Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin
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Gerson 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.pdf
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Abstract
A 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.