Radar-equivalent snowpack: reducing the number of snow layers while retaining their microwave properties and bulk snow mass

Abstract

Snow water equivalent (SWE) retrieval from Ku-band radar measurements is possible with complex retrieval algorithms involving prior information on the snowpack microstructure and a microwave radiative transfer model to link backscatter measurements to snow properties. A key variable in a retrieval is the number of snow layers, with more complex layering yielding richer information but at an increased computational cost and number of unknowns. Here, we show the capabilities of a new method to simplify a complex multilayered snowpack to two to three layers while nearly preserving the microwave scattering behavior of the snowpack and conserving the bulk snow water equivalent. This method, called radar-equivalent snowpack, is based on a k-means clustering algorithm to group the snow layers based on the extinction coefficient and a weighted average using the optical thickness applied to the snow properties. We evaluated our method using snow properties from simulations of the Soil, Vegetation and Snow version 2 (SVS-2)/Crocus physical snow model at 11 sites spanning a large variety of climates across the world and the Snow Microwave Radiative Transfer model to calculate backscatter at 17.25 GHz. The layer simplification is done as an intermediate step between the physical modeling (SVS-2/Crocus) and the microwave radiative transfer (Snow Microwave Radiative Transfer Model - SMRT). Grouping and averaging snow stratigraphy into three layers effectively reproduced the total snowpack backscatter of multilayered snowpacks, with an overall root mean squared error of 0.5 dB and R2Combining double low line0.98. Using this methodology in SWE retrieval applications, this method can be used to simplify snowpacks and reduce the number of variables to optimize while maintaining similar scattering behavior without compromising the modeled snowpack properties. A reduction in the mathematical complexity of SWE retrieval cost functions and a reduction in computation of up to 80 % can be gained by using fewer layers in the SWE retrieval.