ALOS-2/PALSAR-2 dual-polarized L-band data for sea ice parameter estimation and sea ice classification 

2021 ◽  
Author(s):  
Juha Karvonen
Keyword(s):  
Parameter Estimation ◽  
Sea Ice ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Sea Ice Drift ◽  
L Band ◽  
Sar Data ◽  
Dual Polarized ◽  
Sar Imagery

<p>This research is related to the JAXA 6th Research Announcement for the Advanced Land<br>Observing Satellite-2 (ALOS-2) project "Improved Sea Ice Parameter Estimation with L-Band SAR (ISIPELS)".<br>In the study ALOS-2/PALSAR-2 dual-polarized Horizontal-transmit-Horizontal-receive/<br>Horizontal-transmit-Vertical-receive (HH/HV) ScanSAR mode L-band  Synthetic Aperture Radar (SAR) imagery<br>over an Arctic study area were evaluated for their suitability for operational sea ice monitoring.<br>The SAR data consisting of about 140 HH/HV ScanSAR ALOS-2/PALSAR-2 images were acquired during the winter 2017.<br>These L-band SAR data were studied for estimation of different sea ice parameters:<br>sea ice concentration, sea ice thickness, sea ice type, sea ice drift. Also some comparisons with nearly<br>coincident C-band data over the same study area have been made. The results indicate that L-band<br>SAR data from ALOS-2/PALSAR-2 are very useful for estimating the studied sea ice parameters and equally good<br>or better than using the conventional operational dual-polarized C-band SAR satellite data.</p><p> </p>

The neXtSIM-F sea ice forecasting platform

2020 ◽  
Author(s):  
Timothy Williams ◽  
Anton Korosov ◽  
Pierre Rampal ◽  
Einar Olason
Keyword(s):  
Sea Ice ◽  
Initial Conditions ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Daily Data ◽  
Deformation Properties ◽  
Ice Concentration ◽  
Sea Ice Drift ◽  
Ice Model

<p>The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation.</p><p>neXtSIM is a novel sea ice model which is able to reproduce sea ice deformation properties and statistics, such as spatial localisation and temporal intermittency,<br>even at relatively low resolutions. For our forecast we run it at 10km resolution, over a pan-Arctic domain. We assimilate OSISAF SSMI and AMSR2 sea ice concentration products and the SMOS sea ice thickness product by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. </p><p>We present an evaluation of the platform over the period from November 2018 to present, looking at sea ice drift and concentration and extent, and thin ice thickness.</p><p>neXtSIM-F is scheduled to become part of the CMEMS Arctic Marine Forecast Center in June 2020.</p>

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

scholarly journals Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift

2016 ◽  
Vol 8 (5) ◽  
pp. 397 ◽  
Author(s):  
Yufang Ye ◽  
Mohammed Shokr ◽  
Georg Heygster ◽  
Gunnar Spreen
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift

scholarly journals Evolution of the Amundsen Sea Polynya, Antarctica, 2016–2021

2021 ◽  
Author(s):  
Grant J. Macdonald ◽  
Stephen F. Ackley ◽  
Alberto M. Mestas-Nuñez
Keyword(s):  
Sea Ice ◽  
Reanalysis Data ◽  
Sea Ice Concentration ◽  
Amundsen Sea ◽  
Annual Variations ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Sar Imagery ◽  
Key Sites ◽  
Ice Production

Abstract. Polynyas are key sites of ice production during the winter and are important sites of biological activity and carbon sequestration during the summer. The Amundsen Sea Polynya (ASP) is the fourth largest Antarctic polynya, has recorded the highest primary productivity and lies in an embayment of key oceanographic significance. However, knowledge of its dynamics, and of sub-annual variations in its area and ice production, is limited. In this study we primarily utilize Sentinel-1 SAR imagery, sea ice concentration products and climate reanalysis data, along with bathymetric data, to analyze the ASP over the period November 2016–March 2021. Specifically, we analyze (i) qualitative changes in the ASP's characteristics and dynamics, and quantitative changes in (ii) summer polynya area, (iii) winter polynya area and ice production. From our analysis of SAR imagery we find that ice produced by the ASP becomes stuck in the vicinity of the polynya and sometimes flows back into the polynya, contributing to its closure and limiting further ice production. The polynya forms westward off a persistent chain of grounded icebergs that are located at the site of a bathymetric high. Grounded icebergs also influence the outflow of ice and facilitate the formation of a 'secondary polynya' at times. Additionally, unlike some polynyas, ice produced by the polynya flows westward after formation, along the coast and into the neighboring sea sector. During the summer and early winter, broader regional sea ice conditions can play an important role in the polynya. The polynya opens in all summers, but record-low sea ice conditions in 2016/17 cause it to become part of the open ocean. During the winter, an average of 78 % of ice production occurs in April–May and September–October, but large polynya events often associated with high winds can cause ice production throughout the winter. While passive microwave data or daily sea ice concentration products remain key for analyzing variations in polynya area and ice production, we find that the ability to directly observe and qualitatively analyze the polynya at a high temporal and spatial resolution with Sentinel-1 imagery provides important insights about the behavior of the polynya that are not possible with those datasets.

A cyclone-centered perspective on the drivers of asymmetric patterns in the atmosphere and sea ice during Arctic cyclones

2021 ◽  
pp. 1-47
Author(s):  
Robin Clancy ◽  
Cecilia M. Bitz ◽  
Edward Blanchard-Wrigglesworth ◽  
Marie C. McGraw ◽  
Steven M. Cavallo
Keyword(s):  
Sea Ice ◽  
Spatial Patterns ◽  
The West ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Primary Driver ◽  
The Impact ◽  
Cold Core ◽  
East West

AbstractArctic cyclones are an extremely common, year-round phenomenon, with substantial influence on sea ice. However, few studies address the heterogeneity in the spatial patterns in the atmosphere and sea ice during Arctic cyclones. We investigate these spatial patterns by compositing on cyclones from 1985-2016 using a novel, cyclone-centered approach that reveals conditions as functions of bearing and distance from cyclone centers. An axisymmetric, cold core model for the structure of Arctic cyclones has previously been proposed, however, we show that the structure of Arctic cyclones is comparable to those in the mid-latitudes, with cyclonic surface winds, a warm, moist sector to the east of cyclones and a cold, dry sector to the west. There is no consensus on the impact of Arctic cyclones on sea ice, as some studies have shown that Arctic cyclones lead to sea ice growth and others to sea ice loss. Instead, we find that sea ice decreases to the east of Arctic cyclones and increases to the west, with the greatest changes occurring in the marginal ice zone. Using a sea ice model forced with prescribed atmospheric reanalysis, we reveal the relative importance of the dynamic and thermodynamic forcing of Arctic cyclones on sea ice. The dynamic and thermodynamic responses of sea ice concentration to cyclones are comparable in magnitude, however dynamic processes dominate the response of sea ice thickness and are the primary driver of the east-west difference in the sea ice response to cyclones.

Sea-ice freeboard or thickness? Design choices in the context of data assimilation in the coupled numerical prediction system EC-Earth3 for seasonal Arctic sea ice prediction

2021 ◽  
Author(s):  
Francois Massonnet ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Ed Blockley ◽  
Pablo Ortega Montilla ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
General Circulation ◽  
Seasonal Prediction ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration

<p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

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