We are exploring the development of a modeling tool that will benefit national missions in nuclear forensics and test ban treaty enforcement by improving radiological plume predictions. In particular, we are developing statistical methods to generate an optimal atmospheric-model ensemble design to capture the key sources of meteorological uncertainty and generate more accurate nuclear fallout predictions.
Gunawardena, N. 2019. "Adaptively Selecting Optimal Points for Quickly Modeling Atmospheric Ensembles." AGU Fall Meeting 2018. Washington, D.C., December 2018. LLNL-ABS-782901.
Lucas, D. 2019. "New Machine Learning Approach for Inverse Modeling of Atmospheric Sources from Discrete Hit or Miss Data." American Geophysical Union Fall Meeting 2018. Washington, D.C., December 2018. LLNL-ABS-782918.
Lucas, D., et al. 2019. "Novel Statistical Approaches to Characterizing Sources and Uncertainty: Bridging the R&D-Operations Gap." 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. LLNL-POST-763726.
——— . 2019. "Probabilistic Predictions and Uncertainty Estimation Using Adaptively Designed Ensembles for Radiological Plume Modeling." LLNL-POST-774026.
Schroeder, K., et al. 2019. "Weather Uncertainty Total Deposition Novelty Detection." Data Science Institute (poster), Lawrence Livermore National Laboratory. LLNL-POST-782204.
Lawrence Livermore National Laboratory • 7000 East Avenue • Livermore, CA 94550
Operated by Lawrence Livermore National Security, LLC, for the Department of Energy's National Nuclear Security Administration.