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

Abstract

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.

Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments

“…For this OSSE work, the one-dimensional ensemble Kalman filter method (Reichle et al, 2002) is used to assimilate synthetic SWE observations into Noah-MP. The ensemble Kalman filter method allows us to flexibly characterize the model errors and to effectively handle non-linear dynamics and temporal discontinuities of observations (Kumar et al, 2015;Kwon et al, 2021;Lahmers et al, 2022;Cho et al, 2022a). The ensemble Kalman filter method includes forecast and update steps.…”

Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter

“…Most snow DA research, with a few exceptions (e.g. Magnusson et al, 2014;De Lannoy et al, 2012;Cho et al, 2023), has focused on purely temporal DA where the snow in each model grid cell (or more generally spatial unit) is simulated and updated independently of its neighboring cells and the observations therein. However, De Lannoy et al ( 2022) recommend a greater adoption of spatio-temporal multivariate DA.…”