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.
Register by May 8, 2024
We are soliciting abstracts for relevant topics and research from all S2S prediction systems
Submit your abstract by April 12, 2024
A CPAESS Managed Event
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Phone303-497-1000commons@ucar.edu
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