Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements

Garnier, Florent; Fleury, Sara; Garric, Gilles; Bouffard, Jérôme; Laforge, Antoine; Bocquet, Marion; Hansen, Renée Mie Fredensborg; Rémy, Frédérique

doi:10.5194/tc-15-5483-2021

Cited by 27 publications

(26 citation statements)

References 124 publications

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“…To overcome the need for external snow depth information, future altimeters such as CRISTAL (Kern et al, 2020) will be equipped with dual-frequency radar altimeters, with the snow depth estimation being derived from the difference between the signals at Ku (13 GHz) and Ka (35 GHz) frequencies. Garnier et al (2021) already tested this possibility for snow depth and SIT retrievals with encouraging results, using two different altimetric missions, CS2 at 13 GHz and SARAL/AltiKa at 35 GHz (Verron et al, 2015). With SARAL/AltiKa limited to 82°N (thus excluding most of the multi-year ice), we did not consider this product in the current comparison.…”

Section: Global Arctic Results Over the Wintermentioning

confidence: 99%

“…Garnier et al. (2021) already tested this possibility for snow depth and SIT retrievals with encouraging results, using two different altimetric missions, CS2 at 13 GHz and SARAL/AltiKa at 35 GHz (Verron et al., 2015). With SARAL/AltiKa limited to 82°N (thus excluding most of the multi‐year ice), we did not consider this product in the current comparison.…”

Section: Resultsmentioning

confidence: 99%

“…(2020) or Garnier et al. (2021)) can help reduce the uncertainties related to the snow depth, and the future CRISTAL mission, to be launched approximately at the same time as CIMR, will be equipped with this dual frequency capability (Kern et al., 2020). The PMW SIT retrieval proposed here is based on a pragmatic approach.…”

Section: Discussionmentioning

confidence: 99%

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Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Soriot

Prigent

Jiménez³

et al. 2023

Earth and Space Science

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Arctic sea ice thickness (SIT) has been mostly retrieved from lidar and radar altimeters since the 2000s. However, the repeatability of altimeters and their spatial coverage limit SIT estimates spatially and temporally. On the other hand, the passive microwave (PMW) radiometers have daily basin‐scale coverage of the Arctic. In this study, we propose a SIT retrieval from PMW observations, derived from a statistical inversion technique. It is based on the evidence of high correlations between PMW observations and existing altimetric satellite‐derived SIT, especially at 36 GHz. Lidar ICESat‐2 SIT products are used to train a neural network with multiple combinations of brightness temperatures between 1.4 and 36 GHz as inputs over the 2018–2019 time period. The PMW retrieved SIT can mimic the lidar SIT product over the full winter over the Arctic, with a correlation of 0.85, and a root mean square difference (RMSD) of 0.54 cm. Results are also compared with the SIT product CS2SMOS (CryoSat‐2 and Soil Moisture Ocean Salinity merged product), with SIT from a coupled ice/ocean reanalysis model and with the Operation IceBridge QuickLook airborne SIT measurements. The PMW SIT retrieval with all frequencies from 1.4 to 36 GHz shows a correlation of 0.72 and a RMSD of 57 cm when compared to OIB‐QL measurements, for large SIT (mostly above 3 m), under multi‐year ice environments. The PMW SIT retrieval using only 18 and 36 GHz has similar performances and could allow the calculation of long time series, these microwave frequencies being available from satellites since the 1980s.

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Section: Global Arctic Results Over the Wintermentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Soriot

Prigent

Jiménez³

et al. 2023

Earth and Space Science

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show abstract

“…These methods suffer from a lack of in situ measurements to calibrate the estimations, which can have several sources of errors, such as the presence of the snow cover, as well as uncertainties on the freeboard, on the densities of ice and snow, etc. (Garnier et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of micro-seismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

Moreau

Seydoux

Weiss

et al. 2022

Preprint

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Abstract. In the perspective of upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building up on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequakes waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen fjord (Svalbard), we observe the spatial clustering of icequakes sources along the shore line of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the four weeks of data recording. Comparing the energy of the icequakes with that of calibrated seismic sources, we were able to derive a power law of icequake energy, and to relate this energy to the size of the cracks that generate the icequakes.

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“…More recent snow depth data based on the passive microwave, satellite altimetry, and numerical model can help to improve sea ice thickness retrieval (Blanchard-Wrigglesworth et al, 2018;Garnier et al, 2021;Kwok et al, 2020;Liston et al, 2020;Petty et al, 2018;Rostosky et al, 2018).…”

mentioning

confidence: 99%

Improving Arctic sea ice thickness retrieved from CryoSat-2: A comprehensive optimization of a retracking algorithm, radar penetration rate, and snow depth

Zhou

Chen

et al. 2023

Preprint

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Abstract. The Arctic sea ice thicknesses retrieved from the CryoSat-2 satellite are significantly influenced by sea ice surface roughness, snow backscatter, and snow depth on the sea ice. This study is the first to improve the retrieval of sea ice thickness from CryoSat-2 data derived by the Alfred Wegener Institute (AWI CS2) through applying a comprehensive optimization of an improved retracking algorithm, corrected radar penetration rate, and new snow depth. The radar freeboard data was obtained from the improved retracking algorithm of the Lognormal Altimeter Retracker Model (LARM). The radar penetration rates were corrected to 0.77 for first-year ice (FYI), 0.96 for multi-year ice (MYI), and 0.91 for all ice types. The new snow depth data was derived from the Chinese satellite Feng Yun-3B with the MicroWave Radiometer Imager (FY3B/MWRI). Three individual and one combined optimization cases were created by focusing on the retracking algorithm, radar penetration rate, and snow depth, which were validated with in situ observations collected from the National Aeronautics and Space Administration Operation IceBridge (OIB), Ice Mass Balance buoys (IMB), CryoSat Validation Experiment (CryoVEX), and Alfred Wegener Institute IceBird Program (AWI IceBird). The assessment results showed that all the optimization cases had the ability to effectively improve the sea ice thickness, with similar correlation coefficients. In the validation with the four kinds of observed data, the optimization cases reduced the RMSE of AWI CS2 up to 0.23 m (25.0 %), 0.27 m (29.7 %), 0.26 m (25.5 %), and 0.22 m (23.9 %). The improved sea ice thickness retrieval retained the major distribution patterns generated by AWI CS2, but generally showed thinner sea ice thickness. In particular, in the MYI region, the difference in thickness became increasingly evident from fall to spring. The differences in the variation trend between the optimization cases and AWI CS2 were significant in some coastal regions and the central Arctic. The experiments revealed that the radar penetration rate calculation was more sensitive to sea ice density than to snow density. The sensitivity experiments suggested that the snow depth of TOPAZ4, in addition to that of FY3B/MWRI, was also applicable in improving the retrieval of sea ice thickness. The updated scheme of sea ice densities (FYI = 925 kg m−3 and MYI = 902 kg m−3) can be combined with the use of comprehensive optimization to improve the retrieval of sea ice thickness. This successful optimization provided new insights into improving the sea ice thickness retrieved from CryoSat-2 and helped to further understand and quantify the spatiotemporal variations of sea ice thickness.

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Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements

Cited by 27 publications

References 124 publications

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Analysis of micro-seismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

Improving Arctic sea ice thickness retrieved from CryoSat-2: A comprehensive optimization of a retracking algorithm, radar penetration rate, and snow depth

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