Stochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Models

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dc.contributor.authorKroiz, Gerson C.
dc.contributor.authorBasalyga, Jonathan N.
dc.contributor.authorUchendu, Uchendu
dc.contributor.authorMajumder, Reetam
dc.contributor.authorBarajas, Carlos A.
dc.contributor.authorGobbert, Matthias K.
dc.contributor.authorMarkert, Kel
dc.contributor.authorMehta, Amita
dc.contributor.authorNeerchal, Nagaraj K.
dc.date.accessioned2020-07-28T17:31:58Z
dc.date.available2020-07-28T17:31:58Z
dc.description.abstractA daily precipitation generator based on hidden Markov models (HMM) using satellite precipitation estimates is studied for the Potomac river basin in Eastern USA over the wet season months of July to September. GPM-IMERG data between 2001–2018 is used for the study, which at a 0.1◦ × 0.1◦ spatial resolution results in 387 grid points across the basin. A 4-state model has been considered for the state process, and the semi-continuous emission distribution for precipitation at each location is modeled using a mixture comprising a delta function at 0 and two Gamma distributions. The underestimation of the observed spatial correlations between the grid points based on this model is noted, and the HMM is extended using Gaussian copulas to generate spatially correlated precipitation amounts. Performance of this model is examined in terms of dry and wet day stretches, spatial correlations between grid points, and extreme precipitation events. The HMM with Gaussian copulas (HMM-GC) is shown to outperform the classical HMM formulation for precipitation generation when using remote sensing data in the Potomac river basin.en_US
dc.description.sponsorshipThis work is supported by the grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC–1730250). Co-authors Gerson Kroiz, Jonathan Basalyga, Uchendu Uchendu were supported through an REU Supplement to this grant. Co-author Gerson Kroiz was also supported through an Undergraduate Research Award (URA) from UMBC. The hardware in the UMBC High Performance Computing Facility (HPCF) is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. Co-author Reetam Majumder was supported by JCET and as HPCF RA. Co-author Carlos Barajas also acknowledges support as HPCF RA.en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/CT2020Team1.pdfen_US
dc.format.extent19 pagesen_US
dc.genretechnical reportsen_US
dc.identifierdoi:10.13016/m2csmw-0p6s
dc.identifier.citationGerson C. Kroiz et al., Stochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Models, http://hpcf-files.umbc.edu/research/papers/CT2020Team1.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/19254
dc.language.isoen_USen_US
dc.publisherUMBCen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofseriesHPCF;2020–11
dc.rightsThis 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.
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleStochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Modelsen_US
dc.typeTexten_US

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