Canadian Ice Service Research Articles

Automated Detection of Ice and Open Water From Dual-Polarization RADARSAT-2 Images for Data Assimilation

In this paper, we present a new technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH-HV images. Probability of the presence of ice within 2.05 km $\times2.05$ km areas is modeled using a form of logistic regression as a function of the difference between the wind speeds estimated from synthetic aperture radar (SAR) data and those obtained from numerical weather prediction short-term forecasts, the spatial correlation between HH and HV backscatter signals, and the spatial standard deviation of the wind speed estimated from SAR. The resulting ice probability model was built based on thousands of SAR images and corresponding Canadian Ice Service (CIS) Image Analysis products covering all seasons and all Canadian and adjacent Arctic regions being monitored by CIS. Extensive verification of the proposed technique was conducted for an entire year (2013) against independent Image Analysis products and Interactive Multisensor Snow and Ice Mapping System ice extent products. Using a probability threshold of 0.95, 72.2% of the retrievals were classified as either ice or open water with an accuracy of 99.2% in the most clean verification scenario against Image Analysis pure ice and water data. The ability to obtain such a large number of retrievals with a very high accuracy makes it feasible to assimilate the resulting retrievals in an ice prediction system. Consequently, the developed ice/water retrieval technique will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.

Improvement of Lake Ice Thickness Retrieval From MODIS Satellite Data Using a Thermodynamic Model

Observations of ice thickness are limited in high latitude regions, at a time when they are increasingly being requested by operational ice centers. This study aims to improve the retrieval of lake ice thickness using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA’s Aqua (P.M.) and Terra (A.M.) satellites. The accuracy of ice thickness retrievals based on MODIS lake ice surface temperature (LIST) is investigated using a commonly used heat balance equation and the retrieved ice thicknesses are compared to in situ measurements from the Canadian Ice Service. The accuracy of ice thickness estimates is improved when using snow depth from the 1-D thermodynamic lake ice model Canadian Lake Ice Model (CLIMo) rather than an empirical relationship between snow depth and ice thickness utilized in the recent investigations. Taking into account all data over the study period (2002–2014), the mean bias error and the root-mean-square error are reduced from −0.42 to 0.07 m and 0.58 to 0.17 m, respectively, with the novel approach proposed herein. However, this approach is limited to ice thickness estimations of less than ca. 1.7 m.

Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015

Abstract. A new automated method enabling consistent satellite assessment of seasonal lake ice phenology at 5 km resolution was developed for all lake pixels (water coverage ≥ 90 %) in the Northern Hemisphere using 36.5 GHz H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) sensors. The lake phenology metrics include seasonal timing and duration of annual ice cover. A moving t test (MTT) algorithm allows for automated lake ice retrievals with daily temporal fidelity and 5 km resolution gridding. The resulting ice phenology record shows strong agreement with available ground-based observations from the Global Lake and River Ice Phenology Database (95.4 % temporal agreement) and favorable correlations (R) with alternative ice phenology records from the Interactive Multisensor Snow and Ice Mapping System (R = 0.84 for water clear of ice (WCI) dates; R = 0.41 for complete freeze over (CFO) dates) and Canadian Ice Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002–2015) AMSR-E/2 ice record indicates increasingly shorter ice cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p < 0.05) regional trends toward earlier ice melting for only five lakes. Higher-latitude lakes reveal more widespread and larger trends toward shorter ice cover duration than lower-latitude lakes, consistent with enhanced polar warming. This study documents a new satellite-based approach for rapid assessment and regional monitoring of seasonal ice cover changes over large lakes, with resulting accuracy suitable for global change studies.

A Tessellation-Based Methodology for an Interactive Analysis of the Arctic Ice Dynamic Phenomenon with Spatial Online Analytical Processing

A tessellation-based methodology for interactively analyzing the spatio-temporal evolution of a dynamic phenomenon – ice coverage and its characteristics – using a spatial online analytical processing (OLAP) approach is proposed. The feasibility of the method was tested through a prototype developed in the context of the CanICE project using Canadian Ice Service data and the Egg Code, an international standard for characterizing sea and lake ice. By transforming the standard spatial OLAP vector-based point of view – aggregating data from instances of evolving features – into a tessellation-based point of view – aggregating data from constant spaces with evolving properties – the proposed solution makes it possible to meet the criteria of interactive multidimensional analysis for dynamic phenomena. The second innovative aspect of the methodology relates to the management of data quality with a spatial OLAP approach.

Multiyear Arctic Ice Classification Using ASCAT and SSMIS

The concentration, type, and extent of sea ice in the Arctic can be estimated based on measurements from satellite active microwave sensors, passive microwave sensors, or both. Here, data from the Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager/Sounder (SSMIS) are employed to broadly classify Arctic sea ice type as first-year (FY) or multiyear (MY). Combining data from both active and passive sensors can improve the performance of MY and FY ice classification. The classification method uses C-band σ0 measurements from ASCAT and 37 GHz brightness temperature measurements from SSMIS to derive a probabilistic model based on a multivariate Gaussian distribution. Using a Gaussian model, a Bayesian estimator selects between FY and MY ice to classify pixels in images of Arctic sea ice. The ASCAT/SSMIS classification results are compared with classifications using the Oceansat-2 scatterometer (OSCAT), the Equal-Area Scalable Earth Grid (EASE-Grid) Sea Ice Age dataset available from the National Snow and Ice Data Center (NSIDC), and the Canadian Ice Service (CIS) charts, also available from the NSIDC. The MY ice extent of the ASCAT/SSMIS classifications demonstrates an average difference of 282 thousand km - + from that of the OSCAT classifications from 2009 to 2014. The difference is an average of 13.6% of the OSCAT MY ice extent, which averaged 2.19 million km2 over the same period. Compared to the ice classified as two years or older in the EASE-Grid Sea Ice Age dataset (EASE-2+) from 2009 to 2012, the average difference is 617 thousand km2 . The difference is an average of 22.8% of the EASE-2+ MY ice extent, which averaged 2.79 million km2 from 2009 to 2012. Comparison with the Canadian Ice Service (CIS) charts shows that most ASCAT/SSMIS classifications of MY ice correspond to a MY ice concentration of approximately 50% or greater in the CIS charts. The addition of the passive SSMIS data appears to improve classifications by mitigating misclassifications caused by ASCAT's sensitivity to rough patches of ice which can appear similar to, but are not, MY ice.

Replacement of multiyear sea ice and changes in the open water season duration in the Beaufort Sea since 2004

AbstractThe last decade has witnessed the nine lowest Arctic September sea ice extents in the observational record. It also forms the most recent third of the long‐term trend in that record, which reached −13.4% decade−1 in 2015. While hemispheric analyses paint a compelling picture of sea ice loss across the Arctic, the situation with multiyear ice in the Beaufort Sea is particularly dire. This study was undertaken in light of substantial changes that have occurred in the extent of summer multiyear sea ice in the Arctic inferred from the passive microwave record. To better elucidate these changes at a sub‐regional scale, we use data from the Canadian Ice Service archive, the most direct observations of sea ice stage‐of‐development available. We also build upon the only previous sea ice climatological analysis for Canada's western Arctic region by sea ice stage‐of‐development that ended in 2004. The annual evolution of sea ice by stage of development in Canada's western Arctic changed dramatically between 1983 and 2014. The rate of these changes and their spatial prevalence were most prominent in the last decade. In summer, total sea ice loss occurred via reductions in old and first‐year sea ice over increasingly large areas and over more months per year. Resultant delay of thermodynamic freeze up has increased the annual open water duration in the study region. The winter sea ice cover was increasingly composed of first‐year sea ice at the expense of old ice. Breakup timing has not significantly changed in the region.

Improved Sea Ice Concentration Estimation Through Fusing Classified SAR Imagery and AMSR-E Data

. A method to automatically combine binary ice/water information from synthetic aperture radar (SAR) sea ice images with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) daily ice concentration product is proposed for the purpose of generating sea ice concentration estimates with improved detail and accuracy. First, each pixel in the SAR image is labeled as ice or water using the MAp-Guided Ice Classification (MAGIC) SAR image classification system. Second, the labeled pixels are modeled as a Bernoulli process and combined with the AMSR-E ice concentration data in a Bayesian framework to generate improved ice concentration estimation. Visually interpreted ice/water extent and sea ice image analyses from the Canadian Ice Service (CIS) are used as comparison data. The combination of SAR ice/water labeled pixels with the AMSR-E ice concentration is shown to improve the ice concentration estimates, especially at the ice edge where substantial improvements are observed. Although the present study uses ice/water information from SAR, the method is general and could be used with other sources of ice/water remote sensed data.Résumé. Une méthode pour combiner automatiquement les informations binaires de glace/eau des images de radar à ouverture synthétique «synthetic aperture radar» (SAR) de la glace de mer avec le produit de la concentration quotidienne des glaces de «Advanced Microwave Scanning Radiometer-EOS» (AMSR-E), est proposée dans le but de produire des estimations de concentration de glace de mer en améliorant les détails et la précision. Tout d’abord, chaque pixel de l’image SAR est étiqueté comme de la glace ou de l’eau en utilisant le système de classification de l’image SAR «MAp-Guided Ice Classification» (MAGIC). Deuxièmement, les pixels étiquetés sont modélisés comme un processus de Bernoulli et combinés avec les données de concentration de glace AMSR-E dans un cadre Bayésien pour générer une meilleure estimation de la concentration de glace. L’étendue de la glace/eau interprétée visuellement et les analyses de l’image de la glace de mer du Service canadien des glaces «Canadian Ice Service» (CIS) sont utilisées comme données de comparaison. La combinaison des pixels SAR étiquetés glace/eau avec la concentration de glace AMSR-E améliore les estimations de concentration de glace, en particulier à la lisière de glace où des améliorations substantielles sont observées. Bien que la présente étude utilise les informations de glace/eau de SAR, la méthode est générale et peut être utilisée avec d’autres sources de données de télédétection de glace/eau.

Preparation by the Canadian Ice Service for the Operational Use of the RADARSAT Constellation Mission in Their Ice and Oil Spill Monitoring Programs

. The Canadian Ice Service (CIS) has successfully used airborne and spaceborne synthetic aperture radar (SAR) data extensively for almost three decades in their daily ice and oil monitoring operations. Microwave radar's unique ability to penetrate clouds and weather make these types of data invaluable to the CIS's support to efficient environmental stewardship and safe operations in Canadian waters. Canada's RADARSAT-1 and RADARSAT-2 platforms have been used by CIS since 1996, with approximately 70,000 image scenes incorporated into the development of operational products in that time. The next generation of the RADARSAT program, the RADARSAT Constellation Mission (RCM), will provide C-band SAR data continuity for CIS and other Canadian SAR users for many years to come when launched in 2018. The RCM will consist of a three satellite constellation, offering improved coverage and new technological advancements, and will provide CIS with a unique opportunity to develop new and improved ice and oil information products. This technical note will discuss the prelaunch preparations planned at the CIS that will enable its operational programs to effectively use the capabilities afforded by this mission at launch and beyond.Résumé. Le Service canadien des glaces (SCG) a abondamment utilisé avec succès des données de radars à synthèse d’ouverture spatiaux et aéroportés pendant près de trois décennies dans leurs opérations quotidiennes pour la surveillance des glaces et de la pollution pétrolière. La capacité unique des micro-ondes radar à traverser les nuages et les conditions météorologiques adverses rend ces types de données précieuses au SCG pour appuyer une gestion efficace de l’environnement et la sécurité des opérations dans les eaux canadiennes. Les plateformes RADARSAT-1 et RADARSAT-2 du Canada ont été utilisées par le SCG depuis 1996 et, jusqu’à présent, environ 70 000 images ont été incorporées dans le développement de produits opérationnels. Lors de son lancement en 2018, la prochaine génération du programme RADARSAT, la mission de la Constellation RADARSAT (MCR), assurera la continuité des données SAR en bande C pour le SCG et les autres utilisateurs canadiens de données SAR pour de nombreuses années à venir. La MCR sera composée d’une constellation de trois satellites, offrant une meilleure couverture, de nouvelles avancées technologiques, et elle fournira au SCG une occasion unique de développer de nouvelles et meilleures informations sur les glaces et la pollution pétrolière. Cette note technique discute des préparatifs de prélancements qui sont prévus au SCG et qui permettront à ses programmes opérationnels d’utiliser efficacement les capacités offertes par cette mission au moment du lancement et au-delà.

Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System

Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10‐day forecasts on a 1/4°‐resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sea‐level anomaly and sea‐surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D‐Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis‐based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium‐range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open‐water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

TRACKING VESSELS TO ILLEGAL POLLUTANT DISCHARGES USING MULTISOURCE VESSEL INFORMATION

Abstract. Illegal discharge of bilge waters is a significant source of oil and other environmental pollutants in Canadian and international waters. Imaging satellites are commonly used to monitor large areas to detect oily discharges from vessels, off-shore platforms and other sources. While remotely sensed imagery provides a snap-shot picture useful for detecting a spill or the presence of vessels in the vicinity, it is difficult to directly associate a vessel to an observed spill unless the vessel is observed while the discharge is occurring. The situation then becomes more challenging with increased vessel traffic as multiple vessels may be associated with a spill event. By combining multiple sources of vessel location data, such as Automated Information Systems (AIS), Long Range Identification and Tracking (LRIT) and SAR-based ship detection, with spill detections and drift models we have created a system that associates detected spill events with vessels in the area using a probabilistic model that intersects vessel tracks and spill drift trajectories in both time and space. Working with the Canadian Space Agency and the Canadian Ice Service’s Integrated Satellite Tracking of Pollution (ISTOP) program, we use spills observed in Canadian waters to demonstrate the investigative value of augmenting spill detections with temporally sequenced vessel and spill tracking information.

Multiyear ice replenishment in the Canadian Arctic Archipelago: 1997–2013

AbstractIn the Canadian Arctic Archipelago (CAA), multiyear ice (MYI) replenishment from first‐year ice aging (CAAMYI‐Oct‐1) and Arctic Ocean MYI exchange (CAAMYI‐exchange) contribute to the CAA's relatively heavy sea ice conditions at the end of the summer melt season. We estimate these components using RADARSAT and the Canadian Ice Service Digital Archive and explore processes responsible for interannual variability from 1997 to 2013. CAAMYI‐Oct‐1 (52 ± 36 × 103 km2) provides a larger contribution than CAAMYI‐exchange (13 ± 11 × 103 km2). CAAMYI‐Oct‐1 represents ∼10% of the amount that occurs in the Arctic Ocean. CAAMYI‐exchange represents ∼50% of Nares Strait MYI export to Baffin Bay and ∼12% of Fram Strait MYI export to the Greenland Sea. CAAMYI‐Oct‐1 exhibits dependence on warmer (cooler) summers that increase (decrease) melt evident from strong relationships to surface air temperature (SAT), albedo and total absorbed solar radiation (Qtotal). CAAMYI‐exchange is influenced by summer sea level pressure (SLP) anomalies over the Beaufort Sea and Canadian Basin which shifts the primary source of CAAMYI‐exchange between less obstructed M'Clure Strait (low SLP anomalies) and the more obstructed Queen Elizabeth Islands (high SLP anomalies). Over the 17‐record, appreciable replenishment occurred for most years from 1997 to 2004, reduced replenishment from 2005 to 2012, and large replenishment in 2013. The reduced replenishment period was associated with positive SAT, negative albedo, and positive Qtotal anomalies that facilitated more melt and less CAAMYI‐Oct‐1, together with high SLP anomalies that facilitated less CAAMYI‐exchange. Large replenishment in 2013 was primarily from CAAMYI‐Oct‐1 attributed to strongly negative SAT and Qtotal anomalies and strongly positive albedo that impeded melt.

Automated Ice–Water Classification Using Dual Polarization SAR Satellite Imagery

Mapping ice and open water in ocean bodies is important for numerous purposes, including environmental analysis and ship navigation. The Canadian Ice Service (CIS) has stipulated a need for an automated ice-water discrimination algorithm using dual polarization images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions, which are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAp-Guided Ice Classification. First, the HV (horizontal transmit polarization, vertical receive polarization) scene is classified using the “glocal” method, i.e., a hierarchical region-based classification method based on the published iterative region growing using semantics (IRGS) algorithm. Second, a pixel-based support vector machine (SVM) using a nonlinear radial basis function kernel classification is performed exploiting synthetic aperture radar gray-level cooccurrence texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 20 ground truthed dual polarization RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 96.42%, with a minimum of 89.95% for one scene. The MAGIC system is now under consideration by the CIS for operational use.

Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the Environment Canada Regional Ice Prediction System

AbstractThis study describes the impact from three major modifications to an existing ice‐analysis system developed at Environment Canada. The analysis component of the Regional Ice Prediction System currently provides near real‐time gridded estimates of ice concentration for all ice‐affected areas around North America and Greenland, and is primarily aimed to satisfy the operational requirements of the Canadian Ice Service. The first modification is the assimilation of Special Sensor Microwave Imager/Sounder data from three satellite platforms to complement the already assimilated Special Sensor Microwave Imager data from one platform. The second change is the assimilation of ice‐concentration information derived from Advanced Scatterometer data. The third modification is to replace the ice concentration in the analysis with spatially interpolated values for all grid points where an estimated measure of uncertainty is above a specified threshold. Objective verification scores were computed from 1 year experiments spanning all of 2010 using independent verification data to evaluate the accuracy of the analyses. The incremental impact of adding each of the three modifications is examined along with the combined impact from the three modifications. It is demonstrated that the new version of the system produces consistently more accurate ice‐concentration analyses than the previous version, especially during the summer period and when the ice is refreezing.

Sea ice climatology in the Canadian Western Arctic: thermodynamic versus dynamic controls

ABSTRACTBased on the regions in the western Canadian Arctic as outlined by the Canadian Ice Service, the normals and trends from 1981 to 2010 were analysed for the monthly surface air temperature, monthly wind speed and direction. For the month of September, the temperatures from 1981 to 2010, for all of the defined regions, increased by 2–4 °C. The monthly concentrations of sea ice and multiyear ice were analysed for normals and trends from 1981 to 2010. Although the whole Arctic has seen a large reduction in the minimum sea ice extent, in the defined region of study during September, only the Beaufort Sea region shows a statistically significant decrease in sea ice concentrations. Correlations between the climatological state variables and sea ice were investigated in this study to determine relative thermodynamic and dynamic contributions to a decline in sea ice extent in the Western Arctic. As expected, the regions of interest all showed a statistically significant correlation between the surface air temperatures and the total sea ice concentrations. However, neither the wind speed nor the direction had a strong correlation on the sea ice concentration trends. This study showed that in the regions investigated, thermodynamic forcing of sea ice is the dominant driver when compared with dynamic forcing, which is a secondary driver in the western Canadian Arctic.

Evolution of the RADARSAT Program

The RADARSAT program takes its origin in the seventies when the Canadian Government was seeking a reliable technology to ensure safe navigation through sea ice. At that time there was an expectation that it would be necessary for large tanker to navigate through the Beaufort Sea to transport oil extracted from drilling platforms. During the same period the Canadian Center for Remote Sensing was developing SAR applications using an airborne SAR instrument on a Convair 580. One of these applications of great interest for the Canadian Ice Service was ice discrimination provided by the C-band SAR data to support the development of ice map to guide navigation in winter in the Gulf of Saint-Lawrence Seaway. With the combination of both, interest for oil transportation in the Beaufort Sea and the need for accurate and frequent ice map the business case for RADARSAT- 1 was born. This paper provides an overview on the RADARSAT program since its beginning and is partially based on a presentation delivered in October 2013 at the Canadian Space Agency on the motivation and evolution of the RADARSAT program [1].

Sea Ice Motion Tracking From Sequential Dual-Polarization RADARSAT-2 Images

A new sea ice motion tracking algorithm that operates with two sequential synthetic aperture radar (SAR) RADARSAT-2 ScanSAR images is presented. The feature tracking approach is based on the combination of the phase-correlation and cross-correlation methods. An algorithm for selecting control points, a matching technique, an approach for filtering out error vectors, and a confidence levels setting for output drift vectors were specifically developed in order to increase the system's robustness and accuracy. We evaluated ice motion tracking results derived from HH and HV channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice tracking. Furthermore, we found that the ice motion tracking from the HV channel is not affected by noise floor stripes, which are prominent in the cross-polarization RADARSAT-2 ScanSAR images. The developed sea ice tracking technology was implemented at the Canadian Ice Service, Environment Canada for operational use. The system was successfully run to provide operational support of field work in the Arctic Ocean in compliance with the United Nations Convention on the Law of the Sea in the spring of 2010.

A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery

The discrimination of oil spills and look-alike phenomena (e.g., low wind area, wind front area and natural slicks) on Synthetic Aperture Radar (SAR) images is a crucial task in marine oil spill detection. Many classification techniques can be employed for this purpose. In order to make the best use of the large variety of statistical and machine learning classification methods, it is necessary to assess their performance differences and make recommendations for classifier selection and improvement. The objective of this paper is to compare different classification techniques for oil-spill detection in RADARSAT-1 imagery. The data of this study consists of 15 features of 192 oil spills and look-alikes identified by Canadian Ice Service between 2004 and 2008 off Canada's east and west coastal areas. The studied classifiers include the Support Vector Machine (SVM), Artificial Neural Network (ANN), tree-based ensemble classifiers (bagging, bundling and boosting), Generalized Additive Model (GAM) and Penalized Linear Discriminant Analysis (PLDA). Two performance measures, the specificity at fixed sensitivity (80%) and the area under the Receiver Operating Characteristic (ROC) curve (AUC), were estimated using cross-validation to evaluate the performance of classifiers at a high sensitivity. Overall, the bundling technique which achieved a median specificity of 90.7%, at sensitivity of 80%, significantly outperformed the second best (i.e. bagging) by 1.5 percentage points, and the worst (i.e. ANN) by 15 percentage points. The median values of AUC measure indicated consistent results. Bundling and bagging achieved comparable median AUC values of about 92%, followed by GAM and PLDA, with ANN yielding the smallest. Most classifiers (SVM, bundling and especially PLDA and ANN) performed significantly better on datasets pre-processed by log-transformation and standardization than on the original dataset. These results demonstrate the importance and benefit of selecting the optimal classifiers for oil spill classification, and configuring the classifiers by proper feature construction techniques.

A New Environment Canada Regional Ice Analysis System

This paper describes a new regional ice analysis system developed at Environment Canada. It is primarily designed to satisfy the requirements for planning of marine transportation and other marine operations in ice-infested waters around North America, including Canada's two Arctic Metareas; regional sea-ice model initialization; and the needs of regional numerical weather prediction models. A three-dimensional variational approach (3D-Var) is used to assimilate various types of observations. In this first version, only analyses of ice concentration are produced at approximately 5 km resolution using a 6 h persistence forecast from the previous analysis as the background state. The assimilated observations are sea-ice concentrations from two sources of passive microwave satellite data, Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) and Special Sensor Microwave/Imager (SSM/I), and manually derived ice charts from the Canadian Ice Service (CIS). Objective verification scores computed from independent data are used to evaluate the accuracy of the analyses in comparison with other available sources of ice information. This comparison demonstrates that the new analyses have consistently more accurate ice extent compared with the currently operational global sea-ice analyses at the Canadian Meteorological Centre. It also shows that the early morning analysis can provide marine transportation clients with a valuable update to the most recently available CIS daily ice chart. RÉSUMÉ [Traduit par la rédaction] Cet article décrit un nouveau système d'analyse régionale des glaces mis au point à Environnement Canada. Il est principalement conçu pour répondre aux exigences de la planification du transport maritime et autres opérations maritimes dans les eaux infestées de glace entourant l'Amérique du Nord, y compris les deux régions météorologiques arctiques du Canada, pour l'initialisation du modèle régional de glaces de mer et pour les besoins des modèles régionaux de prévision météorologique numérique. Nous utilisons une approche variationnelle tridimensionnelle (3D-VAR) pour assimiler différents types d'observations. Dans cette première version, seules des analyses de la concentration de la glace sont produites avec une résolution d'environ 5 km à l'aide d'une prévision de 6 h basée sur la persistance à partir de l'analyse précédente utilisée comme l'ébauche. Les observations assimilées sont les concentrations de la glace de mer de deux sources satellitaires de données hyperfréquences passives — le radiomètre à balayage hyperfréquences de pointe du Système d'observation de la Terre (AMSR-E) et le capteur hyperfréquences spécialisé/imageur (SSM/I) — et les cartes des glaces élaborées manuellement du Service canadien des glaces (SCG). Nous utilisons des indices de vérification objectifs calculés à partir de données indépendantes pour évaluer l'exactitude des analyses par comparaison avec d'autres sources d'information sur les glaces disponibles. Il ressort que les nouvelles analyses ont constamment des étendues de glaces plus exactes que les analyses globales opérationnelles courantes des glaces de mer employées au Centre météorologique canadien. Cela montre aussi que l'analyse préliminaire du matin peut fournir aux clients du transport maritime une mise à jour valable de la plus récente carte quotidienne des glaces du SCG.

Multiyear Arctic Sea Ice Classification Using QuikSCAT

Long-term trends in Arctic sea ice are of particular interest in studies of global temperature, climate change, and industrial application. This paper analyzes intra-annual and interannual trends in Ku-band backscatter over first-year (FY) and multiyear (MY) sea ice to develop a new sea-ice-type classification method. Histograms of backscatter are derived from high-resolution backscatter images created using the scatterometer image reconstruction (SIR) algorithm applied to measurements obtained by the SeaWinds instrument aboard QuikSCAT. The backscatter of FY and MY sea ice are clearly identifiable and are observed to vary seasonally. Using an average of the annual backscatter trends obtained from QuikSCAT, a classification of MY ice is obtained, which uses a time-dependent threshold value. Validation of the classification method is done using regional ice charts from the Canadian Ice Service. Differences in ice classification are found to be less than 6% for the winters of 2006-2007 and 2007-2008, and the end of 2008. Anomalies in the distribution of sea ice backscatter from year to year suggest a reduction in MY ice cover between 2003 and 2009 and an approximately equivalent increase in FY ice cover.

Landfast Sea Ice Conditions in the Canadian Arctic: 1983 – 2009

We used Canadian Ice Service (CIS) digital charts from 1983 to 2009 to create a climatology of landfast sea ice in the Canadian Arctic. The climatology characterized the spatial distribution and variability of landfast ice through an average annual cycle and identified the mean onset date, breakup date, and duration of landfast ice. Trends in date and duration of onset and breakup were calculated over the 26-year period on the basis of CIS regions and sub-regions. In several sub-regions— particularly in the Canadian Arctic Archipelago—we calculated significant trends towards later landfast ice onset or earlier breakup, or both. These later onset and earlier breakup dates translated into significant decreases in landfast ice duration for many areas of the Canadian Arctic. For communities located in the most affected areas, including Tuktoyaktuk, Kugluktuk, Cambridge Bay, Gjoa Haven, Arctic Bay, and Pond Inlet, this shorter landfast ice season is of significant social, cultural, and economic importance. Landfast sea-ice duration in the interior of the Northwest Passage has not undergone any statistically significant decrease over the time series.