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Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin
Author(s) -
Kroiz Gerson C.,
Majumder Reetam,
Gobbert Matthias K.,
Neerchal Nagaraj K.,
Markert Kel,
Mehta Amita
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000117
Subject(s) - precipitation , hidden markov model , environmental science , structural basin , gamma distribution , replicate , climatology , drainage basin , water year , gaussian , meteorology , hydrology (agriculture) , mathematics , statistics , geography , geology , computer science , cartography , artificial intelligence , geotechnical engineering , paleontology , physics , quantum mechanics
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.