Understanding what happens to hydrometeors, such as atmospheric snow particles (ice crystals, snow crystals, and snowflakes) in clouds is crucial for improving meteorolog-ical forecast and climate models. Consequently, improved predictions of the precipitation amount reaching the ground (snowfall) require accurate knowledge of the microphysical properties of ice crystals, such as their size, cross-sectional area, shape, fall speed, and mass. In particular, the shape is an important parameter. It strongly influences the scattering properties of these ice particles. Snowfall has long been monitored by ground-based instruments, but instruments that can simultaneously measure all microphysical properties are still scarce. Accurate knowledge of microphysical properties is essential to achieve more realistic parameterizations in atmospheric models. Also, this knowledge is required for increasing accuracy of different remote sensing applications such as cloud and precipitation retrievals from passive and active measurements from satellites. Questions of particular interest are whether microphysical properties of precipitating snow particles show notably different characteristics depending on location, for instance at high-latitudes and what parame-terizations best describe these microphysical properties. How particle shape affects other properties, such as fall speed and mass, is also important. The particle shape is an important parameter, not only for the investigation of growth processes but also because of its importance for optical remote sensing retrievals of cloud properties and snow albedo. Therefore, studying snow microphysical properties and how they depend on particle shape is crucial to ensure accurate cloud parameterizations in climate and forecast models, and to the understanding of precipitation in cold climates.In this thesis ground-based in-situ measurements carried out in Kiruna, Sweden, are presented. Natural snow, ice crystals, and other hydrometeors covering particle sizes from 0.05 to 4 mm have been classified. Measurements have been taken during the snow-fall season from the beginning of November to the middle of May from 2014 to 2019. A ground-based in-situ instrument, Dual Ice Crystal Imager (D-ICI), which takes high-resolution side- and top-view images of hydrometeors was used. Particle size (maximum dimension), cross-sectional area, area ratio, aspect ratio, fall speed and mass of individual particles have been determined. A novel shape classification, where each particle shape is sorted into different shape groups, has been proposed, comprising a total of 135 unique shapes, including 34 new snow crystal shapes found in Kiruna. The main contributions of this thesis will enhance the improvement in the under-standing of precipitation in a cold climate. An updated snow crystal shape classification is presented and a different shape classification method is proposed. The new snow mea-surements and parameterizations studied in this work for different snow crystal shapes will be useful for climate and forecast models. These parameterizations include rela-tionships between particle size, cross-sectional area, fall speed and mass as a function of shape. The measured data shows a wide spread; however, binning the data according to size or cross-sectional area has improved correlations leading to more reliable parameteri-zations of fall speed versus size or cross-sectional area. Vertically orientated particles fall faster on average, but most particles for which orientation can be defined fall horizontally. The particle mass has been determined from measured particle size, cross-sectional area, and fall speed. When binning the data, the fall speed vs mass, mass vs particle size, and mass vs cross-sectional area relationships also show a high correlation. The relationships presented in this thesis have been compared with the results shown in previous studies.