THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF THE NORTHERN HEMISPHERE USING ASMR2 DATA

<p><strong>Abstract.</strong> Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heat flux of ice in thin ice areas is strongly affected by the ice thickness difference. Therefore, ice thickness is one of the most important parameters of sea ice. In our previous study, the authors have developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2 for the Sea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of AMSR2 19&thinsp;GHz polarization on difference (V-H) vs 19&thinsp;GHz&thinsp;V polarization. The algorithm was also applicable to the Bering Sea, and could extract most of the thin n ice areas. However, two problems have become clear. One was that some of the thin ice areas were not well extracted, and the other was that some of the consolidated ice were mis-extracted as thin ice areas. In this study, the authors have improved the thin ice area extraction algorithm to solve these problems. By adjusting the parameters of the algorithm applied to the brightness temperature scatter plots of AMSR2 19&thinsp;GHz polarization difference (V-H) vs 19&thinsp;GHz&thinsp;V polarization, most of the thin ice areas were also well extracted in the Bering Sea. The authors also introduced an equation using the brightness temperatures difference of 89GHz vertical and horizontal polarization to reject the thin ice area misextracted over consolidated ice. By applying the above two methods to AMSR2 data, most of the thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of St. Lawrence with good result. The thin ice area extracted data are planed to be approved by JAXA as a AMSR2 research product.</p>

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Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

Annals of Glaciology ◽

10.3189/s0260305500012726 ◽

1993 ◽

Vol 17 ◽

pp. 131-136 ◽

Cited By ~ 8

Author(s):

Kenneth C. Jezek ◽

Carolyn J. Merry ◽

Don J. Cavalieri

Keyword(s):

Brightness Temperature ◽

Microwave Radiometer ◽

Correlated Data ◽

Data Sets ◽

Microwave Brightness Temperature ◽

Brightness Temperatures ◽

Scatter Plots ◽

Microwave Imager ◽

Highly Correlated ◽

The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

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An Atmospherically Driven Sea-Ice Drift Model for the Bering Sea

Annals of Glaciology ◽

10.1017/s0260305500003591 ◽

1984 ◽

Vol 5 ◽

pp. 111-114 ◽

Cited By ~ 14

Author(s):

C. H. Pease ◽

J. E. Overland

Keyword(s):

Sea Ice ◽

Bering Sea ◽

Drift Rate ◽

Sensitivity Study ◽

Ocean Model ◽

Ice Thickness ◽

Drag Coefficients ◽

Drift Model ◽

Ice Drift ◽

The Bering Sea

A free-drift sea-ice model for advection is described which includes an interactive wind-driven ocean for closure. A reduced system of equations is solved economically by a simple iteration on the water stress. The performance of the model is examined through a sensitivity study considering ice thickness, Ekman-layer scaling, wind speed, and drag coefficients. A case study is also presented where the model is driven by measured winds and the resulting drift rate compared to measured ice-drift rate for a three-day period during March 1981 at about 80 km inside the boundary of the open pack ice in the Bering Sea. The advective model is shown to be sensitive to certain assumptions. Increasing the scaling parameter A for the Ekman depth in the ocean model from 0.3 to 0.4 causes a 10 to 15% reduction in ice speed but only a slight decrease in rotation angle (α) with respect to the wind. Modeled α is strongly a function of ice thickness, while speed is not very sensitive to thickness. Ice speed is sensitive to assumptions about drag coefficients for the upper (CA) and lower (CW) surfaces of the ice. Specifying CA and the ratio of CA to CW are important to the calculations.

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Combined SMAP–SMOS thin sea ice thickness retrieval

The Cryosphere ◽

10.5194/tc-13-675-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 675-691 ◽

Cited By ~ 11

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.

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Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

Annals of Glaciology ◽

10.1017/s0260305500012726 ◽

1993 ◽

Vol 17 ◽

pp. 131-136 ◽

Cited By ~ 32

Author(s):

Kenneth C. Jezek ◽

Carolyn J. Merry ◽

Don J. Cavalieri

Keyword(s):

Brightness Temperature ◽

Microwave Radiometer ◽

Correlated Data ◽

Data Sets ◽

Microwave Brightness Temperature ◽

Brightness Temperatures ◽

Scatter Plots ◽

Microwave Imager ◽

Highly Correlated ◽

The Antarctic

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Observation of sea-ice thickness fluctuation in the seasonal ice-covered area during 1992−99 winters

Annals of Glaciology ◽

10.3189/172756401781818707 ◽

2001 ◽

Vol 33 ◽

pp. 449-456 ◽

Cited By ~ 11

Author(s):

Kazutaka Tateyama ◽

Hiroyuki Enomoto

Keyword(s):

Sea Ice ◽

Bering Sea ◽

Sea Of Okhotsk ◽

Retrieval Algorithm ◽

Ice Thickness ◽

The Sea Of Okhotsk ◽

Ice Volume ◽

Fast Ice ◽

Ice Floes ◽

The Bering Sea

AbstractSea-ice fluctuations in the Sea of Okhotsk and the Bering Sea during the winters of 1992−99 were investigated by using the Special Sensor Microwave/ Imager dataset and a new ice-property retrieval algorithm This algorithm can distinguish between ice types such as fast ice floes, young ice and new ice, in an area covered by concentrations of >80% ice, and also has improved display resolution because it uses one of the 85 GHz channels. The ice thicknesses derived from the ice-thickness parameter of the new algorithm were compared with ship-based ice-thickness measurements, and were assumed to be 1−10, 11−34, 35−85 and 86−120 cm for new ice, young ice, floes (first-year ice) and fast ice, respectively. The results showed that ice volume can be small even if the ice area is large, due to thinness of the ice (e.g. in 1999 in the Sea of Okhotsk). A significant out-of-phase response, i.e. ice volume is larger in the Sea of Okhotsk when ice volume is smaller in the Bering Sea, was observed. The period of this see-saw showed two different time-scales, which were short (1 week) and long (2−4 weeks).

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An Atmospherically Driven Sea-Ice Drift Model for the Bering Sea

Annals of Glaciology ◽

10.3189/1984aog5-1-111-114 ◽

1984 ◽

Vol 5 ◽

pp. 111-114 ◽

Cited By ~ 3

Author(s):

C. H. Pease ◽

J. E. Overland

Keyword(s):

Sea Ice ◽

Bering Sea ◽

Drift Rate ◽

Sensitivity Study ◽

Ocean Model ◽

Ice Thickness ◽

Drag Coefficients ◽

Drift Model ◽

Ice Drift ◽

The Bering Sea

A free-drift sea-ice model for advection is described which includes an interactive wind-driven ocean for closure. A reduced system of equations is solved economically by a simple iteration on the water stress. The performance of the model is examined through a sensitivity study considering ice thickness, Ekman-layer scaling, wind speed, and drag coefficients. A case study is also presented where the model is driven by measured winds and the resulting drift rate compared to measured ice-drift rate for a three-day period during March 1981 at about 80 km inside the boundary of the open pack ice in the Bering Sea.The advective model is shown to be sensitive to certain assumptions. Increasing the scaling parameter A for the Ekman depth in the ocean model from 0.3 to 0.4 causes a 10 to 15% reduction in ice speed but only a slight decrease in rotation angle (α) with respect to the wind. Modeled α is strongly a function of ice thickness, while speed is not very sensitive to thickness. Ice speed is sensitive to assumptions about drag coefficients for the upper (CA) and lower (CW) surfaces of the ice. Specifying CA and the ratio of CA to CW are important to the calculations.

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Large-Scale Sea Ice Features in the Western Arctic Basin and the Bering Sea as Viewed by the Noaa-2 Satellite

Arctic and Alpine Research ◽

10.2307/1550329 ◽

1974 ◽

Vol 6 (4) ◽

pp. 333 ◽

Cited By ~ 5

Author(s):

N. A. Streten

Keyword(s):

Sea Ice ◽

Bering Sea ◽

Large Scale ◽

Arctic Basin ◽

Western Arctic ◽

The Bering Sea

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Contrasting trends in sea ice and primary production in the Bering Sea and Arctic Ocean

ICES Journal of Marine Science ◽

10.1093/icesjms/fss113 ◽

2012 ◽

Vol 69 (7) ◽

pp. 1180-1193 ◽

Cited By ~ 62

Author(s):

Zachary W. Brown ◽

Kevin R. Arrigo

Keyword(s):

Sea Ice ◽

Primary Production ◽

Arctic Ocean ◽

Bering Sea ◽

Net Primary Productivity ◽

Remote Sensing Data ◽

Ice Cover ◽

The Arctic ◽

Light Limitation ◽

The Bering Sea

Abstract Brown, Z. W., and Arrigo, K. R. 2012. Contrasting trends in sea ice and primary production in the Bering Sea and Arctic Ocean. – ICES Journal of Marine Science, 69: . Satellite remote sensing data were used to examine recent trends in sea-ice cover and net primary productivity (NPP) in the Bering Sea and Arctic Ocean. In nearly all regions, diminished sea-ice cover significantly enhanced annual NPP, indicating that light-limitation predominates across the seasonally ice-covered waters of the northern hemisphere. However, long-term trends have not been uniform spatially. The seasonal ice pack of the Bering Sea has remained consistent over time, partially because of winter winds that have continued to carry frigid Arctic air southwards over the past six decades. Hence, apart from the “Arctic-like” Chirikov Basin (where sea-ice loss has driven a 30% increase in NPP), no secular trends are evident in Bering Sea NPP, which averaged 288 ± 26 Tg C year−1 over the satellite ocean colour record (1998–2009). Conversely, sea-ice cover in the Arctic Ocean has plummeted, extending the open-water growing season by 45 d in just 12 years, and promoting a 20% increase in NPP (range 441–585 Tg C year−1). Future sea-ice loss will likely stimulate additional NPP over the productive Bering Sea shelves, potentially reducing nutrient flux to the downstream western Arctic Ocean.

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