This study works to improve snow state initialization and prediction within the NWM by taking advantage of observations from the New York State Mesonet (NYSM). To achieve this outcome, we are working to: 1) optimize the snow physics parameterization suite in the NWM to improve streamflow prediction in New York State and assess snow-state uncertainty as related to snow parameterizations, and 2) implement and demonstrate snow data assimilation of in situ snow depth and snow water equivalent observations through the ensemble Kalman filtering framework within NCAR’s Data Assimilation and Research Testbed. We leverage unique high-quality automated snow and meteorological observations from the NYSM that include stations located in the Adirondack Mountains, Catskill Mountains, and Tug Hill Plateau regions. These are supplemented with manual snow course observations, used to ground-truth the NYSM automated observations and characterize spatial variability and station representativeness. Initial results will be presented using data from the 2019-2020 snow season to better constrain uncertainty in snow physics and demonstrate the impact of assimilating in situ snow observations into the NWM.