Evaluation of alternative Markovian models for precipitation occurrence in Oregon
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Evaluation of alternative Markovian models for precipitation occurrence in Oregon

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Published .
Written in English


  • Mathematical statistics.,
  • Precipitation (Meteorology) -- Measurement.

Book details:

Edition Notes

Statementby George Modesto Adotevi-Akue.
The Physical Object
Pagination29 leaves, bound :
Number of Pages29
ID Numbers
Open LibraryOL17949245M

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Evaluation of alternative Markovian models for precipitation occurrence in Oregon Subject: Precipitation (Meteorology) -- Measurement, Mathematical statistics Material Types: Article Abstract: Precipitation occurrence in three winter periods and three summer periods is examined for two Oregon stations: Seaside and Squaw Butte. The winter. Evaluation of alternative Markovian models for precipitation occurrence in Oregon. By. and then proceeding from the assumption\ud that daily precipitation occurrence is a two-state Markov Chain\ud of order 4, the hypotheses are tested that the order of dependence\ud is 0, 1, 2, and 3 within the fourth order. a Markovian model of an. Daily precipitation occurrences modelling with Markov chain of seasonal order. A specific example in which a third‐order model is required to depict the precipitation occurrence in winter is also given in some detail. Therefore the proper Markov order describing the daily precipitation occurrence process has to be determined and cannot be assumed a priori. The common practice of assuming that the Markov order is always.

  The Markovian arrival process (MAP) is a stochastic process that allows for modeling dependent and non-exponentially distributed to its versatility, it has been widely applied in different contexts, from reliability to teletraffic. In this work we show the suitability of the MAP for modeling daily precipitation data, which are often characterized by a non-negligible. A family of multivariate models for the occurrence/nonoccurrence of precipitation at N sites is constructed by assuming a different joint probability of events at the sites for each of a number of. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. An evaluation of methods for determining during-storm precipitation models require information on precipitation volume and phase at sub-event time-scales. Past successes in simulating both the devel- Oregon, Utah, Idaho, Colorado and California, and all required accurate estimates of precipitation phase. To separate mixed-phase.

Markovian Model of Rainfall Pattern with Application Lucy Makokha1, Kennedy Nyongesa2, Sport Climate is the weather conditions of a region as temperature, air, pressure, humidity, The Markov chain model assumes that the occurrence of a wet, medium wet or dry week depends on. The results are as follows: the transition probability of two successive wet days for 30 years at the 14 stations is , and the statistical tests show that the transitions of daily precipitation occurrence in South Korea can have the Markov chain property and be stationary in time, except at Ullung‐do, Seoul, Kangnung, and Mokpo, but. A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceara in northeast Brazil during the February–April wet season – The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian . That is, this study will lead us to know more detailed information about the rainfall pattern due to the climate change. 2. A Markov Chain Model for Daily Rainfall Occurrence. The daily rainfall model based on a Markov chain decides rainfall occurrence (i.e., the wet or dry conditions) based on transition probabilities [31–35]. The transition.