Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin. Issue 1 (25th January 2021)

• Record Type: Journal Article
• Authors: Kroiz, Gerson C.; Majumder, Reetam; Gobbert, Matthias K.; Neerchal, Nagaraj K.; Markert, Kel; Mehta, Amita
• Other Names: Kuhl D. guestEditor.; Meister A. guestEditor.; Ricoeur A. guestEditor.; Wünsch O. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 20:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-25
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.202000117
• Languages: English
• ISSNs: 1617-7061

Abstract

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.