FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1992303965> ?p ?o ?g. }

• W1992303965 endingPage "195" @default.
• W1992303965 startingPage "181" @default.
• W1992303965 abstract "CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 24:181-195 (2003) - doi:10.3354/cr024181 Conditional stochastic model for generating daily precipitation time series Aristita Busuioc1,*, Hans von Storch2 1National Institute of Meteorology and Hydrology, Sos. Bucuresti-Ploiesti 97, Sector 1, Bucharest, Romania 2Institute for Coastal Research, GKSS Research Center, Max-Planck Straße 1, 21502 Geesthacht, Germany *Email: busuioc@meteo.inmh.ro ABSTRACT: The purpose of this paper is the construction of a conditional stochastic model to generate daily precipitation time series. The model is a mixture of a 2-state first-order Markov chain and a statistical downscaling model based on canonical correlation analysis (CCA). The CCA model links the large-scale circulation, represented by the European sea-level pressure (SLP) field, with 4 precipitation distribution parameters: i.e. 2 transition probabilities and 2 gamma distribution parameters. This model is tested for the Bucharest station, for which long observed daily time series were available (1901-1999). The comparison of the capabilities of the conditional stochastic model and an unconditional stochastic model (based only on a Markov chain) is presented using ensembles of 1000 runs of the 2 models. The performance of the conditional stochastic model is analyzed in 2 steps. First, the ability of the CCA model for estimating the 4 precipitation distribution parameters is assessed. Second, the performance of both stochastic models in reproducing the statistical features of the observed precipitation time series is analyzed. The CCA model is most accurate for winter and autumn (transition probabilities), less accurate for the mean precipitation amount on wet days and inaccurate for the shape parameter. There are no significant dissimilarities between the conditional and unconditional models regarding their performance except for the linear trend and interannual variability, which are better captured by the conditional model. Some statistical features are well reproduced by both stochastic models for all seasons, such as mean and expected maximum duration of wet/dry intervals, daily mean of precipitation for wet days. Other statistical features are only partially reproduced by both models or are better reproduced by one of the models, such as mean duration of dry interval, standard deviation of daily precipitation amount, seasonal mean of rainy days and expected maximum daily precipitation. For all seasons, generally, the frequency of shorter dry intervals is underestimated and that of longer dry intervals (greater than 9 d) is overestimated. In conclusion, the conditional stochastic model presented in this paper can be used to generate daily precipitation time series for winter and autumn. For the other seasons, the unconditional model can be used to reproduce some statistical features. KEY WORDS: Stochastic model · Precipitation · CCA · Markov chain · Monte Carlo experiments Full text in pdf format PreviousExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 24, No. 2. Online publication date: July 28, 2003 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2003 Inter-Research." @default.
• W1992303965 created "2016-06-24" @default.
• W1992303965 creator A5066614334 @default.
• W1992303965 creator A5082417191 @default.
• W1992303965 date "2003-01-01" @default.
• W1992303965 modified "2023-09-23" @default.
• W1992303965 title "Conditional stochastic model for generating daily precipitation time series" @default.
• W1992303965 cites W1975223066 @default.
• W1992303965 cites W1985443408 @default.
• W1992303965 cites W2006408698 @default.
• W1992303965 cites W2016420963 @default.
• W1992303965 cites W2019296209 @default.
• W1992303965 cites W2025235817 @default.
• W1992303965 cites W2027364672 @default.
• W1992303965 cites W2047291553 @default.
• W1992303965 cites W2060918051 @default.
• W1992303965 cites W2080712057 @default.
• W1992303965 cites W2091206738 @default.
• W1992303965 cites W2094697813 @default.
• W1992303965 cites W2101036997 @default.
• W1992303965 cites W2116730005 @default.
• W1992303965 cites W2118733873 @default.
• W1992303965 cites W2135059533 @default.
• W1992303965 cites W2173218178 @default.
• W1992303965 cites W2174817738 @default.
• W1992303965 cites W2175192829 @default.
• W1992303965 doi "https://doi.org/10.3354/cr024181" @default.
• W1992303965 hasPublicationYear "2003" @default.
• W1992303965 type Work @default.
• W1992303965 sameAs 1992303965 @default.
• W1992303965 citedByCount "21" @default.
• W1992303965 countsByYear W19923039652013 @default.
• W1992303965 countsByYear W19923039652014 @default.
• W1992303965 countsByYear W19923039652016 @default.
• W1992303965 countsByYear W19923039652019 @default.
• W1992303965 countsByYear W19923039652021 @default.
• W1992303965 countsByYear W19923039652022 @default.
• W1992303965 crossrefType "journal-article" @default.
• W1992303965 hasAuthorship W1992303965A5066614334 @default.
• W1992303965 hasAuthorship W1992303965A5082417191 @default.
• W1992303965 hasBestOaLocation W19923039651 @default.
• W1992303965 hasConcept C105795698 @default.
• W1992303965 hasConcept C107054158 @default.
• W1992303965 hasConcept C127491075 @default.
• W1992303965 hasConcept C143724316 @default.
• W1992303965 hasConcept C149782125 @default.
• W1992303965 hasConcept C151730666 @default.
• W1992303965 hasConcept C153294291 @default.
• W1992303965 hasConcept C205649164 @default.
• W1992303965 hasConcept C33923547 @default.
• W1992303965 hasConcept C41156917 @default.
• W1992303965 hasConcept C43555835 @default.
• W1992303965 hasConcept C86803240 @default.
• W1992303965 hasConcept C98763669 @default.
• W1992303965 hasConceptScore W1992303965C105795698 @default.
• W1992303965 hasConceptScore W1992303965C107054158 @default.
• W1992303965 hasConceptScore W1992303965C127491075 @default.
• W1992303965 hasConceptScore W1992303965C143724316 @default.
• W1992303965 hasConceptScore W1992303965C149782125 @default.
• W1992303965 hasConceptScore W1992303965C151730666 @default.
• W1992303965 hasConceptScore W1992303965C153294291 @default.
• W1992303965 hasConceptScore W1992303965C205649164 @default.
• W1992303965 hasConceptScore W1992303965C33923547 @default.
• W1992303965 hasConceptScore W1992303965C41156917 @default.
• W1992303965 hasConceptScore W1992303965C43555835 @default.
• W1992303965 hasConceptScore W1992303965C86803240 @default.
• W1992303965 hasConceptScore W1992303965C98763669 @default.
• W1992303965 hasLocation W19923039651 @default.
• W1992303965 hasOpenAccess W1992303965 @default.
• W1992303965 hasPrimaryLocation W19923039651 @default.
• W1992303965 hasRelatedWork W1861436242 @default.
• W1992303965 hasRelatedWork W1898378792 @default.
• W1992303965 hasRelatedWork W1994701662 @default.
• W1992303965 hasRelatedWork W2048503975 @default.
• W1992303965 hasRelatedWork W2074337504 @default.
• W1992303965 hasRelatedWork W2748952813 @default.
• W1992303965 hasRelatedWork W2971525397 @default.
• W1992303965 hasRelatedWork W4322624506 @default.
• W1992303965 hasRelatedWork W4380088519 @default.
• W1992303965 hasRelatedWork W4386450442 @default.
• W1992303965 hasVolume "24" @default.
• W1992303965 isParatext "false" @default.
• W1992303965 isRetracted "false" @default.
• W1992303965 magId "1992303965" @default.
• W1992303965 workType "article" @default.