Cold-Season Precipitation Sensitivity to Microphysical Parameterizations: Hydrologic Evaluations Leveraging Snow Lidar Datasets

• Published in: Journal of hydrometeorology Vol. 25; no. 1; pp. 143 - 160
• Main Authors: Rudisill, W. J., Flores, A. N., Marshall, H. P., Siirila-Woodburn, E., Feldman, D. R., Rhoades, A. M., Xu, Z., Morales, A.
• Format: Journal Article
• Language: English
• Published: United States American Meteorological Society 01.01.2024
• Subjects: ENVIRONMENTAL SCIENCES; GEOSCIENCES
• ISSN: 1525-755X; 1525-7541
• DOI: 10.1175/JHM-D-22-0217.1

Abstract Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) Model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750-km 2 East River watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ±3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15% ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8 and 17 cm, and percent biases of 3%–15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization but conclude that snow lidar is a valuable nontraditional indicator of model performance.