CEDAR email: Call for abstracts: Data-based modeling and uncertainty quantification for space weather (SM004)

[Bharat Kunduri]

Dear Colleagues,

Please consider submitting an abstract to our session "Data-based modeling
and uncertainty quantification for space weather" (SM004) at the upcoming
AGU fall 2020 meeting.

Link: https://agu.confex.com/agu/fm20/prelim.cgi/Session/102291

Session Description:
Efforts to understand and model the dynamics of space weather have seen
significant advancements over the last decade. These include statistics
derived using large scale datasets as well as those based on first
principles. In particular, the growth of data from both ground and space
based instruments has fuelled the development and application of models
based on artificial intelligence and improved first-principles models
through data assimilation. Appropriate steps are required to quantify
uncertainties for data-derived benchmarks, statistical analyses, and
physics-based models. Recent advancements in the field of data science with
the availability of better infrastructure and open source libraries can
help address some of these challenges. The goal of this session is to
showcase new research in the application of statistical methods, data
science and engineering, data mining, and machine learning to space
science. We also welcome studies discussing probabilistic forecasting,
model sensitivity, ensemble modeling, and statistical best practices.

We look forward to your participation in this session.

Regards

Bharat Kunduri
Mervyn Freeman
Steven Morley
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mailman.ucar.edu/pipermail/cedar_email/attachments/20200713/ea247448/attachment-0001.html>