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48 results on '"Kim, Gwangseob"'

1. Bayesian Estimation of Neyman–Scott Rectangular Pulse Model Parameters in Comparison with Other Parameter Estimation Methods.

Author

Nizeyimana, Pacifique, Lee, Kyeong Eun, and Kim, Gwangseob

Subjects
GIBBS sampling, MOMENTS method (Statistics), PARAMETER estimation, STOCHASTIC models
Abstract

Neyman–Scott rectangular pulse is a stochastic rainfall model with five parameters. The impacts of initial values and optimization methods on the parameter estimation of the Neyman–Scott rectangular pulse model were investigated using both the method of moments and the method of maximum likelihood. The estimates using the method of moments were influenced by the optimization method and were sensitive to the initial values and the aggregation scale of the data. Thus, by using frequentist estimation methods, we cannot guarantee the unique values as estimates. The aim of this study is to find more reliable unique values as estimates using a Bayesian approach. In this approach, parameters are estimated from the posterior distribution, and model performance is assessed by comparing observed values with fitted values. Slice sampling within the Gibbs sampler algorithm demonstrates superior convergence and model fitting, yielding unique estimates for the model parameters. The main conclusion of this study is that Bayesian estimation methods outperform other estimation methods in terms of providing reliable and stable estimates that improve rainfall generation accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Correlation Analysis of Climate Indices and Precipitation Using Wavelet Image Processing Approach

Author

Sun, Mingdong, Li, Xuyong, Kim, Gwangseob, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Hui, Lin, editor

Published
2018
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21. Spatio-temporal Dynamics of a Soil Moisture Field: Sampling Error Analysis with Simulation Study

Author

Kim, Gwangseob

Subjects
Soil moisture estimation,sampling error,remote sensing,the WGR model,the Turning Bands Method
Abstract

In this study, effects of intermittent visit of observation satellite, partial coverage of remote sensing, heterogeneity of soil properties and precipitation on soil moisture estimations were investigated to develop a sampling strategy. In the soil moisture sampling error analysis, a modified form of theoretical soil moisture model proposed by [1], the WGR model proposed by [2] for use of generating rainfall and the Turning Bands Method for use of generating two dimensional random fields were employed. The evaluation of study results indicates that the sampling error is mainly dominated by sampling interval. The effect of heterogeneity of soil properties and rainfall on sampling error is considerably smaller than that of intermittent visit of observation satellite. The study results suggest that the sampling error generated by other factors such as heterogeneity of rainfall and soil properties, topography and climate conditions can be significantly reduced by increasing the sampling interval, for example, at least twice per day. The effect of partial coverage on sampling error can be ignored provided that the annual mean of coverage portion is higher than 90 %. The impact of water retention capacity of fields on the sampling error seems to be significant. More specifically, the smaller the water retention capacity of fields (i.e., a smaller soil porosity and a thinner active soil depth) the larger the sampling error.

Published
2017

48. Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data.

Author

Kim, Gwangseob and Barros, Ana P.

Subjects
*SOIL moisture potential, *HYDROMETEOROLOGY, *SOIL ecology
Abstract

Previous work showed that remotely sensed soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observations and hydrometeorological forcing (Remote Sens. Environ. 81 (2002) 1). Specifically, it was determined that this multiscaling behavior is consistent with the scaling of soil hydraulic properties and vegetation cover, while the multifractal behavior is associated with the temporal evolution of soil moisture fields. Here, we apply these findings by directly incorporating information on the spatial structure of soil texture and vegetation water content to the spatial interpolation of remotely sensed soil moisture data.A downscaling model is presented which consists of a modified fractal interpolation method based on contraction mapping. This methodology is different from other fractal interpolation schemes because it generates unique fractal surfaces. It is different from other contraction mapping models because it includes spatially and temporally varying scaling functions as opposed to single-valued scaling factors. The scaling functions are linear combinations of the spatial distributions of ancillary data. The model is demonstrated by downscaling soil moisture fields from 10 to 1 km resolution using remote-sensing data from the Southern Great Plains 1997 (SGP''97) field experiment. [Copyright &y& Elsevier]

Published
2002
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