Can convection-permitting modeling provide decent precipitation for offline high-resolution snowpack simulations over mountains?

Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high-resolution (4-km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi-parameterization (Noah-MP) land surface model driven by precipitation forcing from convection-permitting (4-km) Weather Research and Forecasting (WRF) modeling and four widely used high-resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best-performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah-MP snowpack physics. This study highlights that convection-permitting modeling with proper configurations can have added values in providing decent precipitation for high-resolution snowpack simulations over the WUS mountains in a typical ENSO-neutral year, particularly over observation-scarce regions.