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

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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In-situ automatic observations of ice thickness of seas

Metrology and Measurement Systems ◽

10.2478/v10178-012-0051-6 ◽

2012 ◽

Vol 19 (3) ◽

pp. 583-592 ◽

Cited By ~ 1

Author(s):

Yinke Dou ◽

Xiaomin Chang

Keyword(s):

Sea Ice ◽

New Technologies ◽

Capacitive Sensor ◽

Automatic Monitoring ◽

Ice Thickness ◽

In Situ Observation ◽

Glacier Surface ◽

Sea Ice Thickness ◽

Surface Ice

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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Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

Ocean Science Discussions ◽

10.5194/osd-9-1009-2012 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1009-1043 ◽

Cited By ~ 1

Author(s):

G. Dybkjær ◽

R. Tonboe ◽

J. Høyer

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Temperature Data ◽

Coupled Systems ◽

The Arctic ◽

In Situ Observation ◽

Clear Sky ◽

Ice Surface ◽

Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions are prevailing during spring in the Arctic while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveal that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measures complicates the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

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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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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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Sea Ice in the Greenland Polynya in 2018 - A Study with CryoSat-2 and SMOS

10.5194/egusphere-egu2020-5495 ◽

2020 ◽

Author(s):

Weixin Zhu ◽

Lu Zhou ◽

Shiming Xu

Keyword(s):

Sea Ice ◽

Global Climate ◽

Arctic Sea Ice ◽

Climate System ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The North ◽

Ice Surface ◽

Snow Conditions

AbstractArctic sea ice is a critical component in the global climate system. It affects the climate system by radiating incident heat back into space and regulating ocean-atmosphere heat and momentum. Satellite altimetry such as CryoSat-2 serves as the primary approach for observing sea ice thickness. Nevertheless, the thickness retrieval with CryoSat-2 mainly depends on the height of the ice surface above the sea level, which leads to significant uncertainties over thin ice regimes. The sea ice at the north of Greenland is considered one of the oldest and thickest in the Arctic. However, during late February - early March 2018, a polynya formed north to Greenland due to extra strong southern winds. We focus on the retrieval of sea ice thickness and snow conditions with CryoSat-2 and SMOS during the formation of the polynya. Specifically, we investigate the uncertainty of CryoSat-2 and carry out inter- comparison of sea ice thickness retrieval with SMOS and CryoSat-2/SMOS synergy. Besides, further discussion of retrieval with CryoSat-2 is provided for such scenarios where the m&#233;lange of thick ice and newly formed thin ice is present.

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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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Sea-ice thickness variability in Storfjorden, Svalbard

Annals of Glaciology ◽

10.3189/172756411795931561 ◽

2011 ◽

Vol 52 (57) ◽

pp. 61-68 ◽

Cited By ~ 16

Author(s):

S. Hendricks ◽

S. Gerland ◽

L.H. Smedsrud ◽

C. Haas ◽

A.A. Pfaffhuber ◽

...

Keyword(s):

Sea Ice ◽

Barents Sea ◽

Meteorological Data ◽

Ice Thickness ◽

Central Arctic Ocean ◽

Reanalysis Dataset ◽

Sea Ice Thickness ◽

Thickness Variability ◽

Fast Ice ◽

Thickness Variations

AbstractResults from electromagnetic induction surveys of sea-ice thickness in Storfjorden, Svalbard, reveal large interannual ice-thickness variations in a region which is typically characterized by a reoccurring polynya. the surveys were performed in March 2003, May 2006 and March 2007 with helicopter- and ship-based sensors. the thickness distributions are influenced by sea-ice and atmospheric boundary conditions 2 months prior to the surveys, which are assessed with synthetic aperture radar (SAR) images, regional QuikSCAT backscatter maps and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset. Locally formed thin ice from the Storfjorden polynya was frequently observed in 2003 and 2007 (mean thickness 0.55 and 0.37 m, respectively) because these years were characterized by prevailing northeasterly winds. In contrast, the entire fjord was covered with thick external sea ice in 2006 (mean thickness 2.21 m), when ice from the Barents Sea was driven into the fjord by predominantly southerly winds. the modal thickness of this external ice in 2006 increased from 1.2m in the northern fjord to 2.4 m in the southern fjord, indicating stronger deformation in the southern part. This dynamically thickened ice was even thicker than multi-year ice advected from the central Arctic Ocean in 2003 (mean thickness 1.83 m). the thermodynamic ice thickness of fast ice as boundary condition is investigated with a one-dimensional sea-ice growth model (1DICE) forced with meteorological data from the weather station at the island of Hopen, southeast of Storfjorden. the model results are in good agreement with the modal thicknesses of fast-ice measurements in all years.

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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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