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zbMATH Open
Article . 2003
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Detecting Precipitation Climate Changes: An Approach Based on a Stochastic Daily Precipitation Model

Detecting precipitation climate changes: an approach based on a stochastic daily precipitation model
Authors: Neykov, N.; Neytchev, P.; Zucchini, W.;

Detecting Precipitation Climate Changes: An Approach Based on a Stochastic Daily Precipitation Model

Abstract

2002 Mathematics Subject Classification: 62M10. We consider development of daily precipitation models based on [3] for some sites in Bulgaria. The precipitation process is modelled as a two-state first-order nonstationary Markov model. Both the probability of rainfall occurrance and the rainfall intensity are allowed depend on the intensity on the preceeding day. To investigate the existence of long-term trend and of changes in the pattern of seasonal variation we use a synthesis of the methodology presented in [3] and the idea behind the classical running windows technique for data smoothing. The resulting time series of model parameters are used to quantify changes in the precipitation process over the territory of Bulgaria.

Country
Bulgaria
Keywords

Gamma Time Series, Time series, auto-correlation, regression, etc. in statistics (GARCH), Climate Change, Rainfall Modeling, Binary Time Series, Markov Chain, Applications of statistics to environmental and related topics, Generalized Linear Models

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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