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Bayesian analysis of adjustment function for wind-induced loss of precipitation

바람의 영향에 의한 관측 강우 손실에 대한 베이지안 모형 분석

  • Park, Yeongwoo (Department of Statistics, Kyungpook National University) ;
  • Kim, Young Min (Department of Statistics, Kyungpook National University) ;
  • Kim, Yongku (Department of Statistics, Kyungpook National University)
  • Received : 2017.03.02
  • Accepted : 2017.05.13
  • Published : 2017.05.31

Abstract

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.

일반적으로 우량계로 측정된 강수량은 지상에 도달한 실제 강수량보다 적게 관측된다. 측정된 강수량이 실제 강수량 보다 적게 측정되는 것은 강수의 형태 (snow, fixed, rain)나 우량계의 종류 그리고 공간적인 특성에 의해 강수량의 정확한 측정이 어렵기 때문이다 (Nitu, 2013). 이는 강수량의 손실을 발생시키는 계통오차 (systematic errors) 때문이며, 일반적으로 고체 강수량의 계통오차는 보통 액체 강수량보다 크다고 알려져 있다. 본 연구에서는 바람에 의한 고체 강수량의 언더캐치(under-catch)를 알아보고, 겨울에 내리는 모든 강수의 형태 (snow, mixed, rain)에 대하여 연속조정함수를 소개하였다. 이를 위해 고창 표준기상관측소에서 측정된 데이터를 사용하였고, 객관적으로 데이터를 가장 잘 설명하는 모형을 선택하고 평가하기 위해 베이지안 분석을 이용할 것이다. 이번 연구는 강수량 측정에서 Catch Radio의 계통적 구조에 대한 통계적 분석을 보여주었다.

Keywords

References

  1. Chib, S. and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The American Statistician, 49, 327-335.
  2. Gelman, A., Carlin, B. P., Stern, H. S., Dunson, D. B., Vehtari, A. and Rubin, D. B. (2013). Bayesian data analysis, 3rd ed., CRC Press.
  3. Goodison, B. E., Louie, P. Y. T. and Yang, D. (1998). WMO solid precipitation measurement intercomparison. WMO Instruments and Observing Methods Rep.67, WMO/TD-No.872, 212.
  4. Kim, Y. and Kim, H. J. (2012). Stochastic precipitation modeling based on Korean historical data. Journal of the Korean Data & Information Science Society, 23, 1309-1317. https://doi.org/10.7465/jkdi.2012.23.6.1309
  5. Lee, J. E. (2014) Evaluation of the accuracy of solid precipitation measurements, Thesis, Kyungpook National University, Daegu.
  6. Lee, J. J. and Kim, Y. (2016). A spatial analysis of Neyman-Scott rectangular pulse model. Journal of the Korean Data & Information Science Society, 27, 1119-1131. https://doi.org/10.7465/jkdi.2016.27.5.1119
  7. Nitu, R. (2013). Cold as SPICE, Meteorological Technology International, 148-150.
  8. Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Theriault, J., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Cristanelli, S., and Gutmann, E. (2012). How well are we measuring snow?. Bulletin of the American Meteorological Society, 93, 811-829. https://doi.org/10.1175/BAMS-D-11-00052.1
  9. Strangeways, I. (2004). Improving precipitation measurement. International Journal of Climatology, 24, 1443-1460. https://doi.org/10.1002/joc.1075
  10. Theriault, J. M., Rasmussen, R., Ikeda, K. and Landolt, S. (2012). Dependence of snow gauge collection efficiency on snowflake characteristics. Journal of Meteorology and Climatology, 51, 745-762. https://doi.org/10.1175/JAMC-D-11-0116.1
  11. WMO (2008). Guide to meteorological instruments and methods of observation, 7th edition, WMO No. 8, Geneva.
  12. Wolff, M.A., Isaksen, K., Petersen-Overleir, A., Odemark, K., Reitan, T. and Braekkan, R. (2015). Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation, results of a Norwegian field study. Hydrology and Earth System Sciences, 19, 951-967. https://doi.org/10.5194/hess-19-951-2015