Evaluating the Utility of Active Microwave Observations as a Snow Mission Concept Using Observing System Simulation Experiments
Abstract. As a future satellite mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) with advantages including finer spatial resolution and improved capabilities in deeper snowpack and forest-covered areas as compared to existing missions (e.g., passive microwave sensors). In mountainous regions, however, the potential utility of spaceborne active microwave sensors for SWE retrievals particularly under deep snow and forest cover has not been evaluated yet. In this study, we develop an observing system simulation experiment (OSSE) that includes the characterization of expected error levels of the active microwave-based volume-scattering SWE retrievals and realistic orbital configurations over a western Colorado domain. We found that active microwave sensors can improve a root mean square error (RMSE) of SWE by about 20 % in the mountainous environment if the active microwave signals with a mature retrieval algorithm can estimate SWE up to 600 mm of deep SWE and up to 40 % of tree cover fraction (TCF). Results also demonstrated that the potential SWE retrievals have larger improvements in tundra (43 %) snow class, followed by boreal forest (22 %) and montane forest (17 %). Even though active microwave sensors are known to be limited by liquid water in the snowpack, they still reduced errors by up to 6–16 % of domain-average SWE in the melting period, suggesting that the SWE retrievals can add value to meltwater estimations and hydrological applications. Overall, this work provides a quantitative benchmark of the utility of a potential snow mission concept in a mountainous domain, helping prioritize future algorithm development and field validation activities.
Eunsang Cho et al.
Status: final response (author comments only)
- RC1: 'Comment on tc-2022-220', Melody Sandells, 22 Dec 2022
- RC2: 'Comment on tc-2022-220', Anonymous Referee #2, 12 Jan 2023
Eunsang Cho et al.
Eunsang Cho et al.
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This paper demonstrates (mostly) an improvement in simulated SWE from assimilation of synthetic observations derived from an active microwave sensor through an OSSE. The analysis is based on the difference between a calibrated nature run and uncalibrated open loop run, with an assessment of the effects of SWE retrieval and forest cover fraction limits for a fixed satellite orbit and error budget configuration (with extended error budget in the supplementary material). The analysis is performed over a region in Colorado over a range of elevations, vegetation densities and snow types. The paper is very well written and easy to follow, with some excellent visual representations of the results. As the authors highlight, the results depend on the assimilation system used and this complements the Garnaud et al. (2019) study well. The results are useful to demonstrate the necessary retrieval performances for the design of retrieval algorithms and as such is worthwhile for publication.
It would be great to elaborate on these discussion points as part of this paper:
‘We found that active microwave sensors can improve a root mean 20 square error (RMSE) of SWE by about 20% in the mountainous environment if the active microwave signals with a mature retrieval algorithm can estimate SWE up to 600 mm of deep SWE and up to 40% of tree cover fraction (TCF).’ What would be really good for this paper is a small discussion of to what extent the retrieval algorithms can actually fulfil the different criteria e.g. in Figure 10, it might be possible to colour-code the boxes behind the circles according to whether current retrieval methods can achieve this or not. This will then provide impetus to improve retrieval methods for other conditions.
Following on from this point, on line 100-101: ‘to set priorities related algorithm developments.’ Also line 361: ‘thus different priorities for the algorithm development may be required by seasonal snow characteristics’. It would be really useful to have some concrete priorities identified from this study or at least a discussion of the factors. Fig 10 shows relative level of improvements but some may be out of reach so it’s better to target the middle range improvements, or concentrate on montane forest because this is the largest % cover – at least for this site, but what about from a global perspective? The largest improvements are for tundra high forest fraction limits, but what does this mean in reality? How much of the tundra has dense forests according to the updated Sturm classification?
Figure 3 seems to suggest there is more to be gained from improving the physics of the model than in developing assimilation techniques. Is this fair to say? For example, the OL in the mid elevation ranges overestimates melt by a lot in March. Why is the assimilation unable to recover this lost mass if the microwave data has SWE information? Do the OL and NR agree on the timing of the melt? If not, would you get better performance by detecting melt from the NR and forcing the DA model to be cold / use the observations when the NR is not melting but OL is.
Figure 4 – Is it fair to say there’s no point in assimilating over bare ground? There seems to be negligible difference from the OL - why is this, especially as there are no SWE limits here?
Line 297 / Fig 3. Why does the assimilation degrade the performance for shallow SWE?
In terms of specific comments for the paper, it would be good to address the following:
Finally, I’d like to commend the authors for Figs 2, 5 and 9 – these are informative / novel ways of representing the concepts and results (at least to me).