scholarly journals Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers

2021 ◽  
Author(s):  
Xiaoyi Shen ◽  
Chang-Qing Ke ◽  
Haili Li
Keyword(s):  
Tibetan Plateau ◽  
Sea Ice ◽  
Snow Depth ◽  
Depth Estimation ◽  
Passive Microwave ◽  
Energy Budgets ◽  
Antarctic Sea Ice ◽  
Basin Scale ◽  
Daily Scale ◽  
Microwave Radiometers

Abstract. Snow over sea ice controls energy budgets and affects sea ice growth/melting, and thus has essential effects on the climate. Passive microwave radiometers can be used for basin-scale snow depth estimation at a daily scale; however, previously published methods applied to Antarctica clearly underestimated snow depth, limiting their further application. Here, we estimated snow depth using microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2 since 2002. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements showed that the gradient ratio (GR) calculated using brightness temperatures in vertically polarized 37 and 19 GHz, i.e., GR(37/7), was optimal for deriving Antarctic snow depth, with a correlation coefficient of −0.64. We hence derive new coefficients based on GR(37/7) to improve the current snow depth estimation from passive microwave radiometers. Comparing the new retrieval with in situ measurements from the Australian Antarctic Data Centre showed that this method outperformed the previously available method, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, compared to values of −14.47 cm and 19.49 cm, respectively. A comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicated that in thin ice regions, the proposed method performed slightly better than the previous method (with RMSDs of 16.85 cm and 17.61 cm, respectively). Comparable performances during the growth and melting seasons suggest that the proposed method can still be used during the melting season. Gaussian error propagation found an average snow depth uncertainty of 3.81 cm, which accounted for 12 % of the estimated mean snow depth. We generated a complete snow depth product over Antarctic sea ice from 2002 to 2020 on a daily scale, and negative trends could be found in all sea sectors and seasons. This dataset (including both snow depth and snow depth uncertainty) can be downloaded from National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at http://data.tpdc.ac.cn/en/disallow/61ea8177-7177-4507-aeeb-0c7b653d6fc3/ (Shen and Ke, 2021, DOI: 10.11888/Snow.tpdc.271653).

scholarly journals Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

2018 ◽  
Vol 123 (10) ◽  
pp. 7120-7138 ◽  
Author(s):  
Philip Rostosky ◽  
Gunnar Spreen ◽  
Sinead L. Farrell ◽  
Torben Frost ◽  
Georg Heygster ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Arctic Sea ◽  
Microwave Radiometers ◽  
Multiyear Ice ◽  
Lower Frequencies

Spectral linear mixing model applied to data from passive microwave radiometers for sea ice mapping in the Antarctic Peninsula

2021 ◽  
pp. 1-27
Author(s):  
Fernando Luis Hillebrand ◽  
Ulisses Franz Bremer ◽  
Marcos Wellausen Dias de Freitas ◽  
Juliana Costi ◽  
Cláudio Wilson Mendes Júnior ◽  
...  
Keyword(s):  
Sea Ice ◽  
Antarctic Peninsula ◽  
Mixing Model ◽  
Passive Microwave ◽  
Linear Mixing Model ◽  
Microwave Radiometers ◽  
The Antarctic

2 – 8 GHz FMCW radar for estimating snow depth on antarctic sea ice

2008 ◽  
Author(s):  
N. Galin ◽  
A. Worby ◽  
R. Massom ◽  
G. Brooker ◽  
C. Leuschen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Fmcw Radar ◽  
Antarctic Sea Ice

scholarly journals An intercomparison between AMSR-E snow-depth and satellite C- and Ku-band radar backscatter data for Antarctic sea ice

2011 ◽  
Vol 52 (57) ◽  
pp. 279-290 ◽  
Author(s):  
Stefan Kern ◽  
Burcu Ozsoy-Cicek ◽  
Sascha Willmes ◽  
Marcel Nicolaus ◽  
Christian Haas ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Weddell Sea ◽  
Late Winter ◽  
First Year ◽  
Radar Backscatter ◽  
Antarctic Sea Ice ◽  
Ridged Ice ◽  
Ice Fraction ◽  
Ku Band

AbstractAdvanced Microwave Scanning Radiometer (AMSR-E) snow-depth data for Antarctic sea ice are compared with ship-based visual observations of snow depth, ice type and ridged-ice fraction, and with satellite C-band and Ku-band radar backscatter observations for two ship cruises into the Weddell Sea (ISPOL 2004–05,WWOS 2006) and one cruise into the Bellingshausen Sea (SIMBA 2007) during late winter/spring. Most (>75%) AMSR-E and ship-based snow-depth observations agree within 0.2 m during WWOS and SIMBA. Remaining observations indicate substantial underestimations of snow depths by AMSR-E data. These underestimations tend to increase with the ridged-ice fraction for WWOS and SIMBA. In areas with large snow depths, a combination of relatively stable low C-band radar backscatter and variable Ku-band radar backscatter is associated with undeformed first-year ice and may indicate snow metamorphism at this time of year during SIMBA. In areas with small snow depths, a combination of relatively stable low Ku-band radar backscatter, high C-band radar backscatter and low C-band radar backscatter standard deviations is associated with rough first-year ice during SIMBA. This information can help to better understand causes of the observed AMSR-E snow-depth bias during late-winter/spring conditions with decreasing average snow depth and to delineate areas where this bias occurs.

scholarly journals Snow depth uncertainty and its implications on satellite derived Antarctic sea ice thickness

2018 ◽  
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Fast Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.

scholarly journals Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns

2019 ◽  
Vol 13 (3) ◽  
pp. 861-878 ◽  
Author(s):  
Steven W. Fons ◽  
Nathan T. Kurtz
Keyword(s):  
Sea Ice ◽  
Physical Model ◽  
Snow Depth ◽  
Trust Region ◽  
Surface Elevation ◽  
Snow Surface ◽  
Snow Layer ◽  
Antarctic Sea Ice ◽  
The Difference ◽  
Waveform Fitting

Abstract. In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector.

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