Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?. Issue 23 (5th December 2019)

• Record Type: Journal Article
• Authors: He, Cenlin; Chen, Fei; Barlage, Michael; Liu, Changhai; Newman, Andrew; Tang, Wenfu; Ikeda, Kyoko; Rasmussen, Roy
• Is Part Of: Journal of geophysical research. Volume 124:Issue 23(2019)
• Page Start: 12631
• Page End: 12654
• Publication Date: 2019-12-05
• Subjects: precipitation -- convection‐permitting -- snowpack simulation -- snow depth -- snow cover fraction -- snow water equivalent; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2019JD030823
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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. Key Points: Convection‐permitting WRF results capture the observed precipitation, SWE, and snow depth in western and northern portions of WUS mountains All datasets produce large negative bias in precipitation, SWE, and snow depth in interior WUS mountains with fair performance for WRF Convection‐permitting modeling has added values in providing precipitation for snow simulations over mountains without enough observations