We are exploring the development of aÂ modeling tool that willÂ benefitÂ national missions in nuclear forensics and test ban treaty enforcement by improving radiological plume predictions. In particular, we areÂ developing statistical methods to generate anÂ optimal atmospheric-model ensemble design to capture the key sources of meteorological uncertaintyÂ and generate more accurate nuclear fallout predictions.
Publications, Presentations, Etc.
Gunawardena, N.Â 2019. "Adaptively Selecting Optimal Points for Quickly Modeling Atmospheric Ensembles."Â AGU Fall Meeting 2018. Washington, D.C., December 2018.Â LLNL-ABS-782901.
Lucas, D. 2019. "New Machine Learning Approach for Inverse Modeling of Atmospheric Sources from Discrete Hit or Miss Data."Â American Geophysical Union Fall Meeting 2018. Washington, D.C., December 2018.Â LLNL-ABS-782918.Â Â Â Â Â Â Â Â Â Â Â Â Â
Lucas, D., et al. 2019. "Novel Statistical Approaches to Characterizing Sources and Uncertainty: Bridging the R&D-Operations Gap."Â LLNL-PRES-767866.
––– . 2019. "Using Machine Learning to Intelligently Select Members of Large Atmospheric Model Ensembles." American Geophysical Union Fall Meeting 2018. Washington, D.C., December 2018.Â LLNL-POST-763726.
––– . 2019. "Probabilistic Predictions and Uncertainty Estimation Using Adaptively Designed Ensembles for Radiological Plume Modeling." LLNL-POST-774026.
Schroeder, K.,Â et al.Â 2019. "Weather Uncertainty Total Deposition Novelty Detection." Data Science Institute (poster), Lawrence Livermore National Laboratory.Â LLNL-POST-782204.