scholarly journals Determination of areal surface-feature coverage in the Beaufort Sea using aircraft video data

1997 ◽  
Vol 25 ◽  
pp. 434-438 ◽  
Author(s):  
Mark A. Tschudi ◽  
Judith A. Curry ◽  
James A. Maslanik
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Video Camera ◽  
Surface Feature ◽  
Video Data ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Areal Coverage ◽  
Melt Ponds

The surface-energy budget of the Arctic Ocean depends on the distribution of various sea-ice features that form by both mechanical and thermodynamic processes. Melt ponds, new ice and open water greatly affect the determination of surface albedo. However, even basic measurements of some surface-feature characteristics, such as areal extent of melt ponds, remain rare.A method has been developed to assess the areal coverage of melt ponds, new ice and open water using video data from the Beaufort and Arctic Storms Experiment (BASE). A downward-looking video camera mounted on the underside of a Hercules C-130 aircraft provided clear images of the surface. Images acquired over multi-year ice on 21 September 1994 were analyzed using a spectral technique to determine the areal coverage of melt ponds, new ice and open water. Statistics from this analysis were then compared to previous field studies and to the Schramm and others (in press) sea-ice model.

scholarly journals Determination of areal surface-feature coverage in the Beaufort Sea using aircraft video data

1997 ◽  
Vol 25 ◽  
pp. 434-438 ◽  
Author(s):  
Mark A. Tschudi ◽  
Judith A. Curry ◽  
James A. Maslanik
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Video Camera ◽  
Surface Feature ◽  
Video Data ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Areal Coverage ◽  
Melt Ponds

The surface-energy budget of the Arctic Ocean depends on the distribution of various sea-ice features that form by both mechanical and thermodynamic processes. Melt ponds, new ice and open water greatly affect the determination of surface albedo. However, even basic measurements of some surface-feature characteristics, such as areal extent of melt ponds, remain rare.A method has been developed to assess the areal coverage of melt ponds, new ice and open water using video data from the Beaufort and Arctic Storms Experiment (BASE). A downward-looking video camera mounted on the underside of a Hercules C-130 aircraft provided clear images of the surface. Images acquired over multi-year ice on 21 September 1994 were analyzed using a spectral technique to determine the areal coverage of melt ponds, new ice and open water. Statistics from this analysis were then compared to previous field studies and to the Schramm and others (in press) sea-ice model.

scholarly journals The impact of melt ponds on summertime microwave brightness temperatures and sea ice concentrations

2016 ◽  
Author(s):  
S. Kern ◽  
A. Rösel ◽  
L. T. Pedersen ◽  
N. Ivanova ◽  
R. Saldo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Water Fraction ◽  
Microwave Brightness Temperature ◽  
Ice Melt ◽  
Melt Ponds ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. The sea ice concentration (SIC) derived from satellite microwave brightness temperature (TB) data are known to be less accurate during summer melt conditions – in the Arctic Ocean primarily because of the impact of melt ponds on sea ice. Using data from June to August 2009, we investigate how TBs and SICs vary as a function of the ice surface fraction (ISF) computed from open water fraction and melt pond fraction both derived from satellite optical reflectance data. SIC is computed from TBs using a set of eight different retrieval algorithms and applying a consistent set of tie points. We find that TB values change during sea ice melt non-linearly and not monotonically as a function of ISF for ISF of 50 to 100 %. For derived parameters such as the polarization ratio at 19 GHz the change is monotonic but substantially smaller than theoretically expected. Changes in ice/snow radiometric properties during melt also contribute to the TB changes observed; these contributions are functions of frequency and polarization and have the potential to partly counter-balance the impact of changing ISF on the observed TBs. All investigated SIC retrieval algorithms overestimate ISF when using winter tie points. The overestimation varies among the algorithms as a function of ISF such that the SIC retrieval algorithms could be categorized into two different classes. These reveal a different degree of ISF overestimation at high ISF and an opposite development of ISF over-estimation as ISF decreases. For one class, correlations between SIC and ISF are ≥ 0.85 and the associated linear regression lines suggest an exploitable relationship between SIC and ISF if reliable summer sea ice tie points can be established. This study shows that melt ponds are interpreted as open water by the SIC algorithms, while the concentration of ice between the melt ponds is in general being overestimated. These two effects may cancel each other out and thus produce seemingly correct SIC for the wrong reasons. This cancelling effect will in general only be "correct" at one specific value of MPF. Based on our findings we recommend to not correct SIC algorithms for the impact of melt ponds as this seems to violate physical principles. Users should be aware that the SIC algorithms available at the moment retrieve a combined parameter presented by SIC in winter and ISF in summer.

scholarly journals Arctic sea-ice conditions and the distribution of solar radiation during summer

1997 ◽  
Vol 25 ◽  
pp. 445-450 ◽  
Author(s):  
Donald K. Perovich ◽  
Walter B. Tucker
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Melt Ponds ◽  
Arctic Ice ◽  
Median Area

Understanding the interaction of solar radiation with the ice cover is critical in determining the heat and mass balance of the Arctic ice pack, and in assessing potential impacts due to climate change. Because of the importance of the ice-albedo feedback mechanism, information on the surface state of the ice cover is needed. Observations of the surface slate of sea ice were obtained from helicopter photography missions made during the 1994 Arctic Ocean Section cruise. Photographs from one flight, taken during the height of the melt season (31 July 1994) at 76° N, 172° W, were analyzed in detail. Bare ice covered 82% of the total area, melt ponds 12%, and open water 6%, There was considerable variability in these area fractions on scales < 1 km2. Sample areas >2 3 km2gave representative values of ice concentration and pond fraction. Melt ponds were numerous, with a number density of 1800 ponds km-2. The melt ponds had a mean area of 62 m2a median area of 14 m2, and a size distribution that was well lit by a cumulative lognormal distribution. While leads make up only a small portion of the total area, they are the source of virtually all of the solar energy input to the ocean.

scholarly journals Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer

2015 ◽  
Vol 9 (1) ◽  
pp. 255-268 ◽  
Author(s):  
D. V. Divine ◽  
M. A. Granskog ◽  
S. R. Hudson ◽  
C. A. Pedersen ◽  
T. I. Karlsen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Surface Type ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3° N, from 26 July to 3 August 2012. A set of > 10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈ 0.29 and open-water fraction of ≈ 0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover.

scholarly journals Open Source Algorithm for Detecting Sea Ice Surface Features in High Resolution Optical Imagery

2017 ◽  
Author(s):  
Nicholas C. Wright ◽  
Christopher M. Polashenski
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Surface Coverage ◽  
Open Water ◽  
Training Data ◽  
The Arctic ◽  
Source Distribution ◽  
Observation Area ◽  
Melt Ponds ◽  
Optical Imagery

Abstract. Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available m- to dm-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: Snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open source distribution of this algorithm and associated training data sets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery.

scholarly journals Profiling Sea Ice with a Multiple Altimeter Beam Experimental Lidar (MABEL)

2014 ◽  
Vol 31 (5) ◽  
pp. 1151-1168 ◽  
Author(s):  
R. Kwok ◽  
T. Markus ◽  
J. Morison ◽  
S. P. Palm ◽  
T. A. Neumann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Laser Pulses ◽  
The Arctic ◽  
Ice Cloud ◽  
Ice Conditions ◽  
Arctic Coast ◽  
Background Photon ◽  
532 Nm

AbstractThe sole instrument on the upcoming Ice, Cloud, and Land Elevation Satellite (ICESat-2) altimetry mission is a micropulse lidar that measures the time of flight of individual photons from laser pulses transmitted at 532 nm. Prior to launch, the Multiple Altimeter Beam Experimental Lidar (MABEL) serves as an airborne implementation for testing and development. This paper provides a first examination of MABEL data acquired on two flights over sea ice in April 2012: one north of the Arctic coast of Greenland and the other in the east Greenland Sea. The phenomenology of photon distributions in the sea ice returns is investigated. An approach to locate the surface and estimate its elevation in the distributions is described, and its achievable precision is assessed. Retrieved surface elevations over relatively flat leads in the ice cover suggest that precisions of several centimeters are attainable. Restricting the width of the elevation window used in the surface analysis can mitigate potential biases in the elevation estimates due to subsurface returns at 532 nm. Comparisons of nearly coincident elevation profiles from MABEL with those acquired by an analog lidar show good agreement. Discrimination of ice and open water, a crucial step in the determination of sea ice freeboard and the estimation of ice thickness, is facilitated by contrasts in the observed signal–background photon statistics. Future flight paths will sample a broader range of seasonal ice conditions for further evaluation of the year-round profiling capabilities and limitations of the MABEL instrument.

scholarly journals Arctic sea-ice conditions and the distribution of solar radiation during summer

1997 ◽  
Vol 25 ◽  
pp. 445-450 ◽  
Author(s):  
Donald K. Perovich ◽  
Walter B. Tucker
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Melt Ponds ◽  
Arctic Ice ◽  
Median Area

Understanding the interaction of solar radiation with the ice cover is critical in determining the heat and mass balance of the Arctic ice pack, and in assessing potential impacts due to climate change. Because of the importance of the ice-albedo feedback mechanism, information on the surface state of the ice cover is needed. Observations of the surface slate of sea ice were obtained from helicopter photography missions made during the 1994 Arctic Ocean Section cruise. Photographs from one flight, taken during the height of the melt season (31 July 1994) at 76° N, 172° W, were analyzed in detail. Bare ice covered 82% of the total area, melt ponds 12%, and open water 6%, There was considerable variability in these area fractions on scales < 1 km2. Sample areas >2 3 km2 gave representative values of ice concentration and pond fraction. Melt ponds were numerous, with a number density of 1800 ponds km-2. The melt ponds had a mean area of 62 m2 a median area of 14 m2, and a size distribution that was well lit by a cumulative lognormal distribution. While leads make up only a small portion of the total area, they are the source of virtually all of the solar energy input to the ocean.

scholarly journals Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery

2018 ◽  
Vol 12 (4) ◽  
pp. 1307-1329 ◽  
Author(s):  
Nicholas C. Wright ◽  
Chris M. Polashenski
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Surface Coverage ◽  
Open Water ◽  
The Arctic ◽  
Source Distribution ◽  
Ice Melt ◽  
Observation Area ◽  
Melt Ponds ◽  
Optical Imagery

Abstract. Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery.

scholarly journals Regional albedo of Arctic first-year drift ice in advanced stages of melt from the combination of in situ measurements and aerial imagery

2014 ◽  
Vol 8 (4) ◽  
pp. 3699-3732
Author(s):  
D. V. Divine ◽  
M. A. Granskog ◽  
S. R. Hudson ◽  
C. A. Pedersen ◽  
T. I. Karlsen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
In Situ Measurements ◽  
Arctic Sea Ice ◽  
Aerial Imagery ◽  
The Arctic ◽  
First Year ◽  
Melt Ponds ◽  
Advanced Stages

Abstract. The paper presents a case study of the regional (&amp;approx; 150 km) broadband albedo of first year Arctic sea ice in advanced stages of melt, estimated from a combination of in situ albedo measurements and aerial imagery. The data were collected during the eight day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic north of Svalbard at 82.3° N from 26 July to 3 August 2012. The study uses in situ albedo measurements representative of the four main surface types: bare ice, dark melt ponds, bright melt ponds and open water. Images acquired by a helicopter borne camera system during ice survey flights covered about 28 km2. A subset of > 8000 images from the area of homogeneous melt with open water fraction of &amp;approx; 0.11 and melt pond coverage of &amp;approx; 0.25 used in the upscaling yielded a regional albedo estimate of 0.40 (0.38; 0.42). The 95% confidence interval on the estimate was derived using the moving block bootstrap approach applied to sequences of classified sea ice images and albedo of the four surface types treated as random variables. Uncertainty in the mean estimates of surface type albedo from in situ measurements contributed some 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea ice cover. The results of the study are of relevance for the modeling of sea ice processes in climate simulations. It particularly concerns the period of summer melt, when the optical properties of sea ice undergo substantial changes, which existing sea ice models have significant diffuculty accurately reproducing.

Parameterizing Turbulent Exchange over Sea Ice in Winter

2010 ◽  
Vol 11 (1) ◽  
pp. 87-104 ◽  
Author(s):  
Edgar L. Andreas ◽  
P. Ola G. Persson ◽  
Andrey A. Grachev ◽  
Rachel E. Jordan ◽  
Thomas W. Horst ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Open Water ◽  
Stable Stratification ◽  
Surface Fluxes ◽  
The Arctic ◽  
Drifting Snow ◽  
Melt Ponds ◽  
Roughness Lengths ◽  
Turbulence Data

Abstract The Surface Heat Budget of the Arctic Ocean (SHEBA) experiment produced 18 000 h of turbulence data from the atmospheric surface layer over sea ice while the ice camp drifted for a year in the Beaufort Gyre. Multiple sites instrumented during SHEBA suggest only two aerodynamic seasons over sea ice. In “winter” (October 1997 through 14 May 1998 and 15 September 1998 through the end of the SHEBA deployment in early October 1998), the ice was compact and snow covered, and the snow was dry enough to drift and blow. In “summer” (15 May through 14 September 1998 in this dataset), the snow melted, and melt ponds and leads appeared and covered as much as 40% of the surface with open water. This paper develops a bulk turbulent flux algorithm to explain the winter data. This algorithm predicts the surface fluxes of momentum, and sensible and latent heat from more readily measured or modeled quantities. A main result of the analysis is that the roughness length for wind speed z0 does not depend on the friction velocity u* in the drifting snow regime (u* ≥ 0.30 m s−1) but, rather, is constant in the SHEBA dataset at about 2.3 × 10−4 m. Previous analyses that found z0 to increase with u* during drifting snow may have suffered from fictitious correlation because u* also appears in z0. The present analysis mitigates this fictitious correlation by plotting measured z0 against the corresponding u* computed from the bulk flux algorithm. Such plots, created with data from six different SHEBA sites, show z0 to be independent of the bulk u* for 0.15 &lt; u* ≤ 0.65 m s−1. This study also evaluates the roughness lengths for temperature zT and humidity zQ, incorporates new profile stratification corrections for stable stratification, addresses the singularities that often occur in iterative flux algorithms in very light winds, and includes an extensive analysis of whether atmospheric stratification affects z0, zT, and zQ.

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