Gamma Distribution Probability Model for Asian Summer Monsoon Monthly Rainfall

Mooley, D. A.

doi:10.1175/1520-0493(1973)101<0160:gdpmfa>2.3.co;2

Cited by 49 publications

(44 citation statements)

References 26 publications

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“…Most of these studies assume that F is a mixture of the Bernoulli and the Gamma distribution, where the Bernoulli distribution is used to model the probability of precipitation occurrence and the Gamma distribution used to model precipitation intensities (e.g. Thom, 1968;Mooley, 1973;Cannon, 2008). In this study, further mixtures, e.g.…”

Section: Distribution Derived Transformationsmentioning

confidence: 99%

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Gudmundsson

Bremnes

Haugen

et al. 2012

Hydrol. Earth Syst. Sci.

940

730

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Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for postprocessing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation.

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Section: Distribution Derived Transformationsmentioning

confidence: 99%

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Gudmundsson

Bremnes

Haugen

et al. 2012

Hydrol. Earth Syst. Sci.

940

730

View full text Add to dashboard Cite

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“…Mooley (1973) found that a gamma distribution is the most suitable probability density function for monthly rainfall in the Asian summer moonson. Groisman et al (1999) also applied the gamma PDF to assess extreme events in daily rainfall in eight different countries.…”

Section: Determining the Sample Size Of A Nonparametric Distributionmentioning

confidence: 99%

Estimating ensemble size requirements of AGCM simulations

Taschetto

England

2008

Meteorol Atmos Phys

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SummaryThis study investigates the statistical methods for determining the minimum sample size necessary for an ensemble set generated with an atmospheric general circulation model. Due to the limits imposed by computational cost, an improved and a priori estimation of ensemble size is highly desirable. In this context, the methodology shown here is an important step for defining the number of integrations required in a numerical experiment. We show that the global distribution of ensemble size has a spatial and seasonal dependence. In addition, the ensemble size is dependent on the variable being analyzed and the geographic region of interest. For example, we show that a relatively large number of integrations are required to simulate the seasonal mean air temperature at 925 hPa and the sea-level pressure at mid to high latitudes. The seasonal mean precipitation, however, can be well represented with relatively few integrations at high latitudes, but it requires a large ensemble size at the tropics, particularly over the monsoon regions. These latitudinal differences in the number of integrations are associated with the internal variability in the model. Furthermore, differences among the variable fields partly arise due to the distinct shapes of the associated property distributions. Both Gaussian and nonparametric statistics are considered here. The Wilcoxon Rank Sum test reveals that the air temperature, sea-level pressure and precipitation do not follow a Gaussian distribution in some regions of the globe. Thus, we suggest the nonparametric approach be used whenever the normal assumption is violated or cannot be assessed. This study is based on the determination of the sampling size of ensemble simulations using the statistical Gaussian method of Wehner (2000). We extend this previous by considering a nonparametric approach.

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“…One possibility is to fit a gamma distribution. 11 The type of analysis of amounts described above is useful and does provide valuable information, but is often all that is done. By making use of the daily data, many other characteristics of importance to agriculture can be studied.…”

Section: The Direct Methods Of Analysismentioning

confidence: 99%