Trends and periodic movements in climatic series are treated as on-stationary components. A time series model and Bayesian statistics are combined through a Markov chain Monte Carlo procedure. Gibbs sampling is used in the Monte Carlo application. Monthly series of river flow, rainfall and temperature from northern Italy are used. Some late temperature rises are noted, otherwise there are no systematic increases or decreases in the series. Changes in periodicity are also of a random nature. From the results it is also possible to compare these properties between different locations and climatic indicators
Stochastic modelling of periodicities and trends for multisite daily rainfall simulation
KOTTEGODA, NATHABANDU THILAKAS;NATALE, LUIGI;
2008-01-01
Abstract
Trends and periodic movements in climatic series are treated as on-stationary components. A time series model and Bayesian statistics are combined through a Markov chain Monte Carlo procedure. Gibbs sampling is used in the Monte Carlo application. Monthly series of river flow, rainfall and temperature from northern Italy are used. Some late temperature rises are noted, otherwise there are no systematic increases or decreases in the series. Changes in periodicity are also of a random nature. From the results it is also possible to compare these properties between different locations and climatic indicatorsFile in questo prodotto:
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