the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
AWI-ICENet1: A convolutional neural network retracker for ice altimetry
Alireza Dehghanpour
Ronny Hänsch
Erik Loebel
Martin Horwath
Angelika Humbert
Abstract. The Greenlandic and Antarctic Ice Sheet are important indicators of climate change and major contributors to sea level rise. Hence, precise, long-term observations of surface elevation change are required to assess changes and their contribution to sea level rise. Satellite radar altimetry has been used by various missions to measure surface elevation change since 1992. It has been shown that, next to the surface slope and complex topography, one of the most challenging issues is the spatial and temporal variability of radar pulse penetration into the snow pack, especially over the vast East Antarctic plateau. This results in an inaccurate measurement of the true surface elevation and consequently affects surface elevation change (SEC) estimates. To increase the accuracy of surface elevations retrieved by retracking the radar return waveform and thus reduce the uncertainty in SEC, we developed a deep convolutional neural network architecture (AWI-ICENet1). The AWI-ICENet1 is trained using a simulated reference data set with 3.8 million waveforms, taking into account different surface slopes, topography, and attenuation. The successfully trained network is finally applied as AWI-ICENet1-retracker to the full time series of CryoSat-2 Low Resolution Mode (LRM) waveforms over both ice sheets. We compare the AWI-ICENet1 retrieved SEC with estimates of conventional retrackers like TFMRA and ESA ICE1 and ESA ICE2 products. Our results show less uncertainty and a greatly diminished effect of time variable radar penetration, reducing the need to apply corrections based on a close relationship with backscatter- and/or leading edge width, as typically done in SEC processing. This technique provides new opportunities to utilize convolutional neural networks in altimetry, waveform retracking, and processing of satellite altimetry data, which can be applied to historical, recent, and future missions.
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Veit Helm et al.
Status: open (extended)
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RC1: 'Comment on tc-2023-80', Anonymous Referee #1, 29 Sep 2023
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The authors have demonstrated commendable expertise and innovation in their work by introducing CNN in the field of radar altimetry retracking. Their research represents a significant contribution to the field, offering valuable insights and promising results. By employing CNN, the authors have opened new possibilities for improving the accuracy and efficiency of altimetry data processing, and possibly (not addressed) speeding up otherwise time-consuming retracking. The paper introduces this innovative approach and provides convincing evidence of its advantages. While comprehensive research is crucial for presenting a well-rounded perspective, the excessive length of the papers can be challenging for readers to digest. One suggestion would be to separate the retracking from a second paper on the applications or move some of the backgrounds to an appendix. This being said, I only have minor comments on the paper.
One aspect of AWI-ICENet1 which could be addressed is the possible increased efficiency in the processing time. As the authors have great experience in retracking radar altimetry data, it would also be beneficial to highlight the efficiency of the AWI-ICENet1 compared to other methods. We are often faced with very long reprocessing times from the agencies.
In general, the caption for the many figures is very shallow, please read through them and elaborate on them, so a reader who is not reading all 61 pages can follow the main conclusions of the figures.
L2: “long-term observations of surface elevation change are required to”, agree with the meaning but I would suggest that you also acknowledge other methods and hence remove the “Surface”
L5: The snow penetration is an issue in most places, just leave this sentence open and remove “especially over the…”
L15: This shows a broader application and suggests writing “This technique provides new opportunities to utilize convolutional neural networks in the processing of satellite altimetry data, which can be applied to historical, recent, and future missions.”
L21: missing reference at “2010 onwards”
L30 may start with “Ku-band satellite altimeters…” as the mentioned satellites are all Ku.
L40 Why add the PLRM, this is a pure post-processing product.
L43: ICESat-2 operating at green-wavelength is penetrating the snow. And add references for ICESat-2.
L46: please elaborate on the sentence “Because…”,
L73: the common abbreviation is CNN, please consider using this
L124: why exclude Greenland?
L128: to help the reproducibility of the study please give more insights into the chosen backscatter cross-section values.
L144: “rate in the following”, the paper has a couple of these please proofread the paper once more.
L153 What is the resolution of the applied DEM in the modelling? (it might not fit here but should be discussed in this relation)
L177: move the specific tensorflow package to the acknowledgement, and add a reference to this library.
L189: With possible differences/drift in Bs in the processing baseline only one should be used. For consistency use E.
L194: add a reference for ATL06.005
L196: Add the resolution used.
L210: so it is the 2 km product which is used, why is this chosen?
L221: “the correction adapted from Nilsson et al. (2022)” elaborates on what is used.
F5: add a plot of the model vs. test point cloud. Maybe retracked range vs model range.
L238: As Greenland is different and possibly more complicated it would be nice the see an ROI in Greenland.
F7: thank you for this very convincing figure. How does this look with respect to slope?
L265: Roemer is a better solution however the LEPTA relocation seems to improve even further, how does this affect the results?
L268: monthly crossovers are a very long time span for cross-overs on ice sheets please add a lower time constraining on the timing between orbital crossing evaluated.
L270: how many fall within this outlier filtering (maybe in %)
F8-F10: The ESA ICE2 seems as an outlier compared to the others, suggest removing this from the plots to see the specific difference in the others.
L423: “…focus on the time from January 2019 to December 2021…”
F20: Is the trackiness due to errors in the CS2 or ICESat-2 data?
L472: Guess this is ATL15 this should be mentioned.
Citation: https://doi.org/10.5194/tc-2023-80-RC1
Veit Helm et al.
Veit Helm et al.
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