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Global observations of snow cover and snow water equivalent are vitally important for climatological and hydrological studies, at both global and local scales. Passive microwave remote sensing techniques have been used over the past 30 years to produce global estimations of snow water equivalent through empirical calculations. However, the uncertainties surrounding the influence of snow microstructure has led to large errors in snow water equivalent estimation. This study examined the extinction properties of the natural snowpack and produced a new extinction coefficient, for use with the semi-empirical multiple layer Helsinki University of Technology (n-HUT) snow emission model. Snow slabs from the natural snowpack\ud were extracted and observed radiometrically upon bases of different reflectivities, as part of the Arctic Snow Microstructure Experiment (ASMEx). Snow parameters were characterised via traditional snowpit observation techniques, as well as with modern high resolution methods, such as with the SnowMicroPen and X-Ray Computer Tomography analysis. The ASMEx snow slab data were used with a flux coefficient model to calculate six flux absorption and scattering coefficients. The six flux scattering coefficients in the vertical polarization were used with a theoretical absorption coefficient model to create a new empirical extinction coefficient, eliminating the need to use subjective observations.\ud The new extinction coefficient was compared to the original n-HUT extinction coefficient model, through the observation and simulation of snowpack brightness temperatures, obtained as part of the Sodankyl¨a Radiometer Experiment (SoRaX). The derived extinction coefficient produced more accurate simulated brightness temperatures at vertical polarizations, especially at 36.5 GHz. The ability to include snow specific surface\ud area data within the n-HUT model has greatly increased its capability; by increasing the breadth of microstructure parameters to include objective observations of specific surface area, and by increasing the accuracy of simulations of the natural snowpack.