boulder, CO
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Register by May 13, 2024 

Workshop Objective

The workshop will increase collaborations across NOAA, other agencies, and the community to address Subseasonal-To-Seasonal (S2S) time scale prediction capability with a particular focus on the long-standing problem of early model errors and biases. These errors and biases are a common problem for all S2S prediction systems. Specific topics to be addressed include, but are not limited to, tools for diagnosing errors (including the possibility of Artificial Intelligence/Machine Learning (AI/ML), additional observations needed to address the problem either for process understanding, model validation or for initialization, the impact and limitations of model resolution, and what metrics are particularly important to address modeling and stakeholder needs, current and desired R2O and O2R infrastructure for NOAA-external collaborations.

We are soliciting abstracts for relevant topics and research from all S2S prediction systems (April 12 submission deadline).


Desired outcomes of the workshop include:

  • Increasing collaborations across NOAA, other agencies, and the community
  • Determination of the areas where the community is willing/able to engage and collaborate
  • Determination of what the community needs in order to maximize their ability to help
  • Identification of potential technological solutions to gaps (i.e., post processing, analog, statistical/dynamical methods, AI, etc.)
  • Identification of required additional research to improve understanding and realization of sources and limits of temperature, precipitation, and water resources predictability on S2S to decadal timescales



Agenda PDF (updated June 3, 2024)


Abstract Submission

Abstract submission deadline: April 19


Workshop Contacts

Questions about the workshop?

Contact Mark Olsen - NOAA, Contact Christine Bassett - NOAA, or Contact DK Kang 


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