Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016: 2. The Impact of Assimilating Satellite‐Based Snow Cover and Freeze/Thaw Observations Into a Land Surface Model. Issue 7 (4th April 2022)

• Record Type: Journal Article
• Authors: Xue, Yuan; Houser, Paul R.; Maggioni, Viviana; Mei, Yiwen; Kumar, Sujay V.; Yoon, Yeosang
• Is Part Of: Journal of geophysical research. Volume 127:Issue 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-04
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD035992
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: This second paper of the two‐part series focuses on demonstrating the impact of assimilating satellite‐based snow cover and freeze/thaw observations into the hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point‐scale, basin‐scale, and domain‐scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point‐scale assessment is mainly conducted via evaluating against ground‐based measurements. In general, the assimilation enabled estimates are better than no‐assimilation counterparts. Second, the basin‐scale runoff assessment demonstrates that across three snow‐dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness‐of‐fit statistics through mitigating the negative effects brought by the fixed long‐term precipitation correction factors. For example, when forced by the bias‐corrected precipitation, the assimilation‐enabled experiment improves the bias by 69%, the root‐mean‐squared error by 30%, and the unbiased root‐mean‐squared error by 18% (relative to the no‐assimilation counterpart). Finally, the domain‐scale assessment is conducted via evaluating against satellite‐based SWE and skin temperature products. Both sets of domain‐scale analysis further corroborate the findings in the point‐scale evaluations. Overall, this study suggests the benefits of the proposed multi‐variate assimilation system in improving the cryospheric‐hydrological process within a land surface model for use in HMA. Key Points: The skills of the High Mountain Asia‐Land Data Assimilation System (version 1) with and without multi‐variate assimilation are presented The assimilation enabled experiments generally perform better than the no‐assimilation counterparts The assimilation enabled experiments mitigate the negative effects arising from the fixed long‐term precipitation correction factors