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Bayesian analysis of adjustment function for wind-induced loss of precipitation
Journal of the Korean Data & Information Science Society 2017;28:483-92
Published online May 31, 2017
© 2017 Korean Data & Information Science Society.

Yeongwoo Park1 · Young Min Kim2 · Yongku Kim3

123Department of Statistics, Kyungpook National University
Correspondence to: Yongku Kim
Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail:
Received March 2, 2017; Revised May 9, 2017; Accepted May 13, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Precipitation is one of key components in hydrological modeling and water balance studies. A comprehensive, optimized and sustainable water balance monitoring requires the availability of accurate precipitation data. The amount of precipitation measured in a gauge is less than the actual precipitation reaching the ground. The objective of this study is to determine the wind-induced under-catch of solid precipitation and develop a continuous adjustment function for measurements of all types of winter precipitation (from rain to dry snow), which can be used for operational measurements based on data available at standard automatic weather stations. This study provides Bayesian analysis for the systematic structure of catch ratio in precipitation measurement.
Keywords : Adjust function, catch ratio, precipitation, wind-induced loss.