Kroiz, Gerson C.Majumder, ReetamGobbert, Matthias K.Neerchal, Nagaraj K.Markert, KelMehta, Amita2020-07-282020-07-282020-06-30Gerson C. Kroiz et al., Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin, Proceedings in Applied Mathematics and Mechanics, http://hpcf-files.umbc.edu/research/papers/S15_Majumder_v1.pdfhttp://hdl.handle.net/11603/19259UMBC High Performance Computing FacilityA daily precipitation generator based on a hidden Markov model with Gaussian copulas (HMM-GC) is constructed using remote sensing data from GPM-IMERG for the Potomac river basin on the East Coast of the USA. Daily precipitation over the basin from 2001–2018 for the wet season months of July to September is modeled using a 4-state HMM, and correlated precipitation amounts are generated from a mixture of Gamma distributions using Gaussian copulas for each state. Synthetic data from a model using a mixture of two Gamma distributions for the non-zero precipitation is shown to replicate the historical data better than a model using a single Gamma distribution.2 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.UMBC High Performance Computing Facility (HPCF)Text