L-moments and C-moments

Ulrych, Tadeusz J.; Velis, Danilo R.; Woodbury, Allan D.; Sacchi, Mauricio D.

doi:10.1007/s004770050004

Cited by 36 publications

(28 citation statements)

References 20 publications

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“…We use the Lmoment method (Hosking 1990; Fortran routines are available at the Internet location http://lib.stat.cmu.edu/general/ lmoments) to estimate the parameters for the above distributions. The L-moments, though analogous to the conventional central moments, are able to characterize a wider range of distributions and are more robust to the outliers (Hosking 1992;Ulrych et al 2000).…”

Section: Spei/spi Calculationsmentioning

confidence: 99%

Constructing a 1-km Gridded Multi-Scalar Drought Index Dataset (1960 - 2012) in Taiwan Based on the Standardized Precipitation Evapotranspiration Index-SPEI

Weng¹

2016

Terr. Atmos. Ocean. Sci.

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An index sensitive to global warming, the standardized precipitation evapotranspiration index (SPEI), is employed in this study to construct a 1-km gridded multi-scalar drought index data bank in Taiwan. A site-and scale-dependent posterior fitness assessment procedure regarding determination of the most appropriate statistical distribution is used to standardize the station's water deficit/surplus series, thereby SPEI at various time scales. Model uncertainty at different scales is evaluated and the results show that the uncertainty is higher for the shorter 10-day scale. Contrasts in the climatic means between SPEI and popular standardized precipitation index (SPI) are compared. The climatic pre-summer monsoon heating and midsummer drought phenomena are better captured by the SPEI. The established data bank, one of the data integration tasks in the Taiwan Climate Change Projection and Information Platform (TCCIP) project, is helpful in historical drought diagnostics. It also serves as the ground truth to correct model biases while using the regional model to project hydro-climate change and impact assessment, and historical baseline in the statistical downscaling while developing the statistical relationship between the general circulation model outputs and fine-scale observations. As an example of applications, the gridded SPEI at 3-month time scale is used to study the interannual variability in springtime drought. The results show that Taiwan's southwestern plains region is most vulnerable to the risk of droughts. Composite analysis reveals that possible causes of island-wide drought link to the turnabout of cold El Niño-Southern Oscillation (ENSO) phase but also to the interannual variability in Pacific Decadal Oscillation when the analysis is initiated from the regional perspective.

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Section: Spei/spi Calculationsmentioning

confidence: 99%

Constructing a 1-km Gridded Multi-Scalar Drought Index Dataset (1960 - 2012) in Taiwan Based on the Standardized Precipitation Evapotranspiration Index-SPEI

Weng¹

2016

Terr. Atmos. Ocean. Sci.

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“…The issue of L-moments is discussed, for example, in [3] or [4]. Let X be a continuous random variable being distributed with the distribution function F(x) and quantile function x(F).…”

Section: L-moments Of Probability Distributionmentioning

confidence: 99%

“…Main properties of the probability distribution are very well summarized by the following four characteristics: L-location λ 1 , L-variability λ 2 , L-skewness τ 3 and L-kurtosis τ 4 . L-moments λ 1 and λ 2 , the L-coefficient of variation τ and ratios of L-moments τ 3 and τ 4 are the most useful characteristics for the summarization of the probability distribution.…”

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confidence: 99%

L-Moments and TL-Moments as an Alternative Tool of Statistical Data Analysis

Bílková¹

2014

JAMP

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Moments and cumulants are commonly used to characterize the probability distribution or observed data set. The use of the moment method of parameter estimation is also common in the construction of an appropriate parametric distribution for a certain data set. The moment method does not always produce satisfactory results. It is difficult to determine exactly what information concerning the shape of the distribution is expressed by its moments of the third and higher order. In the case of small samples in particular, numerical values of sample moments can be very different from the corresponding values of theoretical moments of the relevant probability distribution from which the random sample comes. Parameter estimations of the probability distribution made by the moment method are often considerably less accurate than those obtained using other methods, particularly in the case of small samples. The present paper deals with an alternative approach to the construction of an appropriate parametric distribution for the considered data set using order statistics.

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“…The issue of L-moments is discussed, for example, in (Adamowski, 2000) or (Ulrych, 2000). Let X be a continuous random variable being distributed with the distribution function F(x) and quantile function x(F).…”

Section: L-moments Of Probability Distributionsmentioning

confidence: 99%