the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilation of airborne gamma observations provides utility for snow estimation in forested environments
Eunsang Cho
Sujay V. Kumar
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.
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Eunsang Cho et al.
Status: closed
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RC1: 'Comment on hess-2022-332', Anonymous Referee #1, 02 Dec 2022
- AC1: 'Reply on RC1', Eunsang Cho, 13 Apr 2023
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RC2: 'Comment on hess-2022-332', Anonymous Referee #2, 15 Dec 2022
Review of " Assimilation of airborne gamma observations provides utility for snow estimation in forested environments" by Cho et al.
SUMMARY:
Overall, I think this paper is relevant for publication in HESS with a clear presentation. This paper leverages airborne gamma observations to estimate snow water equivalent using data assimilation. This is great work for the snow community that shows the potential of remote-sensed gamma observations to improve snow estimates. That said, clarification of how localization is implemented with DA would be helpful for others to repeat your work and interpolate the results.
Line-by-line comments:
- L125: For Equations (1) to (4), make sure to use the same format for the uncollided gamma count rates (e.g., 40Kb 40Kb).
- L138: From my understanding, COOP snow depth is first converted to SWE which is assimilated (rather than snow depth) to get UA SWE. Please make sure this sentence won’t cause any confusion.
- L186: For MERRA2, are bias-corrected precipitation or uncorrected precipitation used as inputs? In line 308, the authors mention that overestimated SWE is likely attributed to precipitation phase partition. Would bias from precipitation contribute to the overestimation?
- L196: Could you briefly summarize how Kwon et al. (2021) perturbs the forcings? For example, which forcings are perturbed? How are the parameters chosen? (i.e., Were in situ observations used to quantify these parameters?) This would be helpful to know if precipitation uncertainties are considered.
- L215: It is not clear to me how localization is applied in the assimilation. Does it weigh the covariance matrix? It might be better to link equation (7) with the relevant equation mentioned above. I might not fully understand it, but why localization used to update SWE estimates would impact the open loop results shown in figure 6? I assume localization would only impact DA SWE.
- L226: maybe use lowercase “A” in the parenthesis.
- L236: it seems peak SWE might not be correctly estimated if only one data point exists after the accumulation season (Figure 4 SJ150 in WY 1989 and NH106 in WY 1997). It might be worth pointing that out and/or discussing this issue.
- Please be consistent with either RMSD or RMSE throughout the manuscript.
Citation: https://doi.org/10.5194/hess-2022-332-RC2 - AC2: 'Reply on RC2', Eunsang Cho, 13 Apr 2023
Status: closed
-
RC1: 'Comment on hess-2022-332', Anonymous Referee #1, 02 Dec 2022
- AC1: 'Reply on RC1', Eunsang Cho, 13 Apr 2023
-
RC2: 'Comment on hess-2022-332', Anonymous Referee #2, 15 Dec 2022
Review of " Assimilation of airborne gamma observations provides utility for snow estimation in forested environments" by Cho et al.
SUMMARY:
Overall, I think this paper is relevant for publication in HESS with a clear presentation. This paper leverages airborne gamma observations to estimate snow water equivalent using data assimilation. This is great work for the snow community that shows the potential of remote-sensed gamma observations to improve snow estimates. That said, clarification of how localization is implemented with DA would be helpful for others to repeat your work and interpolate the results.
Line-by-line comments:
- L125: For Equations (1) to (4), make sure to use the same format for the uncollided gamma count rates (e.g., 40Kb 40Kb).
- L138: From my understanding, COOP snow depth is first converted to SWE which is assimilated (rather than snow depth) to get UA SWE. Please make sure this sentence won’t cause any confusion.
- L186: For MERRA2, are bias-corrected precipitation or uncorrected precipitation used as inputs? In line 308, the authors mention that overestimated SWE is likely attributed to precipitation phase partition. Would bias from precipitation contribute to the overestimation?
- L196: Could you briefly summarize how Kwon et al. (2021) perturbs the forcings? For example, which forcings are perturbed? How are the parameters chosen? (i.e., Were in situ observations used to quantify these parameters?) This would be helpful to know if precipitation uncertainties are considered.
- L215: It is not clear to me how localization is applied in the assimilation. Does it weigh the covariance matrix? It might be better to link equation (7) with the relevant equation mentioned above. I might not fully understand it, but why localization used to update SWE estimates would impact the open loop results shown in figure 6? I assume localization would only impact DA SWE.
- L226: maybe use lowercase “A” in the parenthesis.
- L236: it seems peak SWE might not be correctly estimated if only one data point exists after the accumulation season (Figure 4 SJ150 in WY 1989 and NH106 in WY 1997). It might be worth pointing that out and/or discussing this issue.
- Please be consistent with either RMSD or RMSE throughout the manuscript.
Citation: https://doi.org/10.5194/hess-2022-332-RC2 - AC2: 'Reply on RC2', Eunsang Cho, 13 Apr 2023
Eunsang Cho et al.
Eunsang Cho et al.
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