A spatiotemporal precipitation generator based on a censored latent Gaussian field
dc.contributor.author | Baxevani, Anastassia | en |
dc.contributor.author | Lennartsson, J. | en |
dc.creator | Baxevani, Anastassia | en |
dc.creator | Lennartsson, J. | en |
dc.date.accessioned | 2019-12-02T10:33:48Z | |
dc.date.available | 2019-12-02T10:33:48Z | |
dc.date.issued | 2015 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/56471 | |
dc.description.abstract | A daily stochastic spatiotemporal precipitation generator that yields precipitation realizations that are quantitatively consistent is described. The methodology relies on a latent Gaussian field that drives both the occurrence and intensity of the precipitation process. For the precipitation intensity, the marginal distributions, which are space and time dependent, are described by a composite model of a gamma distribution for observations below some threshold with a generalized Pareto distribution modeling the excesses above the threshold. Model parameters are estimated from data and extrapolated to locations and times with no direct observations using linear regression of position covariates. One advantage of such a model is that stochastic generator parameters are readily available at any location and time of the year inside the stationarity regions. The methodology is illustrated for a network of 12 locations in Sweden. Performance of the model is judged through its ability to accurately reproduce a series of spatial dependence measures and weather indices. © 2015. American Geophysical Union. All Rights Reserved. | en |
dc.source | Water Resources Research | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937524591&doi=10.1002%2f2014WR016455&partnerID=40&md5=d9c626bb91ee4259136659f65aee2ae8 | |
dc.subject | parameterization | en |
dc.subject | regression analysis | en |
dc.subject | Sweden | en |
dc.subject | Stochastic systems | en |
dc.subject | Stochastic models | en |
dc.subject | Gaussian distribution | en |
dc.subject | Digital storage | en |
dc.subject | spatiotemporal analysis | en |
dc.subject | Precipitation (chemical) | en |
dc.subject | Location | en |
dc.subject | Gaussian method | en |
dc.subject | censoring | en |
dc.subject | climate modeling | en |
dc.subject | covariance structure | en |
dc.subject | Covariance structures | en |
dc.subject | extremes | en |
dc.subject | Gaussian field | en |
dc.subject | linear programing | en |
dc.subject | Pareto principle | en |
dc.subject | precipitation assessment | en |
dc.subject | precipitation intensity | en |
dc.subject | Precipitation model | en |
dc.subject | precipitation modeling | en |
dc.subject | space-time latent Gaussian field | en |
dc.subject | spatial data | en |
dc.title | A spatiotemporal precipitation generator based on a censored latent Gaussian field | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1002/2014WR016455 | |
dc.description.volume | 51 | |
dc.description.issue | 6 | |
dc.description.startingpage | 4338 | |
dc.description.endingpage | 4358 | |
dc.author.faculty | Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :1</p> | en |
dc.source.abbreviation | Water Resour.Res. | en |
dc.contributor.orcid | Baxevani, Anastassia [0000-0002-7498-9048] | |
dc.gnosis.orcid | 0000-0002-7498-9048 |
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