Effect of zero measurements on the spatial correlation structure of rainfall

Yoo, Chulsang; Ha, Eunho

doi:10.1007/s00477-006-0064-3

Cited by 26 publications

(26 citation statements)

References 14 publications

(19 reference statements)

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“…Usually, this property is studied by considering gauge stations pair-wise and estimating Pearson's product moment correlation coefficient q of recorded series. This approach is followed in some recent works by Habib et al (2001), Ciach and Krajewski (2006), Yoo and Ha (2007) and Ha and Yoo (2007), among others. These authors tackle the problem of correctly quantifying the dependence between contemporaneous pairs of rainfall observations, taking into account the intermittent nature of the rain and the skewness of the data.…”

Section: Introductionmentioning

confidence: 91%

“…Denoting (X, Y) a random vector of the values collected at two rain gauge stations, Habib et al (2001), Yoo and Ha (2007) and Ha and Yoo (2007) used a bivariate mixed distribution function proposed by Shimizu (1993) to analyse the four different kinds of possible pairs, namely: (i) no rain at both stations (X = 0, Y = 0), (ii-iii) positive value at one station and null value at the other (X [ 0 and Y = 0) and vice versa (X = 0 and Y [ 0), (iv) positive values at both stations (X [ 0 and Y [ 0).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields

Serinaldi

2007

Stoch Environ Res Risk Assess

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The spatial dependence of rainfall is usually studied considering pairs of rain gauges and analysing the behaviour of Pearson's correlation coefficient with respect to the distance between the two devices. However, this measure of linear dependence involves the underlying hypothesis that the data follow a bivariate normal distribution. When the data are skewed, as in the case of rainfall, a preliminary transformation is often performed in an attempt to obtain the required bivariate normality. This approach does not always guarantee satisfactory results, resulting in correlation estimator that exhibits bias and high variance. In this work, the inter-gauge dependence is studied by applying the alternative non-parametric Kendall's rank correlation coefficient and the upper tail dependence coefficient. Exploiting the link between these two indices with copulas allows building an exploratory graphical method useful to drive the definition of a regional 2-copula suitable for the description of the data. Then, this copula is used to generalize the Shimizu's bivariate mixed model, providing some freedom in the choice of the dependence structure and marginals. The analysis is performed on seven years of rainfall observations with 30-min time resolution collected by 35 gauges located in Central Italy. The effect of zero-measurements and several aggregation time scales are considered as well. Results of this study support some conclusions previously appeared in literature; mainly that only positive contemporaneous pairs of rainfall observations correctly describe the inter-site dependence.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields

Serinaldi

2007

Stoch Environ Res Risk Assess

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“…For fields like reflectivity or precipitation this is a problem, because the spatial structure of the field is highly sensitive to the inclusion of the zero regions. Yoo and Ha (2007) specifically examine the effects of zero measurements on the correlation structure of rainfall. In the current study, both analyses are performed-with and without the inclusion of the zero regions-because they capture different facets of the quality of the forecasts.…”

Section: Introductionmentioning

confidence: 99%

Verification with Variograms

Marzban

Sandgathe

2009

Weather and Forecasting

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The verification of a gridded forecast field, for example, one produced by numerical weather prediction (NWP) models, cannot be performed on a gridpoint-by-gridpoint basis; that type of approach would ignore the spatial structures present in both forecast and observation fields, leading to misinformative or noninformative verification results. A variety of methods have been proposed to acknowledge the spatial structure of the fields. Here, a method is examined that compares the two fields in terms of their variograms. Two types of variograms are examined: one examines correlation on different spatial scales and is a measure of texture; the other type of variogram is additionally sensitive to the size and location of objects in a field and can assess size and location errors. Using these variograms, the forecasts of three NWP model formulations are compared with observations/analysis, on a dataset consisting of 30 days in spring 2005. It is found that within statistical uncertainty the three formulations are comparable with one another in terms of forecasting the spatial structure of observed reflectivity fields. None, however, produce the observed structure across all scales, and all tend to overforecast the spatial extent and also forecast a smoother precipitation (reflectivity) field. A finer comparison suggests that the University of Oklahoma 2-km resolution Advanced Research Weather Research and Forecasting (WRF-ARW) model and the National Center for Atmospheric Research (NCAR) 4-km resolution WRF-ARW slightly outperform the 4.5-km WRF-Nonhydrostatic Mesoscale Model (NMM), developed by the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction (NOAA/NCEP), in terms of producing forecasts whose spatial structures are closer to that of the observed field.

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“…Applications of the averaged CCs are substantially different from those of previous studies. A number of previous studies (Ciach and Krajewski 2006;Yoo and Ha 2007) adopted the last CC values using the total number of rainfall data sets to generate the spatial correlation coefficient diagram. The measured rainfall amount at each rain gauge was used to redistribute pre-existing rain gauges in a semi-mountainous region to enhance the resolution of spatiotemporal changes of precipitation.…”

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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Effect of zero measurements on the spatial correlation structure of rainfall

Cited by 26 publications

References 14 publications

Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields

Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields

Verification with Variograms

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

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