the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Altimetric Ku-band Radar Observations of Snow on Sea Ice Simulated with SMRT
Abstract. Sea ice thickness is essential for climate studies and numerical weather prediction. Radar altimetry has provided sea ice thickness measurement since the launch of ERS-1 and currently through CryoSat-2, Sentinel-3 and Altika but uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, modelling the scattering of the electromagnetic waves with the snowpack and ice is necessary to retrieve the sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low resolution altimeter waveform echo from the snow-covered sea ice, using in-situ measurements as input. Measurement from four field campaigns were used: Cambridge Bay, Eureka Sound and near Alert, Nunavut, Canada in April 2022 in the cold and later winter condition when snow and ice thickness are neat their seasonal maxima prior to melt. In-situ measurements included snow temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to adjust sea ice roughness. The retrieved roughness (root-mean-square height) in Cambridge Bay was 2.1 mm and 1.6 mm in Eureka, which was close to the observed value of 1.4 mm for flat sea ice. In addition, simulations of backscatter in preparation for the European Space Agency's CRISTAL mission demonstrated the dominance of scattering from the snow surface at Ku and Ka-band. However, these findings depend on the parameterisation of the roughness. The scattering from the snow surface dominates when roughness is high, but the interface return dominates if the roughness is low ( < 2.5 mm). This is the first study to consider scattering within the snow and demonstrate the origin of CryoSat-2 signals. This work paved the way to a new physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.
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Status: open (until 16 Aug 2024)
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RC1: 'Comment on egusphere-2024-1583', Anonymous Referee #1, 05 Jul 2024
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Review of “Altimetric Ku-band Radar Observations of Snow on Sea Ice Simulated with SMRT” by Julien Meloche et al.
The MS is describing and testing a radar altimeter scattering model implemented as part of the SMRT snow/ice modeling system with in situ observations collected in the Canadian Arctic and satellite CRYOSAT data. The topic is very welcome and altimeter scattering models for sea ice are needed for understanding the uncertainties in sea ice thickness estimation from past, current and future satellite radar altimeter missions. However, the MS seems to be written in a hurry; important info is missing e.g. roughness estimation procedure, key literature is missing from the reference list (see suggested articles), and some initial assumptions about the scattering mechanisms leads to a mismatch between the CRYOSAT observations and simulations with the model. This mismatch is then “fixed” with a calibration factor and this means that conclusions about the surface and volume contributions to the simulated backscatter are biased. In addition, some conclusions are not supported by the results (see specific comments).
Specific comments:
L1: neat-> near
L13: flat->level
L15: “The scattering… “ It is not clear which interface roughness is high or low? I am assuming that it is the snow ice interface roughness…
L16: “This is the first… “ There are studies before this e.g. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1621086
L1: environment->regions, delete “...for climate prediction”, strong->high
L33: what is meant by “mean return”? Is it the track-point? the mean backscatter? Something else? rewrite the sentence.
L34: tracking point->track-point (throughout).
L36: “tidal” are the tides corrected for when deriving the ice thickness?
L65: “...no volume scattering from brine.” here it is mentioned as a shortcoming of the referenced models but is it really important at all?
L67: “Our understanding …” there are some earlier studies, e.g.: https://ieeexplore.ieee.org/document/9000883, https://tc.copernicus.org/articles/15/1811/2021/
Kwok, R.: Simulated effects of a snow layer on retrieval of CryoSat-2 sea ice freeboard, Geophys. Res. Lett., 41, 5014–5020, 2014.
L69: delete “Snow depth…”
L72: “...scattering horizons…” Is the scattering horizon the same as the track-point? Please define.
L73: If Ka-band is scattered at the snow surface, then is it affected by the same “snow properties” as Ku-band? Please, be specific.
L74: “...must be challenged.” What do you mean? Are you challenging this model?
T1: Boundary roughness->Interface roughness
L174: RMS roughness should be measured at a horizontal resolution of 1/10 of a wavelength. See e.g Dierking, W. (2000). RMS slope of exponentially correlated surface roughness for radar applications. I E E E Transactions on Geoscience and Remote Sensing, 38(3), 1451-1454. https://doi.org/10.1109/36.843040. Please provide info on the horizontal resolution of your roughness map.
L195: “...antennae collect..”? rewrite this sentence.
L197: “..shallower depths…” What do you mean?
L211: “... assuming a Gaussian height distribution).” Do you need to assume anything when you have the data? Please find the distribution that fits your data best.
L213: there is a good discussion in: https://doi.org/10.1029/GM068p0111
L238: “To allow…” please rewrite, it is not clear.
L296: “Measured… please rewrite.
L311: I think the assumptions behind the model are wrong and that is why there is a mismatch between observations and simulations.
Figure 5: The simulations even after “correction” are missing the “peak backscatter” and it is not making a good prediction of the observations. Further, when all backscatter contributions, from the snow volume and the surfaces are multiplied by 5.9MW then the volume contribution, which I think that you have estimated correctly before the correction, is overestimated after the correction.
L448: Multiple scattering between interfaces would have a very small impact on the peak backscatter and the match between observations and simulations.
L483: It is not “encouraging” that the snow depth retrieval capability of CRISTAL depends on certain conditions of the interface roughness. This will complicate the retrieval of snow depth with CRISTAL.Citation: https://doi.org/10.5194/egusphere-2024-1583-RC1
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