Occurrence and quantity of precipitation can be modelled simultaneously

Dunn, Peter K.

doi:10.1002/joc.1063

Cited by 94 publications

(85 citation statements)

References 30 publications

(26 reference statements)

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“…The method is based on the fit to daily data of the Gamma distribution, which is believed to represent precipitation phenomena reliably (Bridges and Haan, 1972;Stern and Coe, 1984;Bradley et al, 1987;Wilks, 1995;Groisman et al, 1999;Nicholls and Murray, 1999;Dunn, 2004;Jones et al, 2004). To further support the use of the Gamma distribution, a Kolmogorov-Smirnov test has been applied to each station's daily data (a total of more than 13 000 daily Gamma distributions have been tested).…”

Section: Basics Of the Methodsmentioning

confidence: 99%

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Simolo

Brunetti

Maugeri

et al. 2010

Intl Journal of Climatology

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This work presents a novel method for estimating missing values in daily precipitation series. It is aimed at identifying the event time location with good accuracy and reconstructing the correct amount of daily rainfall. In addition, the statistical properties of the time series, i.e. both probability distribution and long-term statistics, are preserved. The completion method is based on a two-step algorithm that uses information from a cluster of neighboring stations. First, wet and dry days are tagged, and subsequently, the full precipitation amount for wet-classified days is estimated by a modified multi-linear regression approach. This method avoids overestimation of the number of wet days and underestimation of intense precipitation events, which are typical side effects of common regression-based approaches.

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Section: Basics Of the Methodsmentioning

confidence: 99%

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Simolo

Brunetti

Maugeri

et al. 2010

Intl Journal of Climatology

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“…The return period (RP) k associated with a given RV is defined as the inverse of the probability that the RV is reached or exceeded assuming Gamma distribution. Seasonal precipitation is generally believed to follow this distribution (Dunn, 2004). We also calculated RPs assuming normal distribution.…”

Section: Methodsmentioning

confidence: 99%

On the variability of return periods of European winter precipitation extremes over the last three centuries

Pauling

Paeth²

2007

Clim. Past

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Abstract.We investigate the changes of extreme European winter (December-February) precipitation back to 1700 and show for various European regions that return periods of extremely wet and dry winters are subject to significant changes both before and after the onset of anthropogenic influences. Generally, winter precipitation has become more extreme. We also examine the spatial pattern of the changes of the extremes covering the last 300 years where data quality is sufficient. Over central and Eastern Europe dry winters occurred more frequently during the 18th and the second part of the 19th century relative to 1951-2000. Dry winters were less frequent during both the 18th and 19th century over the British Isles and the Mediterranean. Wet winters have been less abundant during the last three centuries compared to 1951-2000 except during the early 18th century in central Europe. Although winter precipitation extremes are affected by climate change, no obvious connection of these changes was found to solar, volcanic or anthropogenic forcing. However, physically meaningful interpretation with atmospheric circulation changes was possible.

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“…They computed the Anscombe residuals from sites with observed rainfall in order to simulate adequate daily records with no missing values. The GLM framework has also been used to model the discrete and continuous nature of daily rainfall variables simultaneously [8][9][10] by fitting Tweedie GLMs to generate monthly rainfall data in 220 Australian stations. They incorporated 4 climatological variables, and the fitted models adequately predicted the preceding month.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Generation Using Markov Chain Models; Case Study: Central Aegean Sea

Mammas

Lekkas

2018

Water

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Generalized linear models (GLMs) are popular tools for simulating daily rainfall series. However, the application of GLMs in drought-prone areas is challenging, as there is inconsistency in rainfall data during long and irregular periods. The majority of studies include regions where rainfall is well distributed during the year indicating the capabilities of the GLM approach. In many cases, the summer period has been discarded from the analyses, as it affects predictive performance of the model. In this paper, a two-stage (occurrence and amounts) GLM is used to simulate daily rainfall in two Greek islands. Summer (June-August) smooth adjustments have been proposed to model the low probability of rainfall during summer, and consequently, to improve the simulations during autumn. Preliminary results suggest that the fitted models simulate adequate rainfall occurrence and amounts in Milos and Naxos islands, and can be used as input in future hydrological applications.

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Occurrence and quantity of precipitation can be modelled simultaneously

Cited by 94 publications

References 30 publications

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

On the variability of return periods of European winter precipitation extremes over the last three centuries

Rainfall Generation Using Markov Chain Models; Case Study: Central Aegean Sea

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