scholarly journals A Study of the Technology Used to Distinguish Sea Ice and Seawater on the Haiyang-2A/B (HY-2A/B) Altimeter Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1490 ◽  
Author(s):  
Chengfei Jiang ◽  
Mingsen Lin ◽  
Hao Wei
Keyword(s):  
Sea Ice ◽  
Sea Water ◽  
Automatic Gain Control ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Altimeter Data ◽  
Support Vector ◽  
Polar Regions ◽  
Waveform Data ◽  
Star Network

When the Haiyang-2B (HY-2B) was launched into space to form a star network with the Haiyang-2A (HY-2A), it provided new data sources for the sea ice research of the Earth’s polar regions. The ability of altimeter echoes to distinguish sea ice and sea water is usable in operational ice charting. In this research study, the level 1B (L1B) data of HY-2A/B altimeter from November 2018 was used to analyze the altimeter waveforms from the polar regions. The Suboptimal Maximum Likelihood Estimation (SMLE) and Offset Center of Gravity (OCOG) tracking packages could maintain the waveform characteristics of diffused and quasi-specular surfaces by comparison. Also, they could be utilized to distinguish sea ice from seawater in the polar regions. It was determined that the types of echoes obtained from the seawater were diffuse. Also, some “ocean-like” waveform data had existed for the old ice formations in the Arctic regions during the study period. The types of echoes obtained from Arctic sea ice were found to be mainly quasi-specular. In the present study, three methods (Threshold segmentation, K-nearest-neighbor (KNN), and Lib-Support Vector machine (LIBSVM)) with four waveform parameters (Automatic Gain Control (AGC) and Pulse Peaking (PP) values of the Ku and C Bands) were adopted to distinguish between the sea ice and seawater areas. The accuracy rate of the separation results for the LIBSVM except band Ku from HY-2B ALT was found to be less than 40% in Antarctic. Meanwhile, the other two methods were observed to have maintained the waveforms correctly at accuracy rates of approximately 80% in Antarctic and the Arctic. In addition, the observed distinguishing errors were located in the regions of the old ice of the Arctic region. In addition, due to the summer melting processes, the large number of ice floes and the snow cover had made it difficult to distinguish the seawater and sea ice in the Antarctic regions.

scholarly journals Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

2021 ◽  
Vol 14 (1) ◽  
pp. 91
Author(s):  
Meijie Liu ◽  
Ran Yan ◽  
Jie Zhang ◽  
Ying Xu ◽  
Ping Chen ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Water ◽  
Incidence Angle ◽  
Arctic Sea Ice ◽  
Detection Methods ◽  
The Arctic ◽  
Support Vector ◽  
Remote Sensors ◽  
Arctic Sea ◽  
Small Incidence

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

scholarly journals Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Special Focus ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Ice Concentration

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

scholarly journals A Decade of Spaceborne Observations of the Arctic Atmosphere: Novel Insights from NASA’s AIRS Instrument

2016 ◽  
Vol 97 (11) ◽  
pp. 2163-2176 ◽  
Author(s):  
Abhay Devasthale ◽  
Joseph Sedlar ◽  
Brian H. Kahn ◽  
Michael Tjernström ◽  
Eric J. Fetzer ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Numerical Models ◽  
Lower Troposphere ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate Projections ◽  
Polar Regions ◽  
Arctic Atmosphere ◽  
Warming World

Abstract Arctic sea ice is declining rapidly and its annual ice extent minima reached record lows twice during the last decade. Large environmental and socioeconomic implications related to sea ice reduction in a warming world necessitate realistic simulations of the Arctic climate system, not least to formulate relevant environmental policies on an international scale. However, despite considerable progress in the last few decades, future climate projections from numerical models still exhibit the largest uncertainties over the polar regions. The lack of sufficient observations of essential climate variables is partly to blame for the poor representation of key atmospheric processes, and their coupling to the surface, in climate models. Observations from the hyperspectral Atmospheric Infrared Sounder (AIRS) instrument on board the National Aeronautics and Space Administration (NASA)’s Aqua satellite are contributing toward improved understanding of the vertical structure of the atmosphere over the poles since 2002, including the lower troposphere. This part of the atmosphere is especially important in the Arctic, as it directly impacts sea ice and its short-term variability. Although in situ measurements provide invaluable ground truth, they are spatially and temporally inhomogeneous and sporadic over the Arctic. A growing number of studies are exploiting AIRS data to investigate the thermodynamic structure of the Arctic atmosphere, with applications ranging from understanding processes to deriving climatologies—all of which are also useful to test and improve parameterizations in climate models. As the AIRS data record now extends more than a decade, a select few of many such noteworthy applications of AIRS data over this challenging and rapidly changing landscape are highlighted here.

scholarly journals September Arctic Sea Ice minimum prediction – a new skillful statistical approach

2018 ◽  
Author(s):  
Monica Ionita ◽  
Klaus Grosfeld ◽  
Patrick Scholz ◽  
Renate Treffeisen ◽  
Gerrit Lohmann
Keyword(s):  
Sea Ice ◽  
Statistical Model ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Ice Extent ◽  
Important Indicator ◽  
Predictive Skill

Abstract. Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice coverage, variability and long term change. However, its predictability is complex and it depends on various atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on oceanic and different atmospheric variables to calculate an estimate of the September sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' atmospheric and oceanic conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

scholarly journals Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130283 ◽  
Author(s):  
Stephan Juricke ◽  
Thomas Jung
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Stochastic Perturbations ◽  
Arctic Sea ◽  
The Mean ◽  
Mean Climate

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.

scholarly journals Wintertime Cold Extremes in Northeast China and Their Linkage with Sea Ice in Barents-Kara Seas

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 386
Author(s):  
Yongyue Luo ◽  
Chun Li ◽  
Jian Shi ◽  
Xiadong An ◽  
Yaqing Sun
Keyword(s):  
Low Temperature ◽  
Sea Ice ◽  
Interannual Variability ◽  
Geopotential Height ◽  
Northeast China ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Northerly Wind ◽  
Cold Air

The impacts of Arctic sea ice on the interannual variability of winter extreme low temperature (WELT) in Northeast China (NEC) and the associated atmospheric circulation patterns are explored in this study based on meteorological observation and the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Results show that WELT in NEC has prominent interannual variability. We further use ±0.8 standard deviation as the threshold to select the years of frequent and rare extreme low temperature anomalies. Using composite analysis, we find that there are significant negative geopotential height anomalies at 500 hPa over NEC and positive geopotential height anomalies along the Arctic region, which represent the intensification of the East Asian trough (EAT) and the negative Arctic Oscillation (AO) phase in the years of more frequent WELT. The opposite characteristics are detected in the years of rare WELT. Furthermore, we determine that the Barents-Kara Seas are key sea ice regions in Arctic area. In the years of frequent WELT, the decrease of autumn Barents-Kara Seas sea ice and the positive sea surface temperature anomaly can last until the following winter, which is conducive to the intensification of anticyclonic anomalies in Ural regions and the northward extension of Ural ridge (UR). The northerly flow in front of UR guides the cold air penetrating southward from polar regions. Moreover, the anomalous cyclone over East Asia deepens the EAT. The northerly wind behind EAT guides the cold air to the NEC region, causing the wintertime low temperature there. The almost opposite situation occurs in the years of rare WELT.

scholarly journals PRELIMINARY RESULTS OF SEA ICE FREEBOARD MEASUREMENTS OF BEAUFORT SEA FROM CRYOSAT-2 ALTIMETRY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1811-1815
Author(s):  
S. Zhang ◽  
Y. Zuo ◽  
F. Xiao ◽  
L. Yuan ◽  
T. Geng ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Water ◽  
High Reliability ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Reliability Estimation ◽  
The Arctic ◽  
Average Height ◽  
Maximum Value ◽  
Arctic Sea

<p><strong>Abstract.</strong> Satellite altimetry has been used to observe the Arctic sea ice in long term and large scale, and the records show a continued decline for Arctic sea ice thickness over decades. In this study, the sea ice freeboard in Beaufort Sea of Arctic have been estimated using CryoSat-2 data, and validated with Upward Looking Sonar (ULS) data of Beaufort Gyre Exploration Project (BGEP). The results show an obvious seasonal variation of the Beaufort Sea with a high reliability estimation of the sea ice freeboard. The average height of the sea ice freeboard increase from January to March and achieve the maximum value 0.38&amp;thinsp;m in March. The sea ice melts after March and the average height of the sea ice freeboard reduces to the minimum 0.12&amp;thinsp;m in August. In the next few months the sea water begins to freeze and the average height of the sea ice freeboard will increase to the maximum value.</p>

scholarly journals The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers

2019 ◽  
Vol 11 (5) ◽  
pp. 521 ◽  
Author(s):  
Jay Hoffman ◽  
Steven Ackerman ◽  
Yinghui Liu ◽  
Jeffrey Key
Keyword(s):  
Sea Ice ◽  
Critical Role ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Contrast Method ◽  
Arctic Sea ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Leads

Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns.

scholarly journals Wave climate in the Arctic 1992–2014: seasonality and trends

2016 ◽  
Vol 10 (4) ◽  
pp. 1605-1629 ◽  
Author(s):  
Justin E. Stopa ◽  
Fabrice Ardhuin ◽  
Fanny Girard-Ardhuin
Keyword(s):  
Sea Ice ◽  
Wave Climate ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Altimeter Data ◽  
Data Set ◽  
Wave Hindcast ◽  
Wind Speeds ◽  
Ice Coverage ◽  
Wave Heights

Abstract. Over the past decade, the diminishing Arctic sea ice has impacted the wave field, which depends on the ice-free ocean and wind. This study characterizes the wave climate in the Arctic spanning 1992–2014 from a merged altimeter data set and a wave hindcast that uses CFSR winds and ice concentrations from satellites as input. The model performs well, verified by the altimeters, and is relatively consistent for climate studies. The wave seasonality and extremes are linked to the ice coverage, wind strength, and wind direction, creating distinct features in the wind seas and swells. The altimeters and model show that the reduction of sea ice coverage causes increasing wave heights instead of the wind. However, trends are convoluted by interannual climate oscillations like the North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation. In the Nordic Greenland Sea the NAO influences the decreasing wind speeds and wave heights. Swells are becoming more prevalent and wind-sea steepness is declining. The satellite data show the sea ice minimum occurs later in fall when the wind speeds increase. This creates more favorable conditions for wave development. Therefore we expect the ice freeze-up in fall to be the most critical season in the Arctic and small changes in ice cover, wind speeds, and wave heights can have large impacts to the evolution of the sea ice throughout the year. It is inconclusive how important wave–ice processes are within the climate system, but selected events suggest the importance of waves within the marginal ice zone.

Sea surface height anomaly of the ice-covered oceans from ICESat-2 and CryoSat-2

2021 ◽  
Author(s):  
Marco Bagnardi ◽  
Nathan Kurtz ◽  
Alek Petty ◽  
Ron Kwok
Keyword(s):  
Sea Ice ◽  
European Space Agency ◽  
Sea Surface Height ◽  
Sea Surface ◽  
The Arctic ◽  
Height Anomaly ◽  
Polar Regions ◽  
Waveform Data ◽  
Polar Oceans ◽  
Sea Surface Height Anomaly

&lt;p&gt;Rapid changes in Earth&amp;#8217;s sea ice and land ice have caused significant disruption to the polar oceans in terms of fresh water storage, ocean circulation, and the overall energy balance. While we can routinely monitor, from space, the ocean surface at lower latitudes, measurements of sea surface in the ice-covered oceans remains challenging due to sampling deficiencies and the need to discriminate returns between sea ice and ocean.&lt;/p&gt;&lt;p&gt;The European Space Agency&amp;#8217;s (ESA) CryoSat-2 satellite has been acquiring unfocussed synthetic aperture radar altimetry data over the polar regions since 2010, providing a key breakthrough in our ability to routintely monitor the ice-covered oceans. Since October 2018, NASA&amp;#8217;s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and its onboard Advanced Topographic Laser Altimeter (ATLAS) have provided new measurements of sea ice and sea surface elevations over similar polar regions. With over two years of overlapping data, we now have the opportunity to compare coincident sea surface height retrievals from the two missions and assess potential elevation differences over two entire freeze-melt cycles across both polar oceans .&lt;/p&gt;&lt;p&gt;Also, as of August 2020, CryoSat-2&amp;#8217;s orbit has been modified as part of the &lt;em&gt;CRYO2ICE&lt;/em&gt; campaign, such that every 19 orbits (20 orbits for ICESat-2) the two satellites align for hundreds of kilometers over the Arctic Ocean, acquiring data along coincident ground tracks with a time difference of approximately three hours.&lt;/p&gt;&lt;p&gt;In this work, we compare sea surface height anomaly (SSHA) retrievals from CryoSat-2 (Level 1b and Level 2 data) and&amp;#160; ICESat-2 (Level 3a data, ATL10). We apply a recently updated waveform fitting method to the CryoSat-2 waveform data (Level 1b) to determine the retracking corrections, &amp;#160;based on &lt;em&gt;Kurtz et al.&lt;/em&gt; (2014). We apply the same mean sea surface adjustment used for ICESat-2 to CryoSat-2 data, and we apply similar geophysical and atmospheric corrections to both datasets.&lt;/p&gt;&lt;p&gt;While we find an overall good agreement between the two datasets, some discrepancies between CryoSat-2 and ICESat-2 SSHA estimates remain. In this work we explore the potential causes of these discrepancies, related to both lead finding/distribution, and range biases.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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