Session 9 Ensemble Modeling and Data Assimilation Improving Forecast Accuracy

Wednesday, 15 January 2020: 8:30 AM-10:00 AM
205A (Boston Convention and Exhibition Center)
Host: 17th Conference on Space Weather
Chairs:
Robert Robinson, Catholic University of America, Greenbelt, MD and Barbara J. Thompson, NASA, Greenbelt, MD

Space weather forecasters and researchers have made progress in using adaptive approaches to improve forecast accuracy. These approaches include ensemble modeling, where a series of model inputs are generated representing the range of possible values, and data assimilation, where recent measurements are used to update the simulation. Ensemble forecasts combine many model predictions to create an ensemble that is more accurate than separate models, and allow the modeler to characterize how the uncertainty in model inputs result in output uncertainty. Another way to handle model output uncertainty is through data assimilation, where a physical model's accuracy can be improved by including, or assimilating additional information and data. These approaches, and other probabilistic methods, pave the wave for more accurate future forecasts, and make optimal use of all available information.

Papers:
8:30 AM
9.1
"Ensemble Modeling" of the September 2017 CME Event Observed at Earth, STEREO-A, and Mars (Invited Presentation)
Christina O. Lee, Space Sciences Laboratory, Univ. of California, Berkeley, Berkeley, CA; and J. G. Luhmann and M. L. Mays
8:45 AM
9.2
Identifying Critical Input Parameters for Accurate Drag-Based Coronal Mass Ejection Arrival Time Predictions
Christina Kay, Catholic Univ. of America, Greenbelt, MD; GSFC, Greenbelt, MD; and L. Mays and C. Verbeke
9:00 AM
9.3
Physics-Informed Machine Learning for Data Assimilation in High-Dimensional Space Weather Models
Piyush Mukesh Mehta, West Virginia Univ., Morgantown, WV; and R. J. Licata III
9:30 AM
9.5
Predicting Space Weather Impacts on the North American Power Grid Using Perturbed-Input Ensemble Modeling
Steven Morley, LANL, Los Alamos, NM; and D. Welling, M. Engel, M. Rivera, and M. G. Henderson
9:45 AM
9.6
Bayesian Parameter Estimation in Geospace Modeling (Invited Presentation)
Enrico Camporeale, NOAA, Boulder, CO; CIRES, Boulder, CO; and M. D. Cash and H. J. Singer

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