Modeling annual extreme temperature using generalized extreme value distribution: A case study in Malaysia

Hasan, Husna; Salam, Norfatin; Kassim, Suraiya

doi:10.1063/1.4801267

Cited by 20 publications

(24 citation statements)

References 5 publications

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“…The use of standard statistical techniques in modelling, forecasting and prediction of extremes in average rainfall and rare events is less prudent because of gross under-estimation. 4 Extreme value theory is an alternative and superior approach to quantify the stochastic behaviour of a process at unusually large or small levels. 4 Extreme value theory provides the statistical framework to make inferences about the probability of very rare and extreme events.…”

Section: Introductionmentioning

confidence: 99%

“…4 Extreme value theory is an alternative and superior approach to quantify the stochastic behaviour of a process at unusually large or small levels. 4 Extreme value theory provides the statistical framework to make inferences about the probability of very rare and extreme events. It is based on the analysis of the maximum (or minimum) value in a selected time period.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe

Chikobvu¹,

Chifurira²

2015

S. Afr. J. Sci.

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We modelled the mean annual rainfall for data recorded in Zimbabwe from 1901 to 2009. Extreme value theory was used to estimate the probabilities of meteorological droughts. Droughts can be viewed as extreme events which go beyond and/or below normal rainfall occurrences, such as exceptionally low mean annual rainfall. The duality between the distribution of the minima and maxima was exploited and used to fit the generalised extreme value distribution (GEVD) to the data and hence find probabilities of extreme low levels of mean annual rainfall. The augmented Dickey Fuller test confirmed that rainfall data were stationary, while the normal quantile-quantile plot indicated that rainfall data deviated from the normality assumption at both ends of the tails of the distribution. The maximum likelihood estimation method and the Bayesian approach were used to find the parameters of the GEVD. The Kolmogorov-Smirnov and Anderson-Darling goodnessof-fit tests showed that the Weibull class of distributions was a good fit to the minima mean annual rainfall using the maximum likelihood estimation method. The mean return period estimate of a meteorological drought using the threshold value of mean annual rainfall of 473 mm was 8 years. This implies that if in the year there is a meteorological drought then another drought of the same intensity or greater is expected after 8 years. It is expected that the use of Bayesian inference may better quantify the level of uncertainty associated with the GEVD parameter estimates than with the maximum likelihood estimation method. The Markov chain Monte Carlo algorithm for the GEVD was applied to construct the model parameter estimates using the Bayesian approach. These findings are significant because results based on non-informative priors (Bayesian method) and the maximum likelihood method approach are expected to be similar.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe

Chikobvu¹,

Chifurira²

2015

S. Afr. J. Sci.

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“…The work was later extended to monthly maximum temperature data for twenty-two meteorological stations in Malaysia and Hasan et. al [14] concluded that the GEV distribution is an appropriate distribution for describing the extreme maximum temperatures in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have been conducted in Malaysia that concern the modeling of extreme temperatures using the GEV distribution. Hasan et al [12] modeled maximum temperature data using the GEV distribution over five different time periods: weekly, biweekly, monthly, quarterly, and half yearly. The data used were from Penang weather stations from 2000 to 2009 and on a sample of 10 stations (see [13]).…”

Section: Introductionmentioning

confidence: 99%

“…Hasan et al [12] modeled maximum temperature data using the GEV distribution over five different time periods: weekly, biweekly, monthly, quarterly, and half yearly. The data used were from Penang weather stations from 2000 to 2009 and on a sample of 10 stations (see [13]). The work was later extended to monthly maximum temperature data for twenty-two meteorological stations in Malaysia and Hasan et.…”

Section: Introductionmentioning

confidence: 99%

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Stationary and non-stationary extreme value modeling of extreme temperature in Malaysia

Hasan¹,

Salleh²,

Kassim³

2014

AIP Conference Proceedings

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Extreme annual temperature of eighteen stations in Malaysia is fitted to the Generalized Extreme Value distribution. Stationary and non-stationary models with trend are considered for each station and the Likelihood Ratio test is used to determine the best-fitting model. Results show that three out of eighteen stations i.e. Bayan Lepas, Labuan and Subang favor a model which is linear in the location parameter. A hierarchical cluster analysis is employed to investigate the existence of similar behavior among the stations. Three distinct clusters are found in which one of them consists of the stations that favor the nonstationary model. T-year estimated return levels of the extreme temperature are provided based on the chosen models.

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A comparative study to determine the optimal copula model for the wind speed and precipitation of typhoons

Joo

Nam

et al. 2016

Intl Journal of Climatology

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Typhoon is usually one of main reasons to damage the buildings, infrastructures, water operations, and human life in summer in East Asia, which includes China, Japan, and Korea. Typhoons can be considered multivariate extremes that consists of climate variables that affect wind and precipitation. Copula models can be used to investigate the dependence between random variables. In this study, we used copula models to investigate the optimal marginal distribution for the relations between the wind speed and the precipitation of typhoons at the Jeju weather station in South Korea. We used the 10‐min average wind speed (W10) and the total precipitation (PT) from 65 typhoon events that had affected the Jeju station. Two case studies were considered using the typhoon data. Case one included all typhoon events, and case two included 39 typhoon events with the following thresholds: W10 ≥ 10 m s−1 and PT ≥ 50 mm. The marginal distributions for the copula models included the generalized extreme value (GEV), generalized logistic (GLO), generalized Pareto (GPA), and Weibull (WBU) distributions as well as three copula models: Clayton, Frank, and Gumbel copulas. Each step was checked by the robust diagnostic, the probability plot correlation coefficient (PPCC) test, and the goodness‐of‐fit test (Sn statistic). The Frank copula model with GLO (W10) – GPA (PT) had the best performance in both cases. The results of this study present the key steps needed to identify an optimum copula for a bivariate distribution in applications related to atmospheric sciences and provides an example of how this applies to wind velocity and precipitation from typhoons.

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Modeling annual extreme temperature using generalized extreme value distribution: A case study in Malaysia

Cited by 20 publications

References 5 publications

Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe

Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe

Stationary and non-stationary extreme value modeling of extreme temperature in Malaysia

A comparative study to determine the optimal copula model for the wind speed and precipitation of typhoons

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