the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors
Ying Chen
Xi Zhao
Ruibo Lei
Shengli Wu
Yue Liu
Pei Fan
Qing Ji
Peng Zhang
Xiaoping Pang
Abstract. Sea ice concentration (SIC) is the main variable for quantifying the sea ice change in the polar regions. Continuous SIC data is very important for the studies of climate and polar marine environments. This study generates a new SIC product covering the Arctic and Antarctic from November 2010 to December 2019. It is the first long-term SIC product derived from the Microwave Radiation Imager (MWRI) sensors onboard the Chinese FengYun-3B, -3C, and -3D satellites, after a recent re-calibration of brightness temperature. We also modified the preliminary dynamic tie points Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm mainly through input TB and initial tie points. The MWRI-ASI SIC was compared to the existing ASI SIC products and validated using ship-based SIC observations. Results show that the MWRI-ASI SIC mostly coincides with the ASI SIC obtained from the Special Sensor Microwave Imager series sensors, with overall biases of -1.4 ± 1.8 % in the Arctic and 0.5 ± 2.2 % in the Antarctic, respectively. The overall mean absolute deviation between the MWRI-ASI SIC and ship-based SIC is 16.1 % and 17.1 % in the Arctic and Antarctic, respectively, which is close to the existing ASI SIC products. The trend of sea ice extent (SIE) derived from MWRI-ASI SIC closely agrees with those of the Sea Ice Index SIEs provided by OSI-SAF and NSIDC. Therefore, the MWRI-ASI SIC is comparable with other SIC products and is qualified to be integrated into long-term sea ice records. The MWRI-ASI SIC dataset is available at https://doi.pangaea.de/10.1594/PANGAEA.945188 (Chen et al., 2022).
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Ying Chen et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-186', Lars Kaleschke, 23 Sep 2022
I am pleased to see that the ASI method is being used successfully on the Chinese satellites FengYun-3B, 3C and 3D. To my knowledge this is the first sea ice product from a Chinese satellite series that covers a long time period. Therefore, I consider this work potentially relevant for the ESSD journal.
I’d like to give only general comments and questions because the information provided with the manuscript does not allow further review in particular with respect to the source data and validation. A code repository is missing, see https://essd.copernicus.org/articles/10/2275/2018/
For the introduction of the near 90 GHz method please also refer to the prior work of Svendsen et al. (1987) which forms the basics of the algorithm.
Svendsen, E., Matzler c., and Grenfell, TC. (1987). "A Model for Retrieving Total Sea Ice Concentration from a Spaceborne Dual-Polarized Passive Microwave Instrument Operating Near 90 GHz", International Journal 0/ Remote Sensing, Vol. 8, No. 10, pp. 1479-1487.
I can not judge the quality of the brightness temperature because it is not yet published: The recently re-calibrated brightness temperature (TB) of the MWRI sensors provided by NSMC (Wu et al., 2022) were used in this study
Because ESSD is a journal for “open data” (isn’t it?), I also would like to know a bit more about the availability of the source data.
I have not fully understood the concept of the preliminary dynamic tie points. This could be outlined in more detail.
My main concern is insufficient scientific motivation: “In order to promote the application of MWRI sensors, especially to back up the existing sea ice products”. Why is there the need for a backup for the existing data? Well, I could understand the argument for the future. This could be further elaborated. However, I think the main advantage of having such a data set is the true independence which potentially allows the application of techniques like triple collocation between different satellite data records. How would you judge the “risk of breaking”. For example, we expect the AMSR3 launch in 2023 or 24.
What is meant with “qualified to be integrated into long-term sea ice records” and “The MWRI-ASI SIE can be better integrated into the Sea Ice Index SIE in the Arctic and the OSI-SAF SIE in the Antarctic compared to other products.”
Table 1: TB characteristics missing. What about uncertainties? Grid resolution is different from field of view.
Table 1 in Zhao et al. (2021) states across scan resolution of 89 GHz is 9x15 km^2. With a grid spacing of 12.5 km there is significant undersampling in one direction. Why not use a 6 km grid for the sake of Nyquist-Shannon?
The ICDC ASI-SSMI version is probably the 5-day median-filtered version? The single day data are available from IFREMER. Both data sets are different in their characteristics. Probably not too much but this should be considered. This is important also for the discussion of the land spillover because the temporal filter has a strong effect. For potential further improvement I refer to Maaß et al. (2010).
Nina Maaβ & Lars Kaleschke (2010) Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea, Tellus A: Dynamic Meteorology and Oceanography, 62:4, 393-410, DOI: 10.1111/j.1600-0870.2009.00452.x
Citation: https://doi.org/10.5194/essd-2022-186-RC1 -
AC1: 'Reply on RC1', Xiaoping Pang, 05 Jan 2023
Dear Dr. Lars Kaleschke,
Many thanks for the review of our manuscript. The comments and suggestions are very useful to improve our manuscript. We have considered each comment carefully and provided an itemized response to the comments in the “supplement”. In the revised version of our manuscript, we marked all the changes in blue. Also, a supplement file of our manuscript is provided.
Looking forward to hearing from you soon.
Best Regards,
Xiaoping Pang and other contributors.
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AC1: 'Reply on RC1', Xiaoping Pang, 05 Jan 2023
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RC2: 'Comment on essd-2022-186', Vishnu Nandan, 03 Dec 2022
In this manuscript, the authors use FengYun satellite series and derived sea ice concentration from 2010-19, which is interesting and valuable to the sea ice community and adds valuable information to the existing SIC climatology from other PMW satellite data. Therefore, I suggest potential publication to ESSD journal. However, I have few major comments that needs to be addressed before publication. Since I am a radar remote sensing scientist with expertise in sea ice (and snow) geophysics, for this round of review, I focus more on the geophysical uncertainty aspect that needs some attention. I am happy to review the revised manuscript and then would give a comprehensive review with more technical and specific comments. Below are my major comments.
a) My main concern with your paper is that you have not provided any information on how SNOW as a critical geophyical parameter affects brightness temperature and SIC estimates and its UNCERTAINTY from your datasets. In a warming Arctic and a fluctuating Antarctic, how does shift in sea ice types from MYI to FYI affect snow properties and that in turn affects your SIC estimates? Can you provide a uncertainty range in your derived SICs based on the snow cover and its spatiotemporal variability? For example, FYI cover is characterized by saline snow covers while thicker snow on thinner ice (especially in the Antarctic) is severely affected by flooding, slush and refrozen snow-ice formations. They severely affect the emitting layer correct? I think authors, since they are showing a brand new dataset, its worth and necessary to show the geophysical uncertainties as a quanity. I am a bit disappointed that snow is completely neglected (I should say) in your data product.
b) I am really surprised to see almost NO regional variabiity in Antarctic SIC (see Figure 3 (g) to (i)), from your products, when even recent studies (for example: https://essd.copernicus.org/articles/14/619/2022/essd-14-619-2022.html just to quote one) from the same time period as yours have shown large variability in snow depth (that heavily influences SIC) across multiple Antarctic sea ice sectors. This points to my previous comment about accounting for snow depth and its variability in your calculations. I think its a good idea to revisit PMW-derived snow depth data (not that its completely accurate) or any other snow models that can be used to quantify the SIC uncertainty, and combinely use them to figure out the regional SIC variaiblity atleast in the Antarctic. I am sure readers would appreciate that !
c) Like reviewer 1 mentioned, the introduction with your rationale is missing or maybe scattered/lost/cluttered within the intro material. I think refining the introduction will help the readability of the paper. Also, just a minor comment. I was curious to know the incidence angle used for the data acquisition.
For now I refrain to providing these major comments above. In your revised version, I will provide comprehensive comments on the write up.
Citation: https://doi.org/10.5194/essd-2022-186-RC2 -
AC2: 'Reply on RC2', Xiaoping Pang, 05 Jan 2023
Dear Dr. Vishnu Nandan,
Many thanks for the review of our manuscript. The comments and suggestions are very helpful to improve our manuscript. We have considered each comment carefully and provided responses to the comments in the “supplement”. In the revised version of our manuscript, we marked all the changes in blue. Also, a supplement file of our manuscript is provided.
Looking forward to hearing from you soon.
Best Regards,
Xiaoping Pang and other contributors.
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AC2: 'Reply on RC2', Xiaoping Pang, 05 Jan 2023
Ying Chen et al.
Data sets
Sea ice concentration derived from temperature brightness data of the Microwave Radiation Imager sensors onboard the Chinese FengYun-3 satellites in the polar regions from 2010 to 2019 Chen, Ying; Pang, Xiaoping; Lei, Ruibo; Zhao, X (2022) https://doi.pangaea.de/10.1594/PANGAEA.945188
Ying Chen et al.
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