A new daily weather generator to preserve extremes and low-frequency variability

Khazaei, Mohammad Reza; Ahmadi, Shahin; Saghafian, Bahram; Zahabiyoun, Bagher

doi:10.1007/s10584-013-0740-5

Cited by 20 publications

(33 citation statements)

References 29 publications

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“…The combination of Markov chains and two parameter Gamma distribution is an example of a weather generator (Richardson, ), in which Markov chains are used to predict the occurrences of a climatic variable and the Gamma distribution is used to determine the corresponding amounts. The applications of weather generators in statistical downscaling are found in the studies of Semenov and Stratonovitch (), Iizumi et al (), Khazaei et al ().…”

Section: Introductionmentioning

confidence: 99%

Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation

Sachindra

Huang

Barton

et al. 2014

Intl Journal of Climatology

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This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 GCM (HadCM3). Both models were based on the multi-linear regression (MLR) technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the NCEP/NCAR reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash-Sutcliffe Efficiency (NSE), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with NCEP/NCAR reanalysis data were identified. The HadCM3 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with NCEP/NCAR reanalysis data displayed NSEs of 0.74 and 0.70, respectively. The model built with HadCM3 outputs showed NSEs of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under-predict high precipitation values and over-predict near-zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with HadCM3 outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of HadCM3, showed that there is large bias in the precipitation output of HadCM3. This indicated the need of a bias-correction, which is detailed in the second companion article.

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Section: Introductionmentioning

confidence: 99%

Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation

Sachindra

Huang

Barton

et al. 2014

Intl Journal of Climatology

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“…As shown in the results of SSWG and MSWG untaper (Fig. 2), weather generators without considering the low-frequency variability usually underestimate the variances (Chen et al, 2010;Khazaei et al, 2013;Mehrotra and Sharma, 2007); however, MSWG taper presented better variance simulation since it overestimated the variances (Wilks, 1998). The variance overestimation from MSWG taper increased the variability of monthly precipitation and further influenced those of annual precipitation, which is the reason why stations passing the squared rank test for annual precipitation from MSWG taper were less than those from SSWG and MSWG untaper ( Table 1).…”

Section: Statistics Of Generated Precipitationmentioning

confidence: 88%

Links between the spatial structure of weather generator and hydrological modeling

Lü

et al. 2015

Theor Appl Climatol

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Impacts of the spatial structure of weather generators on hydrological modeling have been largely qualitatively discussed; however, their links have been rarely quantified. The precipitation occurrence and amount were respectively generated with Markov chain and the mixed exponential distribution for single sites, and then the procedures were extended to multi-site simulation according to Wilks (1998). In the multi-site model, precipitation amounts were respectively generated with untapered or tapered mixed exponential scale parameters. The generated precipitation series were used as inputs of Soil and Water Assessment Tool (SWAT) to interpret the links between the spatial structure of weather generators and hydrological modeling. The single-site and multi-site model using untapered scale parameters gave similar averages for monthly and annual streamflow; however, the untapered multi-site model was superior to simulating hydrological variability. The single-site model underestimated the maxima and variances while overestimated the minima of streamflow; therefore, the use of single-site models for hydrological variability simulation should be cautious. The multi-site model using tapered scale parameters greatly overestimated the averages, extremes, and variances of streamflow. The Wilks model for multi-site precipitation simulation using tapered scale parameters is not appropriate for hydrological modeling, and the untapered version is thus recommended. Overall, the spatial structure of weather generators has significant impacts on hydrological modeling, especially for hydrological variability simulation; therefore, the links between them should be paid great attentions.

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“…Several models dealing with climatic variables preserving the variability have been proposed in the literature Chen et al, 2010 andKhazaei et al, 2013). In other works such as Khazaei et al (2013) and Virgens Filho et al (2013), the authors have compared generators of climatic data.…”

Section: Introductionmentioning

confidence: 99%

“…In other works such as Khazaei et al (2013) and Virgens Filho et al (2013), the authors have compared generators of climatic data. Minuzzi et al (2010) have investigated trends of monthly climatic variables, such as minimum temperature in some stations in the state of Minas Gerais -Brazil.…”

Section: Introductionmentioning

confidence: 99%

“…While weather generators are good at preserving the precipitation quantity, they however underestimate lowfrequency variations. Khazaei et al (2013) proposed a daily weather generator to preserve extremes and low-frequency variability. Addresses also the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables.…”

Section: Introductionmentioning

confidence: 99%

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A New Methodology for Stochastic Simulation of Daily Climatic Data Preserving the Interannual Variability

Kist

Filho

Guardia

2017

ABClima

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ABSTRACT:In this work we propose a new methodology to reproduce, by means of simulations, the interannual variability of climatic variables which included only the minimum air temperature. To evaluate the performance of the proposed method, it was maked a comparison with other two weather generators (i.e., PGECLIMA_R and LARS-WG). Moreover, it was utilized the historical series of thirty years of five meteorological stations of the state of Parana -Brazil to generate ten sets of thirty years for each model, which were confronted with the respective historical series. The performance of the proposed model as well as weather generators was evaluated by applying tests of central tendency, variability and distribution. Furthermore, was utilized the statistical measures RMSE, MBE and Willmott agreement index (d). In the stations investigated, the proposed methodology reduced the total error and eliminated the negative bias of interannual variability. In only four (of 600) generated sequences the interannual variability differs significantly from the observed one. The series generated by PGECLIMA_R and LARS-WG presented rejection rate of 99% in the variability test. In this case, the bias was ten times greater and the RMSE was twice times greater than the proposed methodology. The d index was always greater than 0.98 for the five locations in the proposed methodology and around 0.83 in other models. Based on these results, the new methodology provides a relevant contribution concerning the interannual variability of climatic variables.Keywords: Simulation of climatic data, interannual variability, minimum air temperature. UMA NOVA METODOLOGIA PARA SIMULAÇÃO ESTOCÁSTICA DE DADOS CLIMÁTICOS DIÁRIOS PRESERVANDO A VARIABILIDADE INTERANUAL RESUMO:Este trabalho objetivou propor uma nova metodologia para reproduzir, por meio de simulações, a variabilidade interanual de variáveis climáticas que incluiu apenas a temperatura mínima do ar. Para avaliar o desempenho da metodologia proposta fez-se a comparação com outros dois geradores de clima: PGECLIMA_R e LARS-WG. Foram utilizadas séries históricas de trinta anos de cinco estações meteorológicas do estado do Paraná -Brasil para gerar dez conjuntos de trinta anos para cada modelo, que foram confrontados com a respectiva série histórica. O desempenho do modelo proposto e dos geradores de clima foi avaliado através da aplicação de testes de tendência central, variabilidade e distribuição. Além disso, utilizaram-se as medidas estatísticas RMSE, MBE e índice de concordância de Willmott (d). Nas estações analisadas, a metodologia proposta reduziu o erro total e eliminou o viés negativo da variabilidade interanual. Em apenas quatro (das 600) sequências geradas a variabilidade interanual difere significativamente da observada. As sequências geradas pelo PGECLIMA_R e LARS-WG apresentaram taxa de rejeição de 99% no teste de variabilidade. O viés nesses modelos foi dez vezes maior e o RMSE foi duas vezes maior do que na metodologia proposta. O índice d foi sempre maior do que 0,98 nas cin...

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A new daily weather generator to preserve extremes and low-frequency variability

Cited by 20 publications

References 29 publications

Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation

Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation

Links between the spatial structure of weather generator and hydrological modeling

A New Methodology for Stochastic Simulation of Daily Climatic Data Preserving the Interannual Variability

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