ISWAT Team O1-02 Quantification of Uncertainties in Space Weather Forecasts 

(Team Leaders: N. Pogorelov, C.N Arge, E. Camporeale, T. Singh, and M. Zhang)

Successful space weather (SWx) forecasts require a synergy of data analysis, numerical simulations, and machine learning (ML). A traditional way to address the challenges in SWx physics is to derive the boundary conditions for coronal models using data assimilation in the photosphere and surface flux transport models, run coronal models to heliocentric distances outside of the Alfvénic surface, and use the obtained solutions in corresponding heliospheric models. A plethora of ensemble simulations has demonstrated that uncertainties are not acceptable, being about 12 hours even for CME arrival time,on the average.

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Our goals are to use machine learning (ML) techniques (1) to automatize the quantification of uncertainties in ensemble modeling; (2) to improve the forecasts; and (3) once a successful forecast is made, to backtrack the reasons of errors introduced by multiple uncertainties and determine the sensitivity of forecasts to different sources of uncertainties.

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ML is also of importance for modeling solar energetic particles (SEPs), where uncertainties exist also in the process of ion injection into the acceleration process.

Session 1 (Thursday AM1) Topics:

1) Challenges in uncertainty quantification: Introduction (N. Pogorelov); 

2) Uncertainties in CME predictions and their mitigation with ML (Talwinder Singh); 

3) ML for the ambient solar wind (Syed Raza); 

4) Uncertainties in SEP physics that affect SWx forecasts (Ming Zhang).

Session 2 (Thursday PM1) Topics:

1)The nature and importance of uncertainty quantification (Nick Arge); 

2) Uncertainties related to Photospheric magnetograms (Ron Caplan/Sam Schonfeld) 

3) Uncertainties in ensemble modeling of solar wind flow as seen by multiple spacecraft and locations (Dinesha Hegde). 

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All introductory talks followed by discussions.