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

Ha, Eunho; Yoo, Chulsang

doi:10.1002/hyp.6526

Cited by 19 publications

(19 citation statements)

References 26 publications

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“…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%

“…Introducing the concept of bivariate mixed distribution function makes it possible to derive the spatial correlation functions analytically (Kedem et al 1990;Ha and Yoo 2006), which also makes it possible to show how the portion of zero measurements and the spatial variability of positive measurements change the spatial correlation structure of rainfall.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Effect of zero measurements on the spatial correlation structure of rainfall

Yoo

2006

Stoch Environ Res Ris Assess

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“…Yoo and Ha (2007) and Ha and Yoo (2007) have shown that zero measurements cannot be used for characterizing a rainfall field from rain gauge measurements because they decrease the spatial variability of the data and produce a high variability of the correlation between pairs of time series, with several abnormally high estimates. However, considering pairs of radar and gauge rainfall series, zero radar rainfall estimates occur especially at far ranges, when the radar returns from precipitation can be quite close to the minimum detectable signal due to range degradation or attenuation.…”

Section: Radar Datamentioning

confidence: 99%

On precipitation measurements collected by a weather radar and a rain gauge network

Sebastianelli

Russo

Napolitano

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Abstract. Many phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The present paper evaluates radar error as a function of the range, in order to correct the rainfall radar estimates. The radar is calibrated utilizing data from the rain gauges. Then, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount is calculated against the slant range. The trend of the G/R ratio shows two behaviours: a concave part due to the melting layer effect close to the radar location and an almost linear, increasing trend at greater distances. A best fitting line is used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology is verified by comparing pairs of rainfall time series that are observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor is investigated at the scale of event, both for convective and stratiform events. The main result is that there is not a univocal range error pattern, as it also depends on the characteristics of the considered event. On the other hand, the adjustment factor tends to stabilize itself for time aggregations of the order of one year or greater.

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“…They evaluated the effect of time-scale range, inter-storm variability, and rainfall intensity. Furthermore, to characterize the spatiotemporal variability of the rainfall intensity, Ha and Yoo (2007) used the inter-station correlation coefficient of the rainfall intensity derived from the mixed bivariate rainfall distribution. Based on the use of inter-gauge correlation, the purpose of this study is to assess a rain-gauge distribution to increase capability and efficiency in rainfall measurements.…”

Section: Introductionmentioning

confidence: 99%

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

Jung¹,

Kim²,

Baik³

et al. 2014

Terr. Atmos. Ocean. Sci.

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A reliable network of rain gauges is a crucial component of rainfall estimation in a watershed. To provide a better evaluation method for rain-gauge networks, a new evaluation method using average inter-gauge correlation coefficients (averaged CC) for estimating an effective radius for each rain gauge was developed. In this study, averaged CCs were obtained from the values of inter-gauge correlation coefficients after choosing a minimum number of rainfall data sets as a threshold. The Nam River Basin (2400 km 2 ) and its 24 rain gauges were selected with 8 years (2003 -2010) rainfall data to validate a new evaluation method. In the spatial correlation coefficient fitting process for generating correlation distances, averaged CCs increased fitness accuracy (maximum 37%) in terms of coefficient of determination (R 2 ) compared with a commonly used method (the last value of the inter-gauge correlation coefficient as the number of data sets is increased: last CC). In the evaluation of effective radii for 8 years, the robustness of the averaged CCs was supported by lower standard deviations for all rain gauges. For the optimum coverage of rainfall estimation in terms of effective radius, the Nam River Basin requires 20 rain gauges. Investigation of altitude effects presented that the effective radii were minimally influenced by the altitude of rain-gauge locations for this area.

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

Cited by 19 publications

References 26 publications

Effect of zero measurements on the spatial correlation structure of rainfall

Effect of zero measurements on the spatial correlation structure of rainfall

On precipitation measurements collected by a weather radar and a rain gauge network

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

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