scholarly journals Surface-Based Ku- and Ka-band Polarimetric Radar for Sea Ice Studies

2020 ◽  
Author(s):  
Julienne Stroeve ◽  
Vishnu Nandan ◽  
Rosemary Willatt ◽  
Rasmus Tonboe ◽  
Stefan Hendricks ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Incidence Angle ◽  
Ice Thickness ◽  
Polarimetric Radar ◽  
Radar Backscatter ◽  
Sea Ice Thickness ◽  
Ka Band ◽  
Snow Properties ◽  
Wide Range

Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as investigating the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully-polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC International Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and a scatterometer (scanning mode), provided the first in situ Ku- and Ka-band dual frequency radar observations from autumn freeze-up through mid-winter, and covering newly formed ice in leads, first-year and second-year ice floes. Data gathered in the altimeter mode, will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angle ranges, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar and illustrate examples of these data and demonstrate their potential for these investigations.

scholarly journals Surface-based Ku- and Ka-band polarimetric radar for sea ice studies

2020 ◽  
Vol 14 (12) ◽  
pp. 4405-4426
Author(s):  
Julienne Stroeve ◽  
Vishnu Nandan ◽  
Rosemary Willatt ◽  
Rasmus Tonboe ◽  
Stefan Hendricks ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Thickness ◽  
Polarimetric Radar ◽  
Radar Altimeter ◽  
Radar Backscatter ◽  
Sea Ice Thickness ◽  
Ka Band ◽  
Snow Properties ◽  
Wide Range

Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

scholarly journals Uncertainties in Antarctic sea-ice thickness retrieval from ICESat

2015 ◽  
Vol 56 (69) ◽  
pp. 107-119 ◽  
Author(s):  
Stefan Kern ◽  
Gunnar Spreen
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Sensitivity Study ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Earth Observing System ◽  
Antarctic Sea Ice ◽  
Snow Properties ◽  
The Mean

AbstractA sensitivity study was carried out for the lowest-level elevation method to retrieve total (sea ice + snow) freeboard from Ice, Cloud and land Elevation Satellite (ICESat) elevation measurements in the Weddell Sea, Antarctica. Varying the percentage (P) of elevations used to approximate the instantaneous sea-surface height can cause widespread changes of a few to ˃10cm in the total freeboard obtained. Other input parameters have a smaller influence on the overall mean total freeboard but can cause large regional differences. These results, together with published ICESat elevation precision and accuracy, suggest that three times the mean per gridcell single-laser-shot error budget can be used as an estimate for freeboard uncertainty. Theoretical relative ice thickness uncertainty ranges between 20% and 80% for typical freeboard and snow properties. Ice thickness is computed from total freeboard using Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow depth data. Average ice thickness for the Weddell Sea is 1.73 ± 0.38 m for ICESat measurements from 2004 to 2006, in agreement with previous work. The mean uncertainty is 0.72 ± 0.09 m. Our comparison with data of an alternative approach, which assumes that sea-ice freeboard is zero and that total freeboard equals snow depth, reveals an average sea-ice thickness difference of ∼0.77m.

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

scholarly journals Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

2006 ◽  
Vol 44 ◽  
pp. 281-287 ◽  
Author(s):  
Shotaro Uto ◽  
Haruhito Shimoda ◽  
Shuki Ushio
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Snow Depth ◽  
East Antarctica ◽  
Ice Thickness ◽  
First Year ◽  
Total Thickness ◽  
Sea Ice Thickness ◽  
Landfast Ice ◽  
Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

KuKa altimeter mode data gathered during MOSAiC: scattering from snow covered sea ice and snow depth determination using dual-frequency and polarimetric approaches

2021 ◽  
Author(s):  
Rosemary Willatt ◽  
Julienne Stroeve ◽  
Vishnu Nandan ◽  
Rasmus Tonboe ◽  
Stefan Hendricks ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Field Measurements ◽  
Dual Band ◽  
Altimetry Data ◽  
Dual Frequency ◽  
Sea Ice Thickness ◽  
Ka Band ◽  
Snow And Ice

<p>Retrieving the thickness of sea ice, and its snow cover, on long time- and length-scales is critical for studying climate. Satellite altimetry has provided estimations of sea ice thickness spanning nearly three decades, and more recently altimetry techniques have provided estimations of snow depth, using dual-band satellite altimetry data. These approaches are based on assumptions about the main scattering surfaces of the radiation. The dominant scattering surface is often assumed to be the snow/ice interface at Ku-band frequencies and the air/snow interface at Ka-band and laser frequencies. It has previously been shown that these assumptions do not always hold, but field data to investigate the dominant scattering surfaces and investigate how these relate to the physical snow and ice characteristics were spatially and temporally limited. The MOSAiC expedition provided a unique opportunity to gather data using a newly-developed Ku- and Ka-band radar 'KuKa' deployed over snow-covered sea ice, along with coincident field measurements of snow and ice properties. We present transect data gathered with the instrument looking at nadir to demonstrate how the scattering characteristics vary spatially and temporally in the Ku- and Ka-bands, and discuss implications for interpretation of dual-frequency satellite radar altimetry data. We compare KuKa data with field measurements to demonstrate snow depth retrieval using Ku- and Ka-band data.</p>

scholarly journals Combined SMAP–SMOS thin sea ice thickness retrieval

2019 ◽  
Vol 13 (2) ◽  
pp. 675-691 ◽  
Author(s):  
Cătălin Paţilea ◽  
Georg Heygster ◽  
Marcus Huntemann ◽  
Gunnar Spreen
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
Brightness Temperature ◽  
Incidence Angle ◽  
Data Availability ◽  
Ice Thickness ◽  
Polar Regions ◽  
Sea Ice Thickness ◽  
Brightness Temperatures ◽  
Spatial Coverage

Abstract. The spaceborne passive microwave sensors Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) provide brightness temperature data in the L band (1.4 GHz). At this low frequency the atmosphere is close to transparent and in polar regions the thickness of thin sea ice can be derived. SMOS measurements cover a large incidence angle range, whereas SMAP observes at a fixed 40∘ incidence angle. By using brightness temperatures at a fixed incidence angle obtained directly (SMAP), or through interpolation (SMOS), thin sea ice thickness retrieval is more consistent as the incidence angle effects do not have to be taken into account. Here we transfer a retrieval algorithm for the thickness of thin sea ice (up to 50 cm) from SMOS data at 40 to 50∘ incidence angle to the fixed incidence angle of SMAP. The SMOS brightness temperatures (TBs) at a given incidence angle are estimated using empirical fit functions. SMAP TBs are calibrated to SMOS to provide a merged SMOS–SMAP sea ice thickness product. The new merged SMOS–SMAP thin ice thickness product was improved upon in several ways compared to previous thin ice thickness retrievals. (i) The combined product provides a better temporal and spatial coverage of the polar regions due to the usage of two sensors. (ii) The radio frequency interference (RFI) filtering method was improved, which results in higher data availability over both ocean and sea ice areas. (iii) For the intercalibration between SMOS and SMAP brightness temperatures the root mean square difference (RMSD) was reduced by 30 % relative to a prior attempt. (iv) The algorithm presented here allows also for separate retrieval from any of the two sensors, which makes the ice thickness dataset more resistant against failure of one of the sensors. A new way to estimate the uncertainty of ice thickness retrieval was implemented, which is based on the brightness temperature sensitivities.

scholarly journals Freeboard height and snow depth observed by floating GPS on land-fast sea ice in Nella Fjord, Antarctica

2020 ◽  
Vol 61 (82) ◽  
pp. 227-239
Author(s):  
Qingchuan Zhang ◽  
Fei Li ◽  
Jintao Lei ◽  
Shengkai Zhang ◽  
Zhuoming Ding ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Single Point ◽  
Pressure Gauge ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Zhongshan Station ◽  
Ocean Topography ◽  
Bottom Pressure Gauge ◽  
The Relationship

AbstractAlthough altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.

The impact of snow products on detecting trends in sea ice thickness during the CryoSat-2 era

2020 ◽  
Author(s):  
Heidi Sallila ◽  
Samantha Buzzard ◽  
Eero Rinne ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Age ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Seasonal Snow ◽  
Uncertainty Estimates ◽  
The One ◽  
The Impact ◽  
First Time

<p>Retrieval of sea ice depth from satellite altimetry relies on knowledge of snow depth in the conversion of freeboard measurements to sea ice thickness. This remains the largest source of uncertainty in calculating sea ice thickness. In order to go beyond the use of a seasonal snow climatology, namely the one by Warren created from measurements collected during the drifting stations in 1937 and 1954–1991, we have developed as part of an ESA Arctic+ project several novel snow on sea ice pan-Arctic products, with the ultimate goal to resolve for the first time inter-annual and seasonal snow variability.</p><p><span>Our products are inter-compared and calibrated with each other to guarantee multi-decadal continuity, and also compared with other recently developed snow on sea ice modelling </span><span>and satellite based </span><span>products. Quality assessment and uncertainty estimates are provided at a gridded level and as a function of sea ice cover characteristics such as sea ice age, and sea ice type.</span></p><p>We investigate the impact of the spatially and temporally varying snow products on current satellite estimates of sea ice thickness and provide an update on the sea ice thickness uncertainties. We pay particular attention to potential biases of the seasonal ice growth and inter-annual trends.</p>

scholarly journals The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

2015 ◽  
Vol 9 (1) ◽  
pp. 37-52 ◽  
Author(s):  
S. Kern ◽  
K. Khvorostovsky ◽  
H. Skourup ◽  
E. Rinne ◽  
Z. S. Parsakhoo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Thickness ◽  
Data Sets ◽  
Snow Density ◽  
Radar Altimetry ◽  
Sea Ice Thickness ◽  
The Mean ◽  
Satellite Radar ◽  
Satellite Radar Altimetry

Abstract. We assess different methods and input parameters, namely snow depth, snow density and ice density, used in freeboard-to-thickness conversion of Arctic sea ice. This conversion is an important part of sea ice thickness retrieval from spaceborne altimetry. A data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and co-locate observations of total (sea ice + snow) and sea ice freeboard from the Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) airborne campaigns, of sea ice draft from moored and submarine upward looking sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer (AMSR-E) and the Warren climatology (Warren et al., 1999). We compare the different data sets in spatiotemporal scales where satellite radar altimetry yields meaningful results. An inter-comparison of the snow depth data sets emphasizes the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. We test different freeboard-to-thickness and freeboard-to-draft conversion approaches. The mean observed ULS sea ice draft agrees with the mean sea ice draft derived from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the approaches are able to reproduce the seasonal cycle in sea ice draft observed by moored ULS. A sensitivity analysis of the freeboard-to-thickness conversion suggests that sea ice density is as important as snow depth.

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