Modelling Bivriate Data Containing Zeroes, with an Analysis of Daily Rainfall Data

Shimizu, Kunio; Sagae, Masahiko

doi:10.5023/jappstat.19.19

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…In this study, the log-normal distribution was considered for deriving the inter-station correlation coefficients for cases A-C. Especially the bivariate mixed log-normal distribution defined by Shimizu and Sagae (1990) and Shimizu (1993) was adopted for the bivariate analysis in this study. Detailed procedure of applying this bivariate distribution function can also be found in Ha and Yoo (2006).…”

Section: Derivation Of Spatial Correlation Functionsmentioning

confidence: 99%

Effect of zero measurements on the spatial correlation structure of rainfall

Yoo

2006

Stoch Environ Res Ris Assess

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In this study, the effect of zero measurements on the spatial correlation function of rainfall is analyzed for the quantification of a rainfall field. The use of a bivariate mixed distribution function made it possible to analyze and compare the spatial correlation functions for these three different data sets: only the positive measurements at both gauge locations, positive measurements at either one or both gauge locations, and all measurements including zero at both locations. As an example, the spatial correlation functions are derived for the Geum River Basin, Korea and evaluated for the wet and dry seasons, respectively. Results show that the effect of zero measurements on spatial correlation structures is significant during the wet season, when the interstation correlations were estimated significantly lower than those during the dry season. It was also found that only the case considering positive measurements are valid for the quantification of rainfall field. Even during the wet season, the inter-station correlation coefficients derived by considering the zero measurements show their high variability along with many abnormally looking high estimates, which made the quantification of the spatial correlation function become very ambiguous.

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Section: Derivation Of Spatial Correlation Functionsmentioning

confidence: 99%

Effect of zero measurements on the spatial correlation structure of rainfall

Yoo

2006

Stoch Environ Res Ris Assess

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“…In this study, the following modelling based on the log-normal distribution is adopted. The resulting distribution, first defined by Shimizu and Sagae (1990), is called a bivariate mixed log-normal distribution, which is denoted by…”

Section: Case Of Mixed Bivariate Log-normal Distributionmentioning

confidence: 99%

Use of mixed bivariate distributions for deriving inter‐station correlation coefficients of rain rate

Yoo

2007

Hydrological Processes

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Abstract:Even though rain rate is notorious for its spatial and temporal intermittency, its effect on the second-order statistics of rain rate, especially the inter-station correlation coefficients, has not been intensively evaluated before. This study has derived and compared the inter-station correlation coefficient of rain rate for three cases of data: (1) only the positive measurements at both locations; (2) the positive measurements at either one or both locations; (3) all the measurements including zero measurement at both locations. For these three cases, the inter-station correlation coefficients are analytically derived by applying the mixed bivariate log-normal distribution. As an application example, the model parameters are estimated using the rain rate data collected at the Geum River basin, Korea, and the resulting inter-station correlation coefficients are evaluated and compared with those estimated by applying the Gaussian distribution. We could find that highly biased inter-station correlation coefficients are unavoidable when simply estimating them under the assumption of Gaussian distribution, or even when using the log-transformed rain rate data.

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Consideration of rainfall intermittency and log-normality on the merging of radar and the rain gauge rain rate

Yoo

2020

Journal of Hydrology

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Modelling Bivriate Data Containing Zeroes, with an Analysis of Daily Rainfall Data

Cited by 6 publications

References 12 publications

Effect of zero measurements on the spatial correlation structure of rainfall

Effect of zero measurements on the spatial correlation structure of rainfall

Use of mixed bivariate distributions for deriving inter‐station correlation coefficients of rain rate

Consideration of rainfall intermittency and log-normality on the merging of radar and the rain gauge rain rate

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