scholarly journals Direct helicopter EM — Sea-ice thickness inversion assessed with synthetic and field data

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. F127-F137 ◽  
Author(s):  
Andreas Pfaffling ◽  
Christian Haas ◽  
James E. Reid
Keyword(s):  
Sea Ice ◽  
Field Data ◽  
Flying Height ◽  
Small Scale ◽  
Ice Thickness ◽  
Systematic Bias ◽  
Sea Ice Thickness ◽  
Instrumental Noise ◽  
Precision And Accuracy ◽  
Better Than

Accuracy and precision of helicopter electromagnetic (HEM) sounding are the essential parameters for HEM sea-ice thickness profiling. For sea-ice thickness research, the quality of HEM ice thickness estimates must be better than [Formula: see text] to detect potential climatologic thickness changes. We introduce and assess a direct, 1D HEM data inversion algorithm for estimating sea-ice thickness. For synthetic quality assessment, an analytically determined HEM sea-ice thickness sensitivity is used to derive precision and accuracy. Precision is related directly to random, instrumental noise, although accuracy is defined by systematic bias arising from the data processing algorithm. For the in-phase component of the HEM response, sensitivity increases with frequency and coil spacing, but decreases with flying height. For small-scale HEM instruments used in sea-ice thickness surveys, instrumental noise must not exceed [Formula: see text] to reach ice thickness precision of [Formula: see text] at 15-m nominal flying height. Comparable precision is yielded at 30-m height for conventional exploration HEM systems with bigger coil spacings. Accuracy losses caused by approximations made for the direct inversion are negligible for brackish water and remain better than [Formula: see text] for saline water. Synthetic precision and accuracy estimates are verified with drill-hole validated field data from East Antarctica, where HEM-derived level-ice thickness agrees with drilling results to within 4%, or [Formula: see text].

Early detection of the Weddell polynya re-opening using SAR imagery

2020 ◽  
Author(s):  
Adriano Lemos ◽  
Céline Heuzé
Keyword(s):  
Early Detection ◽  
Sea Ice ◽  
Open Water ◽  
Weddell Sea ◽  
Small Scale ◽  
Ice Thickness ◽  
Sar Images ◽  
Sea Ice Thickness ◽  
Sar Imagery ◽  
Microwave Sensors

<p>The sea ice thickness in the Weddell Sea during the austral winter normally exceeds 1 m, but in the case of a polynya, this thickness decreases to 10 cm or less. There are two theories as to why the Weddell Polynya opens: 1) comparatively warm oceanic water upwelling from its nominal depth of several hundred metres to the surface where it melts the sea ice from underneath; or 2) opening of a lead by a passing storm, lead which will then be maintained open either by the atmosphere or ocean and grow. The objective of this study is to estimate how long in advance the recent Weddell Polynya opening could have been detected by synthetic aperture radar (SAR) images due to the decrease of the sea ice thickness and/or early appearance of leads. We use high temporal and spatial resolution SAR images from the Sentinel-1 constellation (C-band) and ALOS2 (L-band) during the austral winters 2014-2018. We use an adapted version of the algorithm developed by Aldenhoff et al. (2018) to monitor changes in sea ice thickness over the polynya region. The algorithm detects the transition of the sea ice thickness through changes in small scale surface roughness and thus reduced backscatter, and allowing us to distinguish three different categories: ice, thin ice, and open water. The transition from ice to thin ice and then to open water indicates that the polynya is melted from under, whereas a direct transition from ice to open water will reveal leads. The high resolution and good coverage of the SAR imagery, and a combined effort of different satellites sensors (e.g. infrared and microwave sensors), opens the possibility of an early detection of Weddell Polynya opening.</p>

scholarly journals Toward a method for downscaling sea ice pressure

2020 ◽  
Author(s):  
Jean-Francois Lemieux ◽  
Bruno Tremblay ◽  
Mathieu Plante
Keyword(s):  
High Pressure ◽  
Sea Ice ◽  
Numerical Experiments ◽  
Large Scale ◽  
High Stress ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
High Stress Concentration ◽  
Ice Pressure

Abstract. Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Contemporary large-scale sea ice forecasting systems can predict the evolution of sea ice pressure. There is, however, a mismatch between the spatial resolution of these systems (a few km) and the typical dimensions of ships (a few tens of m) navigating in ice-covered regions. In this paper, we investigate the downscaling of sea ice pressure from the km-scale to scales relevant for ships. Results show that sub-grid scale pressure values can be significantly larger than the large-scale pressure (up to $\\sim$ 4x larger in our numerical experiments). High pressure at the sub-grid scale is associated with the presence of defects (e.g. a lead). Numerical experiments show that a ship creates its own high stress concentration by forming a lead in its wake while navigating. These results also highlight the difficulty of forecasting the small-scale distribution of pressure and especially the largest values. Indeed, this distribution strongly depends on variables that are not well constrained: the rheology parameters and the small-scale structure of sea ice thickness (more importantly the length of the lead behind the ship).

scholarly journals Small-scale horizontal variability of snow, sea-ice thickness and freeboard in the first-year ice region north of Svalbard

2013 ◽  
Vol 54 (62) ◽  
pp. 261-266 ◽  
Author(s):  
Jari Haapala ◽  
Mikko Lensu ◽  
Marie Dumont ◽  
Angelika H.H. Renner ◽  
Mats A. Granskog ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Sea Ice ◽  
Snow Accumulation ◽  
Small Scale ◽  
Ice Thickness ◽  
First Year ◽  
Sea Ice Thickness ◽  
The Mean ◽  
Ridged Ice ◽  
Snow Thickness

AbstractVariability of sea-ice and snow conditions on the scale of a few hundred meters is examined using in situ measurements collected in first-year pack ice in the European Arctic north of Svalbard. Snow thickness and surface elevation measurements were performed in the standard manner using a snow stick and a rotating laser. Altogether, 4109 m of measurement lines were surveyed. The snow loading was large, and in many locations the ice freeboard was negative (38.8% of snowline measurements), although the modal ice and snow thickness was 1.8 m. The mean of all the snow thickness measurements was 36 cm, with a standard deviation of 26 cm. The mean freeboard was only 3 cm, with a standard deviation of 23 cm. There were noticeable differences in snow thickness among the measurement sites. Over the undeformed ice areas, the mean snow thickness and freeboard were 23 and 2.4 cm, respectively. Over the ridged ice areas, the mean freeboard was only –0.3 cm due to snow accumulation on the sails of ridges (average thickness 54 cm). These findings imply that retrieval algorithms for converting freeboard to ice thickness should take account of spatial variability of snow cover.

scholarly journals Estimating Early-Winter Antarctic Sea Ice Thickness From Deformed Ice Morphology

2019 ◽  
Author(s):  
M. Jeffrey Mei ◽  
Ted Maksym ◽  
Hanumant Singh
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Relative Error ◽  
Snow Depth ◽  
Large Scale ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Mean Relative Error ◽  
Antarctic Sea Ice

Abstract. Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness can be estimated from surface elevation measurements, such as those from airborne/satellite LiDAR, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic sea ice thickness have errors as high as ~ 50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate sea ice thickness at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate sea ice thickness at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ~ 15 %). Moreover, the neural network does not require specifying snow depth/density, which increases its potential applications to other LiDAR datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season, which is much lower than the mean relative error for a linear fit (errors up to 47 %). This method may be extended to lower-resolution, larger-footprint data such as such as IceBridge, and suggests a possible avenue to reduce errors in satellite estimates of Antarctic sea ice thickness from ICESat-2 over current methods, especially at smaller scale.

scholarly journals Evaluation of AMSR-E Thin Ice Thickness Algorithm from a Mooring-Based Observation: How Can the Satellite Observe a Sea Ice Field with Nonuniform Thickness Distribution?

2019 ◽  
Vol 36 (8) ◽  
pp. 1623-1641 ◽  
Author(s):  
Haruhiko Kashiwase ◽  
Kay I. Ohshima ◽  
Yasushi Fukamachi ◽  
Sohey Nihashi ◽  
Takeshi Tamura
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Thickness Distribution ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Coastal Polynya ◽  
Ice Floes ◽  
Ice Production

AbstractThe quantification of sea ice production in coastal polynyas is a key issue to understand the global climate system. In this study, we directly compared Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data with the sea ice thickness distribution obtained from a mooring observation during the winter of 2003 off Sakhalin in the Sea of Okhotsk to evaluate the algorithm for estimation of sea ice thickness in coastal polynyas. By using thermal ice thickness as a target physical quantity, we found that the obtained relationship between the polarization ratio (PR) and ice thickness can provide an appropriate AMSR-E algorithm to estimate thin ice thickness, irrespective of the uniform or nonuniform ice thickness field. The relationship between the PR value and thermal ice thickness is likewise consistent with the local PR–thickness relationship that is observed at individual ice floes. This is because both the PR value and thermal ice thickness are more sensitive to thinner ice. Furthermore, we evaluated the method for detection of active frazil in a coastal polynya by comparing with the mooring data, and subsequently modified it to classify the coastal polynya into three thin ice types, namely, active frazil, thin solid ice, and mixed ice (mixture of active frazil and thin solid ice). The improved algorithm successfully represents the thermal ice thickness even for a relatively small-scale polynya off Sakhalin and is expected to be useful for better quantification of sea ice production in the global ocean owing to its high versatility.

scholarly journals Improved Multimodel Superensemble Forecast for Sea Ice Thickness using Global Climate Models

2020 ◽  
Author(s):  
Wang Yangjun ◽  
Liu Kefeng ◽  
Zhang Ren ◽  
Qian Longxia ◽  
Zhang Yu
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Ensemble Methods ◽  
Global Climate Models ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Advanced Method ◽  
Multi Criteria Evaluation ◽  
Better Than

Abstract. This paper aims to find a possible ensemble method to combine the global climate models, providing an accuracy forecast of sea ice thickness. Conventional multimodel superensemble, the advanced method that is widely used in atmosphere, ocean and other fields, cannot be well performed in sea ice thickness simulation. Hence, an adaptive forecasting through exponential re-weighting (AFTER) algorithm is adopted to improve the conventional multimodel superensemble. Results show our proposed methods perform better than any other mainstream ensemble methods by using a multi-criteria evaluation. The proposed method is used to predict the future sea ice thickness in the period of 2020–2049, where the possible biases are discussed.

scholarly journals Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness

2021 ◽  
Vol 15 (10) ◽  
pp. 4909-4927
Author(s):  
Isolde A. Glissenaar ◽  
Jack C. Landy ◽  
Alek A. Petty ◽  
Nathan T. Kurtz ◽  
Julienne C. Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
The Arctic ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
Stage Of Development ◽  
Spring Sea Ice

Abstract. In the Arctic, multi-year sea ice is being rapidly replaced by seasonal sea ice. Baffin Bay, situated between Greenland and Canada, is part of the seasonal ice zone. In this study, we present a long-term multi-mission assessment (2003–2020) of spring sea ice thickness in Baffin Bay from satellite altimetry and sea ice charts. Sea ice thickness within Baffin Bay is calculated from Envisat, ICESat, CryoSat-2, and ICESat-2 freeboard estimates, alongside a proxy from the ice chart stage of development that closely matches the altimetry data. We study the sensitivity of sea ice thickness results estimated from an array of different snow depth and snow density products and methods for redistributing low-resolution snow data onto along-track altimetry freeboards. The snow depth products that are applied include a reference estimated from the Warren climatology, a passive microwave snow depth product, and the dynamic snow scheme SnowModel-LG. We find that applying snow depth redistribution to represent small-scale snow variability has a considerable impact on ice thickness calculations from laser freeboards but was unnecessary for radar freeboards. Decisions on which snow loading product to use and whether to apply snow redistribution can lead to different conclusions on trends and physical mechanisms. For instance, we find an uncertainty envelope around the March mean sea ice thickness of 13 % for different snow depth/density products and redistribution methods. Consequently, trends in March sea ice thickness from 2003–2020 range from −23 to 17 cm per decade, depending on which snow depth/density product and redistribution method is applied. Over a longer timescale, since 1996, the proxy ice chart thickness product has demonstrated statistically significant thinning within Baffin Bay of 7 cm per decade. Our study provides further evidence for long-term asymmetrical trends in Baffin Bay sea ice thickness (with −17.6 cm per decade thinning in the west and 10.8 cm per decade thickening in the east of the bay) since 2003. This asymmetrical thinning is consistent for all combinations of snow product and processing method, but it is unclear what may have driven these changes.

scholarly journals Estimating early-winter Antarctic sea ice thickness from deformed ice morphology

2019 ◽  
Vol 13 (11) ◽  
pp. 2915-2934 ◽  
Author(s):  
M. Jeffrey Mei ◽  
Ted Maksym ◽  
Blake Weissling ◽  
Hanumant Singh
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Large Scale ◽  
Field Studies ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Potential Applications

Abstract. Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimated from snow freeboard measurements, such as those from airborne/satellite lidar, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic SIT have errors as high as ∼50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate SIT at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate SIT at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ∼15 %). Moreover, the neural network does not require specification of snow depth or density, which increases its potential applications to other lidar datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season. This method may be extended to lower-resolution, larger-footprint data such as such as Operation IceBridge, and it suggests a possible avenue to reduce errors in satellite estimates of Antarctic SIT from ICESat-2 over current methods, especially at smaller scales.

scholarly journals Airborne electromagnetic footprints in 1D earths

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. G63-G72 ◽  
Author(s):  
James E. Reid ◽  
Andreas Pfaffling ◽  
Julian Vrbancich
Keyword(s):  
Sea Ice ◽  
Frequency Domain ◽  
Half Space ◽  
Field Data ◽  
Inductive Limit ◽  
Thin Sheet ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Flight Height ◽  
Airborne Electromagnetic

Existing estimates of footprint size for airborne electromagnetic (AEM) systems have been based largely on the inductive limit of the response. We present calculations of frequency-domain, AEM-footprint sizes in infinite-horizontal, thin-sheet, and half-space models for the case of finite frequency and conductivity. In a half-space the original definition of the footprint is extended to be the side length of the cube with its top centered below the transmitter that contains the induced currents responsible for 90% of the secondary field measured at the receiver. For a horizontal, coplanar helicopter frequency-domain system, the in-phase footprint for induction numbers less than 0.4 (thin sheet) or less than 0.6 (half-space) increases from around 3.7 times the flight height at the inductive limit to more than 10 times the flight height. For a vertical-coaxial system the half-space footprint exceeds nine times the flight height for induction numbers less than 0.09. For all models, geometries, and frequencies, the quadrature footprint is approximately half to two-thirds that of the in-phase footprint. These footprint estimates are supported by 3D model calculations that suggest resistive targets must be separated by the footprint dimension for their individual anomalies to be resolved completely. Analysis of frequency-domain AEM field data acquired for antarctic sea-ice thickness measurements supports the existence of a smaller footprint for the quadrature component in comparison with the in-phase, but the effect is relatively weak. In-phase and quadrature footprints estimated by comparing AEM to drillhole data are considerably smaller than footprints from 1D and 3D calculations. However, we consider the footprints estimated directly from field data unreliable since they are based on a drillhole data set that did not adequately define the true, 3D, sea-ice thickness distribution around the AEM flight line.

scholarly journals Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay sea ice thickness

2021 ◽  
Author(s):  
Isolde A. Glissenaar ◽  
Jack C. Landy ◽  
Alek A. Petty ◽  
Nathan T. Kurtz ◽  
Julienne C. Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
The Arctic ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
Stage Of Development ◽  
Spring Sea Ice

Abstract. In the Arctic, multi-year sea ice is being rapidly replaced by seasonal sea ice. Baffin Bay, situated between Greenland and Canada, is part of the seasonal ice zone. In this study, we present a long-term multi-mission assessment (2003–2020) of spring sea ice thickness in Baffin Bay from satellite altimetry and sea ice charts. Sea ice thickness within Baffin Bay is calculated from Envisat, ICESat, CryoSat-2 and ICESat-2 freeboard estimates, alongside a proxy from the ice chart stage of development that closely matches the altimetry data. We study the sensitivity of sea ice thickness results estimated from an array of different snow depth and snow density products and methods for redistributing low resolution snow data onto along-track altimetry freeboards. The snow depth products that are applied include a reference estimated from the Warren climatology, a passive microwave snow depth product, and the dynamic snow scheme SnowModel-LG. We find that applying snow depth redistribution to represent small-scale snow variability has a considerable impact on ice thickness calculations from laser freeboards but was unnecessary for radar freeboards. Decisions on which snow loading product to use and whether to apply snow redistribution can lead to different conclusions on trends and physical mechanisms. For instance, we find an uncertainty envelope around the March mean sea ice thickness of 13 % for different snow depth/density products and redistribution methods. Consequently, trends in March sea ice thickness from 2003–2020 range from −23 cm/dec to 17 cm/dec, depending on which snow depth/density product and redistribution method is applied. Over a longer timescale, since 1996, the proxy ice chart thickness product demonstrates statistically significant thinning within Baffin Bay of 7 cm/dec. Our study provides further evidence for long-term asymmetrical trends in Baffin Bay sea ice thickness (with −17.6 cm/dec thinning in the west and 10.8 cm/dec thickening in the east of the bay) since 2003. This asymmetrical thinning is consistent for all combinations of snow product and processing method, but it is unclear what may have driven these changes.

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