Evaluation of CryoSat-2 derived sea-ice freeboard over fast ice in McMurdo Sound, Antarctica

Price, Daniel D.; Beckers, Justin; Ricker, Robert; Kurtz, N. T.; Rack, Wolfgang; Haas, Christian; Helm, Veit; Hendricks, Stefan; Leonard, Greg H; Langhorne, Patricia J.

doi:10.3189/2015jog14j157

Cited by 35 publications

(37 citation statements)

References 47 publications

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“…In their study they could show that the winter mean freeboard from ERS-2 shows a reasonable distribution and good qualitative agreement with ship-based observations. Later, Price et al (2015) found a good agreement with field data using CS-2 radar signals to derive sea-ice freeboard over the fast ice of McMurdo Sound.…”

Section: Introductionsupporting

confidence: 54%

“…A study of Price et al (2015) indicated that the reflection horizon of CS-2 data over Antarctic sea ice, derived with a retracker threshold of 40 %, is certainly close to the snow/air interface. However, Operation IceBridge laser freeboard measurements over Antarctic sea ice as well as laser altimeter data from the R/V Polarstern expedition PS81 in winter 2013 and PS89 in summer 2014/2015 are expected to be available in the near future.…”

Section: Discussionmentioning

confidence: 99%

“…In this case radar altimeter range measurements generally relate to ice freeboard. However, the generality of this assumption has been recently questioned by several publications (Willatt et al, 2010(Willatt et al, , 2011Ricker et al, 2014;Kurtz et al, 2014;Price et al, 2015;Kwok, 2014).…”

Section: Introductionmentioning

confidence: 99%

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About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

et al. 2016

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Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on board Envisat and freeboard measurements from the SyntheticAperture Interferometric Radar Altimeter on board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are in reasonable agreement over the sea-ice growth season (May-October) and partly also beyond. In general, Envisat data show higher freeboards in the first-year ice zone while CryoSat-2 freeboards are higher in the multiyear ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regionally separated) Southern Ocean differs generally by not more than 3 cm (8 cm, with few exceptions) between Envisat and CryoSat-2, and the differences between modal freeboards lie generally within ±10 cm and often even below.

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Section: Introductionsupporting

confidence: 54%

Section: Discussionmentioning

confidence: 99%

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About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

et al. 2016

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“…Laser and radar altimetries are the most effective methods to date and have produced the first sea‐ice thickness results over the Antarctic and WG (Giles et al, ; Kurtz & Markus, ; Yi et al, ; Zwally et al, ). However, altimetric sea‐ice thickness retrievals are subject to large uncertainties, particularly due to the unknown thickness and microwave scattering properties of the overlying snow (e.g., D. Price et al, ; Xie et al, ). These uncertainties are further complicated by the snow‐ice interface, which can fall below the local water level as a result of heavy snow fall on the relatively thin sea ice, causing a negative freeboard and widespread flooding (e.g., Massom et al, ).…”

Section: Cryosphere Ii: Sea Icementioning

confidence: 99%

The Weddell Gyre, Southern Ocean: Present Knowledge and Future Challenges

Vernet

Geibert

Hoppema

et al. 2019

Reviews of Geophysics

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154

147

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The Weddell Gyre (WG) is one of the main oceanographic features of the Southern Ocean south of the Antarctic Circumpolar Current which plays an influential role in global ocean circulation as well as gas exchange with the atmosphere. We review the state‐of‐the art knowledge concerning the WG from an interdisciplinary perspective, uncovering critical aspects needed to understand this system's role in shaping the future evolution of oceanic heat and carbon uptake over the next decades. The main limitations in our knowledge are related to the conditions in this extreme and remote environment, where the polar night, very low air temperatures, and presence of sea ice year‐round hamper field and remotely sensed measurements. We highlight the importance of winter and under‐ice conditions in the southern WG, the role that new technology will play to overcome present‐day sampling limitations, the importance of the WG connectivity to the low‐latitude oceans and atmosphere, and the expected intensification of the WG circulation as the westerly winds intensify. Greater international cooperation is needed to define key sampling locations that can be visited by any research vessel in the region. Existing transects sampled since the 1980s along the Prime Meridian and along an East‐West section at ~62°S should be maintained with regularity to provide answers to the relevant questions. This approach will provide long‐term data to determine trends and will improve representation of processes for regional, Antarctic‐wide, and global modeling efforts—thereby enhancing predictions of the WG in global ocean circulation and climate.

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“…For CryoSat the assumption is that most of the signal is scattered at the snow-ice interface; however different studies suggest that in some cases (e.g. with a saline snowpack, slush and layering) the main scattering horizon is rather "somewhere within the snowpack" (King et al, 2018;Price et al, 2015;Kwok and Kacimi, 2018). Alexandrov et al (2010) state that the freeboard error may be reduced by averaging.…”

Section: Rmsementioning

confidence: 99%

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Braakmann-Folgmann

Donlon

2019

The Cryosphere

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Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge.A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath > 1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn-dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger "hole at the pole". Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

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Evaluation of CryoSat-2 derived sea-ice freeboard over fast ice in McMurdo Sound, Antarctica

Cited by 35 publications

References 47 publications

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

The Weddell Gyre, Southern Ocean: Present Knowledge and Future Challenges

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

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