the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Anticipating CRISTAL: An exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023
Abstract. The EU and ESA plan to launch a dual-frequency Ku- and Ka-band polar-orbiting synthetic aperture radar (SAR) altimeter, CRISTAL (Copernicus Polar Ice and Snow Topography Altimeter), by 2028 to monitor polar sea ice thickness and its overlying snow depth, among other applications. However, the interactions of Ku- and Ka-band radar waves with snow and sea ice are not fully understood, demanding further research effort before we can take full advantage of the CRISTAL observations. Here, we use three ongoing altimetry missions to mimic the sensing configuration of CRISTAL over Arctic sea ice and investigate the derived snow depth estimates obtained from dual-frequency altimetry. We apply a physical model for the backscattered radar altimeter echo over sea ice to CryoSat-2’s Ku-band altimeter in SAR mode and to the SARAL mission’s AltiKa Ka-band altimeter in low-resolution mode (LRM), then compare to reference laser altimetry observations from ICESat-2. ICESat-2 snow freeboards (snow + sea ice) are representative of the air-snow interface, whereas the radar freeboards of AltiKa are expected to represent a height at or close to the air-snow interface, and CryoSat-2 radar freeboards a height at or close to the snow-ice interface. The freeboards from AltiKa and ICESat-2 show similar patterns and distributions; however, the AltiKa freeboards do not thicken at the same rate over winter, implying that Ka-band height estimates can be biased low by 10 cm relative to the snow surface due to uncertain penetration over first-year ice in spring. Previously-observed mismatches between radar freeboards and independent airborne reference data have been frequently attributed to radar penetration biases, but can be significantly reduced by accounting for surface topography when retracking the radar waveforms. Waveform simulations of CRISTAL in its expected sea ice mode reveal that the heights of the detected snow and ice interfaces are more sensitive to multi-scale surface roughness than snow properties. For late-winter conditions, the simulations suggest that the CRISTAL Ku-band radar retrievals will track a median elevation 3 % above the snow-ice interface, because the radar return is dominated by surface scattering from the snow-ice interface which has a consistently smoother footprint-scale slope distribution than the air-snow interface. Significantly more backscatter is simulated to return from the air-snow interface and snow volume at Ka-band, with the radar retrievals tracking a median elevation 10 % below the air-snow interface. These model results generally agree with the derived satellite radar freeboards, which are consistently thicker for AltiKa than CryoSat-2, across all measured snow and sea ice conditions.
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Status: open (until 12 Dec 2024)
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CC1: 'Comment on egusphere-2024-2904', Zhaoqing Dong, 29 Sep 2024
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Hey, Jack!
I just curious about these specific values of satellite velocity, synthetic beam gain, along-track antenna parameter and across-track antenna parameter of pulse-limited altimeter AltiKa SARAL of pulse-limited altimeter. How do you set these parameters in FBEM model? Thank you for your explanation!
Cheers,
Zhaoqing
Citation: https://doi.org/10.5194/egusphere-2024-2904-CC1 -
AC1: 'Reply on CC1', Jack Landy, 02 Oct 2024
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Good question Zhaoqing,
For the AltiKa version of FBEM, we set
satellite velocity = 7470
synthetic beam gain = 1 (as a dummy, not used for LRM echoes)
along-track pattern term = (0.605*pi/180)/(2*sqrt(log(2)))
across-track pattern term = along-track pattern termMost AltiKa instrument parameters were obtained from https://directory.eoportal.org/web/eoportal/satellite-missions/s/saral
I've also now uploaded the SHELL.m script for AltiKa echoes to the FBEM github page.
All the best, Jack
Citation: https://doi.org/10.5194/egusphere-2024-2904-AC1
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AC1: 'Reply on CC1', Jack Landy, 02 Oct 2024
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CC2: 'Comment on egusphere-2024-2904', Arttu Jutila, 01 Nov 2024
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Dear Jack and co-authors,
Kudos to you on your important and timely work on this topic! With this community comment, I would like to raise some points regarding the reference observations (L175ff).
First off, the minor technicalities:
- Could you please update the reference Jutila et al. (2021) from the preprint in The Cryosphere Discussions to the published article that has been available now for nearly three years? I mention this here because I have encountered recent papers where this has not always been caught even after professional copy-editing.
- Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., Krumpen, T., and Haas, C.: Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements, The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, 2022.
- While the geophysical measurement data have not changed, I would appreciate if you would refer to the most recent version (v2) of the AWI IceBird dataset as:
- Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., and Haas, C.: Airborne sea ice parameters during the IceBird Winter 2019 campaign in the Arctic Ocean, Version 2 [dataset publication series], PANGAEA, https://doi.org/10.1594/PANGAEA.966057, 2024.
Then to the more interesting bit, which is applying pySnowRadar and the peakiness method to the April 2019 OIB data.
- Which flights have you processed exactly? The snow radar parameter spreadsheet (snow_param_2019_Greenland_P3.xls available at https://gitlab.com/openpolarradar/opr_params) lists a total of six, not five, flights over sea ice (sheet “cmd”, column “mission_names”, “Sea Ice:*”). Also NSIDC has six files with those dates in 2019 data (https://doi.org/10.5067/GRIXZ91DE0L9).
- On L185, you mention using “the same pySnowRadar parameters as the IceBird data”, but I guess you mean the peakiness method parameters? pySnowRadar contains also other retrieval algorithm modules like the wavelet method (Newman et al., 2014) with very different parameters for very different purposes.
- From the snow radar parameter spreadsheet notes, it is also obvious that some OIB flights were carried out with a reduced bandwidth (2-8 GHz instead of the full 2-18 GHz) and/or at an unusually high altitude of 3500 ft (nominally ~1500 ft). The peakiness method was not developed and has not been tested for such missions, and I am curious how the snow depth retrieval results looked like. Did you compare them against the official OIB product at NSIDC (https://doi.org/10.5067/GRIXZ91DE0L9)?
- While its impact is rather minor, which snow density value did you use in the processing? Later, on L193, you mention assuming snow density of 350 kg m-3. Or was it perhaps varying according to Mallett (2024)? In AWI IceBird, it was fixed at 300 kg m-3.
Citation: https://doi.org/10.5194/egusphere-2024-2904-CC2 - Could you please update the reference Jutila et al. (2021) from the preprint in The Cryosphere Discussions to the published article that has been available now for nearly three years? I mention this here because I have encountered recent papers where this has not always been caught even after professional copy-editing.
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RC1: 'Comment on egusphere-2024-2904', Anonymous Referee #1, 01 Nov 2024
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This paper explores existing satellite and airborne radar and laser altimetry observations over the Arctic from 2018-2023 to assess potential future observations from dual frequency Ka and Ku radar from CRISTAL. The study is very well organized, and thoroughly explores similarities and differences in current observations with strong ties to understanding the physical basis for differences. The results are highly impactful and will be an extremely useful reference in preparation for CRISTAL and understanding differences in Ku and Ka radar as well as laser altimetry missions.
I did not see any major technical errors and the explanations and figures were very clear. I just noted some questions that arose while reading the manuscript as outlined below. These are all minor and I would otherwise suggest publication subject to some minor revisions.
L118: What is meant by calibrated and uncalibrated observations in this sentence?
L203-204: Can you describe in more detail how the interpolation is done between tie points? Is it linear and over what length scale?
L225-230: Are these four parameter terms independent or are they linked together in some way e.g. the surface topography root-mean square height and mm-cm ‘radar-scale’ roughness?
L259: There looks to be a typo here in 95 8%
L270-275: How are the initial starting point values for the lsqnonlin solver determined?
Figure 2: Can you describe the methodology for discarding secondary peaks? Does this differ between CryoSat-2 and AltiKA?
L331: Is a consistent snow density as outlined here used also in the processing of the snow radar data?
L396-400: Can you calculate a skewness for the results? They do indeed appear Gaussian visually, but perhaps this metric could show this quantitatively.
L428: I was confused by the reference to the Beaufort Sea and MOSAiC transects here though see these are discussed a bit later in the paper. The MOSAiC measurements could be discussed in more detail in Section 2 as well.
L588: I’m not sure the statement about filtering out dark leads applies here. Dark leads are only not considered for sea surface height determination, but their freeboard heights still remain in the product.
Citation: https://doi.org/10.5194/egusphere-2024-2904-RC1
Data sets
University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2 Jack Landy https://doi.org/10.5281/zenodo.13774843
Model code and software
Facet-Based SAR Altimeter Echo Model Jack Landy https://github.com/jclandy/FBEM
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