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

Abstract

AbstractThis study proposes a new method of estimating snow depth by using a moment ($${M}_{n}$$ M n ) of snow particle size distribution ($$SPSD$$ SPSD ). We assumed that estimated snow depth ($$ESD$$ ESD ) is given by a simple relationship: $$ESD$$ ESD (cm) = $$A$$ A ×$${M}_{n}$$ M n , where the parameters, $$A$$ A and $$n$$ 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 $${M}_{n}$$ M n from $$SPSD$$ 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 $$OSD$$ OSD and $$\overline{ESD}$$ ESD ¯ (obtained by using average values of $$A$$ A and $$n$$ n ) was 0.81, indicating a fairly good correlation between them. This indicates that $$\overline{ESD}$$ ESD ¯ does appear to have potential for estimating operationally, timely information on snow depth. This study suggests that $$SPSD$$ 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.