Spatial Representativeness Analysis for Snow Depth Measurements of Meteorological Stations in Northeast China

Abstract

AbstractTriple collocation (TC) is a popular technique for determining the data quality of three products that estimate the same geophysical variable using mutually independent methods. When TC is applied to a triplet of one point-scale in situ and two coarse-scale datasets that have the similar spatial resolution, the TC-derived performance metric for the point-scale dataset can be used to assess its spatial representativeness. In this study, the spatial representativeness of in situ snow depth measurements from the meteorological stations in northeast China was assessed using an unbiased correlation metric estimated with TC. Stations are considered representative if ; that is, in situ measurements explain no less than 50% of the variations in the “ground truth” of the snow depth averaged at the coarse scale (0.25°). The results confirmed that TC can be used to reliably exploit existing sparse snow depth networks. The main findings are as follows. 1) Among all the 98 stations in the study region, 86 stations have valid values, of which 57 stations are representative for the entire snow season (October–December, January–April). 2) Seasonal variations in are large: 63 stations are representative during the snow accumulation period (December–February), whereas only 25 stations are representative during the snow ablation period (October–November, March–April). 3) The is positively correlated with mean snow depth, which largely determines the global decreasing trend in from north to south. After removing this trend, residuals in can be explained by heterogeneity features concerning elevation and conditional probability of snow presence near the stations.