Kaitlyn Jesmonth:
Microphysical and Thermodynamic Characteristics of a Heavy Mixed Precipitation Band in an Eastern U.S. Winter Storm
Mesoscale snowbands within extratropical cyclones can cause major societal disruptions and are challenging to predict. Previous literature has suggested the importance of mid-level frontogenesis in primary snowband development northwest of the surface low. However, little is known about the coupled microphysical and thermodynamic processes within these primary snowbands. In this study we examine the 16–17 January 2022 winter storm, where a band of heavy snow and ice pellets organized over western New York. Microphysical observations for this case were obtained from the University of Illinois System for Characterizing and Measuring Precipitation (SCAMP), a multi-sensor suite of instruments deployed in Buffalo, NY during the NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field project. To further investigate the thermodynamic evolution of the environment, output from a Weather Research and Forecasting (WRF) model simulation with 1-km grid spacing was analyzed.
Cross-sectional analysis from the WRF simulation through banded precipitation reveals strong mid-level frontogenesis above persistent low-level sinking. This low-level sinking preceded the development of a 925–800 hPa above freezing layer, which moved northwest toward the SCAMP site in Buffalo, NY. As this above freezing layer developed, particle size and fall speed distributions from the SCAMP depicted melting snow as the predominant precipitation type. Results show the low-level sinking and above freezing layer likely contributed to snow particle melting, aggregation, and consequently, the intense snowfall rates observed during this storm. The above freezing layer became superimposed with a frontogenetical circulation, orographic gravity waves and shear-induced turbulence atop a low-level stable layer. Ongoing research is examining the relative contributions of these processes to the observed mixed precipitation band formation and evolution.
Kyuhaeng Lee:
Single Particle Instrument Simulator: Bridging Experiments and Models
Understanding aerosol mixing state is crucial for quantifying aerosol interactions with solar radiation and cloud formation. Aerosol mixing state, the distribution of chemical species across individual aerosol particles, directly impacts these interactions. For example, the absorptivity of an aerosol particle varies depending on whether absorbing species, such as black carbon, are externally or internally mixed with other species. This complexity and significance of mixing state, whether internal or external, must be incorporated into models that simulate aerosol impacts on climate. However, directly comparing model outputs with observational data remains challenging. While particle-resolved Monte Carlo aerosol simulation (PartMC) can resolve aerosol mixing state by providing the mass of individual species within aerosol particles, instruments such as mass spectrometers provide ion signals based on mass-to-charge ratios. These fundamental differences in output make direct comparison challenging. This study, SPIN-sim (single particle instrument simulator), aims to bridge this gap by converting single-particle mass spectrometer data into model-comparable outputs. Using Non-negative Matrix Factorization (NMF), the mass spectra data were decomposed to capture the fractions of individual species in the mixture, making possible conversion of instrument output into model-comparable data. Preliminary results with synthetic data show promising potential for determining individual species fractions with most particle reconstructions aligning within a 5% error. By developing this simulator, we can enable the validation of aerosol models and enhance their comparison against real-world observations.