CEDAR email: Fall AGU Session SM017: Quantifying Uncertainty in Space Weather Modeling and Forecasting

[Gang Lu]

Dear Colleagues,
Abstract submission is open for the Fall AGU meeting. We'd like to bring to
your attention a session cutting across the disciplines in Space Physics
and Aeronomy. Session SM017: Quantifying Uncertainty in Space Weather
Modeling and Forecasting. If this sounds relevant to you, please consider
submitting an abstract to this session. The session description is below.

The AGU meeting will be held December 10-14 2018 in Washington DC.  Further
details about the meeting can be found at: fallmeeting.agu.org. Abstract
submissions are due before 23:59 Eastern Daylight Time on Wednesday, August
1st.

Regards,

Steve Morley (Los Alamos National Laboratory)

Gang Lu (National Center for Atmospheric Research)

Sophie Murray (Trinity College Dublin)

---------------------------Session
Description-----------------------------------------
SM017: Quantifying Uncertainty in Space Weather Modeling and Forecasting

Dynamic solar outputs including flares, high-speed solar wind, and coronal
mass ejections, drive responses in geospace that can have deleterious
effects on technological systems. Further, upward propagation of
atmospheric waves and tides produces natural variability in Earth's
ionosphere and thermosphere. Many types of models are used to forecast,
nowcast, or hindcast, space weather relevant quantities, but the
uncertainty of these predictions is often not quantified or reported.
Simulations of varying complexity are key to our understanding of the
physics that drives space weather, and any simulation or forecast has
uncertainty which can arise from a number of sources. These sources include
uncertainties in the initial condition, uncertainties in input data, and
approximations made in the construction of the model. The goal of this
session is to showcase new research in ensemble modeling, probabilistic
forecasting, model sensitivity studies, and other approaches to improve
uncertainty quantification in space weather modeling and forecasting.

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