Primary Modes of Northern Hemisphere Snowfall Particle Size Distributions

Abstract A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying principal component analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three empirical orthogonal functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2), and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability [i.e., principal component (PC) groups] with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low- and high-intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles and deep, stratiform snowfall events with large aggregates, respectively. Significance Statement This research enhances our understanding of varying snow particle size distributions in the Northern Hemisphere, offering valuable new insights for improving future remote sensing–based snowfall retrieval algorithms. Using a statistical technique called principal component analysis, we found that 95% of the variability in observed snowfall could be explained by three primary features: how intense the snowfall is, how dense the particles are, and the depth of the storm. We identified six unique snowfall groups, each with its own set of traits, such as the snow being light and fluffy, or heavy and packed. By linking these traits to external environmental observations, we can better understand the driving physical mechanisms within each group.