In-situ automatic observations of ice thickness of seas

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

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Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

Remote Sensing ◽

10.3390/rs12040650 ◽

2020 ◽

Vol 12 (4) ◽

pp. 650

Author(s):

Pablo Sánchez-Gámez ◽

Carolina Gabarro ◽

Antonio Turiel ◽

Marcos Portabella

Keyword(s):

Soil Moisture ◽

Sea Ice ◽

Brightness Temperature ◽

European Space Agency ◽

Ground Truth ◽

Ice Thickness ◽

Sea Ice Thickness ◽

In Situ Data ◽

L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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

10.5194/tc-2018-92 ◽

2018 ◽

Cited By ~ 1

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.

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Remote sensing of sea ice: advances during the DAMOCLES project

The Cryosphere ◽

10.5194/tc-6-1411-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1411-1434 ◽

Cited By ~ 19

Author(s):

G. Heygster ◽

V. Alexandrov ◽

G. Dybkjær ◽

W. von Hoyningen-Huene ◽

F. Girard-Ardhuin ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Estimation Method ◽

Satellite Observations ◽

Microwave Emission ◽

The Arctic ◽

Ice Thickness ◽

In Situ Observation ◽

Microwave Emissivity

Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

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Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

10.5194/egusphere-egu2020-11436 ◽

2020 ◽

Author(s):

Alex Cabaj ◽

Paul Kushner ◽

Alek Petty ◽

Stephen Howell ◽

Christopher Fletcher

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Arctic Ocean ◽

Ice Model

Snow on Arctic sea ice plays multiple&#8212;and sometimes contrasting&#8212;roles in several feedbacks between sea ice and the global climate system. For example, the presence of snow on sea ice may mitigate sea ice melt by increasing the sea ice albedo and enhancing the ice-albedo feedback. Conversely, snow can inhibit sea ice growth by insulating the ice from the atmosphere during the sea ice growth season. In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. In particular, snow contributes to uncertainties in retrievals of sea ice thickness from satellite altimetry measurements, such as those from ICESat-2. Snow-on-sea-ice models can produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are absent over most of the Arctic Ocean, so it can be difficult to determine which reanalysis snowfall product is best suited to be used as input for a snow-on-sea-ice model.In the absence of in-situ snowfall rate measurements, measurements from satellite instruments can be used to quantify snowfall over the Arctic Ocean. The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rates can be retrieved. This instrument provides the most extensive high-latitude snowfall rate observation dataset currently available. CloudSat&#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&#176;N, with a 16-day repeat cycle, over the time period from 2006-2016.We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM when different reanalysis inputs are used. In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.

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Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

Annals of Glaciology ◽

10.1017/aog.2017.37 ◽

2017 ◽

Vol 59 (76pt2) ◽

pp. 163-172 ◽

Cited By ~ 1

Author(s):

Anja Rösel ◽

Jennifer King ◽

Anthony P. Doulgeris ◽

Penelope M. Wagner ◽

A. Malin Johansson ◽

...

Keyword(s):

Sea Ice ◽

Satellite Data ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Remotely Sensed Data ◽

Sea Ice Thickness ◽

In Situ Data ◽

Ice Conditions ◽

Area North

ABSTRACTKnowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

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Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

The Cryosphere ◽

10.5194/tc-13-1409-2019 ◽

2019 ◽

Vol 13 (4) ◽

pp. 1409-1422

Author(s):

Daniel Price ◽

Iman Soltanzadeh ◽

Wolfgang Rack ◽

Ethan Dale

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Snow Accumulation ◽

In Situ Measurements ◽

Ice Thickness ◽

Growth Season ◽

Mcmurdo Sound ◽

Sea Ice Thickness ◽

Antarctic Sea 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 the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound 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 in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the 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 what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

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Estimation of thin Sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica

Annals of Glaciology ◽

10.3189/172756406781811745 ◽

2006 ◽

Vol 44 ◽

pp. 269-274 ◽

Cited By ~ 17

Author(s):

Takeshi Tamura ◽

Kay I. Ohshima ◽

Hiroyuki Enomoto ◽

Kazutaka Tateyama ◽

Atsuhiro Muto ◽

...

Keyword(s):

Heat Flux ◽

Sea Ice ◽

Meteorological Data ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Flux Calculation ◽

Ice Surface ◽

Wilkes Land ◽

Ice Surface Temperature

AbstractAntarctic coastal polynyas are major areas of intense ocean–atmosphere heat and moisture flux, and associated high Sea-ice production and dense-water formation. Their accurate detection, including an estimate of thin ice thickness, is therefore very important. In this paper, we apply a technique originally developed in the Arctic to an estimation of Sea-ice thickness using Us National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data and meteorological data in the Vincennes Bay polynya off Wilkes Land, East Antarctica. The method is based upon the heat-flux calculation using Sea-ice Surface temperature estimates from the Satellite thermal-infrared data combined with global objective analysis (European Centre for Medium-Range Weather Forecasts (ECMWF)) data. The validity of this method is assessed by comparing results with independent ice-surface temperature and ice-thickness data obtained during an Australian-led research cruise to the region in 2003. In thin-ice (polynya) regions, ice thicknesses estimated by the heat-flux calculation using AVHRR and ECMWF data Show reasonable agreement with those estimated by (a) applying the heat-flux calculation to in Situ radiation thermometer and meteorological data and (b) in Situ observations. The Standard deviation of the difference between the AVHRR-derived and in Situ data is ∽0.02 m. Comparison of the AVHRR ice-thickness retrievals with coincident Satellite passive-microwave polarization ratio data confirms the potential of the latter as a means of deriving maps of thin Sea-ice thickness on the wider Scale, uninterrupted by darkness and cloud cover.

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Sea ice thickness retrieval algorithms based on in situ surface elevation and thickness values for application to altimetry

Journal of Geophysical Research Oceans ◽

10.1002/jgrc.20252 ◽

2013 ◽

Vol 118 (8) ◽

pp. 3807-3822 ◽

Cited By ~ 22

Author(s):

Burcu Ozsoy-Cicek ◽

Stephen Ackley ◽

Hongjie Xie ◽

Donghui Yi ◽

Jay Zwally

Keyword(s):

Sea Ice ◽

Surface Elevation ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Retrieval Algorithms

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Retrieval of sea ice thickness during melt season from in situ, airborne and satellite imagery

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2016.7731002 ◽

2016 ◽

Cited By ~ 5

Author(s):

Larysa Istomina ◽

Christian Melsheimer ◽

Marcus Huntemann ◽

Marcel Nicolaus ◽

Georg Heygster

Keyword(s):

Sea Ice ◽

Satellite Imagery ◽

Ice Thickness ◽

Sea Ice Thickness

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In situ measurements of the direct-current conductivity of Antarctic Sea ice: implications for airborne electromagnetic Sounding of Sea-ice thickness

Annals of Glaciology ◽

10.3189/172756406781811772 ◽

2006 ◽

Vol 44 ◽

pp. 217-223 ◽

Cited By ~ 15

Author(s):

J.E. Reid ◽

A. Pfaffling ◽

A.P. Worby ◽

J.R. Bishop

Keyword(s):

Sea Ice ◽

Direct Current ◽

Sea Water ◽

Vertical Direction ◽

In Situ Measurements ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Direct Current Conductivity

AbstractAirborne, Ship-borne and Surface low-frequency electromagnetic (EM) methods have become widely applied to measure Sea-ice thickness. EM responses measured over Sea ice depend mainly on the Sea-water conductivity and on the height of the Sensor above the Sea-ice–sea-water interface, but may be Sensitive to the Sea-ice conductivity at high excitation frequencies. We have conducted in Situ measurements of direct-current conductivity of Sea ice using Standard geophysical geoelectrical methods. Sea-ice thickness estimated from the geoelectrical Sounding data was found to be consistently underestimated due to the pronounced vertical-to-horizontal conductivity anisotropy present in level Sea ice. At five Sites, it was possible to determine the approximate horizontal and vertical conductivities from the Sounding data. The average horizontal conductivity was found to be 0.017 Sm–1, and that in the vertical direction to be 9–12 times higher. EM measurements over level Sea ice are Sensitive only to the horizontal conductivity. Numerical modelling has Shown that the assumption of zero Sea-ice conductivity in interpretation of airborne EM data results in a negligible error in interpreted thickness for typical level Antarctic Sea ice.

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