Snow Depth Estimation by using its Drop Size Moment in South Korea Regions

Abstract

This study proposes a new method of estimating snow depth by using a moment (\({M}_{n}\)) of snow particle size distribution (\(SPSD\)). We assumed that estimated snow depth (\(ESD\)) is given by a simple relationship: \(ESD\) (cm) = \(A\)×\({M}_{n}\), where the parameters, \(A\) and \(n\) are a proportional coefficient and an exponent in the moment formula, respectively. They were determined by a regression analysis between the observed snow depths (OSD) by laser snow depth meter, and the values of \({M}_{n}\) from \(SPSD\) observed by Parsivel, installed at three observation sites: Cloud and Physics Observation Site (CPOS), Yongpyeong (YP) and Mokpo (MP) in South Korea. Snow observations were made from November to April: CPOS (2012 to 2015), YP (2015 to 2017) and MP (2005 to 2015). The analysis results indicate that the optimized value of A ranges from 2.16 × 10^–5 to 2.28 × 10^–5, and the optimized range of n is 2.21 to 2.68. The average values of A and n are 2.47 × 10^–5 and 2.21, respectively. The coefficient of determination (R²) between \(OSD\) and \(\overline{ESD}\)(obtained by using average values of \(A\) and \(n\)) was 0.81, indicating a fairly good correlation between them. This indicates that \(\overline{ESD}\) does appear to have potential for estimating operationally, timely information on snow depth. This study suggests that \(SPSD\) observed by disdrometer (Parsivel or 2DVD) can be also used as an alternative of the typical snow measuring instruments such as snow stake and ultra-sonic snow depth meter.