A Comparison of Stochastic Precipitation Generation Models for the Potomac River Basin
| dc.contributor.author | Kroiz, Gerson C. | |
| dc.contributor.author | Gobbert, Matthias K. | |
| dc.date.accessioned | 2020-07-28T18:18:07Z | |
| dc.date.available | 2020-07-28T18:18:07Z | |
| dc.description | UMBC High Performance Computing Facility | en_US |
| dc.description.abstract | Weather ensembles are an integral part of weather forecasting and can also be used to test the sensitivity and performance of climate models. Among meteorological variables, simultaneous simulation of precipitation at multiple sites presents unique challenges since precipitation has a semi-continuous distribution. We compare Robertson’s hidden Markov model setup with Wilks’ Multivariate Markov Chain based generator to see how well they recreate the spatiotemporal characteristic of gridded satellite precipitation estimates. Our results show that the Wilks method does a better job of capturing spatial correlations, while the HMM model can estimate and simulate longer durations of time. | en_US |
| dc.description.sponsorship | Special thanks to Reetam Majumder, the research assistant for this work, for providing background information and aiding with any issues throughout the project. I would also like to acknowledge Dr. Gobbert for advising the research project and guiding the progress of the work. I also acknowledge funding via the grant CyberTraining: DSE: Cross-Trainingof Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC–1730250). I 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. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/Kroiz_SeniorThesis2020.pdf | en_US |
| dc.format.extent | 9 pages | en_US |
| dc.genre | senior theses | en_US |
| dc.identifier | doi:10.13016/m2wo71-nons | |
| dc.identifier.citation | Gerson C. Kroiz, A Comparison of Stochastic Precipitation Generation Models for the Potomac River Basin, Spring 2020, http://hpcf-files.umbc.edu/research/papers/Kroiz_SeniorThesis2020.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19258 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UMBC | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This 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.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | A Comparison of Stochastic Precipitation Generation Models for the Potomac River Basin | en_US |
| dc.type | Text | en_US |