Selection of the best fit probability distributions for temperature data and the use of L-moment ratio diagram method: a case study for NSW in Australia

Haddad, Khaled

doi:10.1007/s00704-020-03455-2

Cited by 12 publications

(7 citation statements)

References 76 publications

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“…GEV distribution was selected to develop both stationary and non-stationary TDF curves as it is widely used to develop model climatic extremes (Ouarda and Charron, 2018a;Ouarda, Charron and St-Hilaire, 2020;Haddad, 2021;Devi, Gouda and Lenka, 2022). Under the condition of stationary, three parameters of the GEV distributions, μ, σ, ξ, which are the location, scale and shape parameters, respectively, were assumed to be constant, whereas, under non-stationary conditions, the parameters were expressed as a linear or quadratic function of the covariate(s).…”

Section: Methodsmentioning

confidence: 99%

Stationary and Non-stationary Temperature-Duration-Frequency Curves for Australia

Laz

Rahman

Ouarda

et al. 2023

Preprint

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Australian summer heat events have become more frequent and severe in recent times. Temperature-duration-frequency (TDF) curves connect the severity of heat episodes of various durations to their frequencies and thus can be an effective tool for analysing the heat extremes. This study examines Australian heat events using data from 82 meteorological stations. TDF curves have been developed under stationary and non-stationary conditions. Generalised Extreme Value (GEV) distribution is considered to estimate extreme temperatures for return periods of 2, 5, 10, 25, 50 and 100 years. Three major climate drivers for Australia have been considered as potential covariates along with Time to develop the non-stationary TDF curves. According to the Akaike information criterion, the non-stationary framework for TDF modelling provides a better fit to the data than its stationary equivalent. The findings can be beneficial in offering new information to aid climate adaptation and mitigation at the regional level in Australia.

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Section: Methodsmentioning

confidence: 99%

Stationary and Non-stationary Temperature-Duration-Frequency Curves for Australia

Laz

Rahman

Ouarda

et al. 2023

Preprint

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“…In previous research, a normal probability distribution was found to be a good fit primarily for maximum temperatures. Haddad (2021) demonstrate that the gamma distribution is more appropriate when considering daily temperature data, which incorporates both the maximum and minimum temperatures recorded on a given day. The probability density function of the gamma distribution is described by the function (1).…”

Section: Estimating a Gamma Distribution For Temperature Datamentioning

confidence: 99%

“…Gamma distribution was fitted to daily temperature data (see justification in Section 2.2). Below is the provided description of the gamma distribution as outlined in the work by (Haddad, 2021). Definition 1.…”

Section: Preliminary Conceptsmentioning

confidence: 99%

“…For instance, Andrews and Shivamoggi (1990) found the gamma distribution provided a good fit to temperature data and effectively estimated temperature, while Moriarty et al (2020) found it suitable for estimating expected temperature in the future. Haddad (2021) shows the gamma distribution is appropriate for daily temperature readings, accommodating both maximum and minimum temperatures and non-negative values. However, the literature does not address how to ensure an associated stochastic differential equation satisfies the FokkerPlanck equation and Markovian autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

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Pricing Gamma Based Temperature Derivatives

Vwalika,

Dzupire

2024

Preprint

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Farmers are impacted by temperature as high temperatures during the rainy season can lead to a substantial decrease in crop production. To safeguard farmers from this risk, temperature derivatives can be used, but they are frequently mispriced. This study aims to address this issue by developing a Stochastic Differential Equation (SDE) for temperature, with the assumption that it conforms to a gamma distribution. A synthesis technique that effectively manages the auto correlation within the data is employed to deduce the SDE. The resulting pricing formula is based on the anticipated value derived from the SDE. Notably, the formulated equation’s outcome is not linked to the expected temperature itself, but rather hinges on the gamma distribution parameters and the trigger temperature. This approach yields accurate forecasts for both price predictions and temperature projections. The model is found to predict temperature with R2 = 91%, MSE = 0.14, and MAPE = 1.3%. When used to price call option, the prices decrease with increase in trigger value, which is more realistic. Thus, the model is more flexible.

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“…Five distributions are the most commonly used in Peninsular Malaysia, namely generalized extreme value distribution, generalized Pareto distributions, generalized logistics distributions, lognormal distributions and Pearson 3 distributions (Romali et al, 2017;Badyalina et al, 2013). Then the parameter of the distribution can be estimated using the L-Moment method or maximum likelihood estimation (Mokhtar et al, 2021;Haddad (2021); Sahu et al, 2021;Mokhtar et al, 2021;Ming et al, 2021;Jan et al, 2018). After obtaining the distribution parameter, the best-fitted distribution will be determined, and flood quantiles will be calculated using the best distribution for each station.…”

Section: Introductionmentioning

confidence: 99%

Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

Badyalina¹,

Shabri²,

Mokhtar³

et al. 2021

IJSP

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Handling flood quantile with little data is essential in managing water resources. In this paper, we propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia. In this proposed MGMDH model, the principal component analysis (PCA) method is matched to the group method of data handling (GMDH) with various transfer functions. The MGMDH model consists of four transfer functions: polynomial, sigmoid, radial basis function, and hyperbolic tangent sigmoid transfer functions. The prediction performance of MGMDH models is compared to the conventional GMDH model. The appropriateness and effectiveness of the proposed models are demonstrated with a simulation study. Cauchy distribution is used in the simulation study as a disturbance error. The implementation of Cauchy Distribution as an error disturbance in artificial data illustrates the performance of the proposed models if the extreme value or extreme event occurs in the data set. The simulation study may say that the MGMDH model is superior to other comparison models, namely LR, NLR, GMDH and ANN models. Another beauty of this proposed model is that it shows a strong prediction performance when multicollinearity is absent in the data set.

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Selection of the best fit probability distributions for temperature data and the use of L-moment ratio diagram method: a case study for NSW in Australia

Cited by 12 publications

References 76 publications

Stationary and Non-stationary Temperature-Duration-Frequency Curves for Australia

Stationary and Non-stationary Temperature-Duration-Frequency Curves for Australia

Pricing Gamma Based Temperature Derivatives

Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

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