10 Oct 2022
 | 10 Oct 2022
Status: this preprint is currently under review for the journal HESS.

Assimilation of airborne gamma observations provides utility for snow estimation in forested environments

Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich

Abstract. An airborne gamma-ray remote sensing technique provides a strong potential to estimate reliable snow water equivalent (SWE) in forested environments where typical remote sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model to improve SWE estimates in forested areas in the northeastern U.S. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah land surface model with multiple parameterization options (Noah-MP) via assimilation despite the limited number of the measurements. Improvements are witnessed during the snow accumulation period while reduced skills are seen during the snow melting period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective in reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential of extending the impact of flight line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32-km distances from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.

Eunsang Cho et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-332', Anonymous Referee #1, 02 Dec 2022
  • RC2: 'Comment on hess-2022-332', Anonymous Referee #2, 15 Dec 2022

Eunsang Cho et al.

Eunsang Cho et al.


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Short summary
An airborne gamma-ray remote sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote sensing techniques (e.g. passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern U.S. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.