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In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of the surface assumption. Some factors of variability of the obtained emissivities are analyzed: variability in space, in time, with the zenith angle, and with respect to the frequency. We show that the near-nadir surface emissivity and emissivity difference (obtained using two contrasted surface assumptions) could be used as an excellent proxy to detect ice/no ice regions. We also show that near-nadir sea ice emissivity at some selected frequencies and the combination of both high and low window frequencies could also be very useful to better characterize sea ice surface physical properties and provide additional information for existing sea ice classifications, as they bring relevant information about first year and multiyear sea ice properties and their seasonal evolution. [ABSTRACT FROM AUTHOR]

The low-level temperature inversions have significant impacts on Arctic climate change feedbacks. The Atmospheric Infrared Sounder (AIRS) can extract the inversions over both land and ocean, and it is, however, sensitive to the presence of clouds. In this paper, we evaluate the effect of cloud fraction (CF) on AIRS inversions over both land and ocean. First, the AIRS inversions under clear-sky conditions are compared with the results from the microwave-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) observations in 2007–2013. Results show that despite the COSMIC and AIRS inversions appearing to be deeper and stronger in winter than in autumn, spring, and summer, the former is generally shallower and stronger than the latter in all seasons over both land and ocean. Time-series analysis of the mean monthly inversions from COSMIC and AIRS observations in 2007–2013 under both clear-sky and cloudy conditions further indicates that their differences are systematic and can be effectively mitigated after calibration under all sky conditions. Taking the calibrated COSMIC inversions as references, the AIRS inversion depths can be estimated with a root mean square (rms) of less than about 86 and 135 m, and the AIRS inversion strength can be obtained with an rms of better than about 1.7 °C and 1.3 °C under cloudy conditions over land and ocean, respectively. Moreover, while the AIRS inversion depths are insensitive to CF variations over both land and ocean, the inversion strengths are more sensitive to the CF variations over land than ocean. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

The variation of low-level inversion characteristics is one of the important factors for Arctic amplification. Radiosonde (RS) observations are one of the reliable tools to study Arctic inversions. However, they provide limited information on the spatial distribution of temperature inversions. The radio occultation (RO) technique can provide accurate, all-weather, high vertical resolution atmospheric parameter profiles over both land and ocean. In this paper, the RO technique is investigated to estimate the inversions, and their uncertainties are assessed via comparing with the RS inversions. Results show that the inversions (base, depth, and strength) from RS and RO observations are lower, deeper, and stronger in winter than in autumn, spring, and summer. Moreover, comparisons of spatiotemporally synchronized RO and RS inversions (i.e., Scheme I) during 2007–2013 reveal that the former are higher, deeper, and weaker than the latter. Further comparisons of RS and 5 ^\circ \times 5 ^\circ gridded RO inversions (i.e., Scheme II) show smaller overall standard deviations than Scheme I, indicating that the gridded RO observations can better describe the large-scale spatial structure of temperature inversions. Furthermore, corrections of the observed remarkable systematic differences between RO and RS inversions in both the schemes are also made. The results show that the RO inversions can be well calibrated, and that greater improvements in RO inversions calibration are achieved in Scheme II than in Scheme I. Therefore, the RO technique could be a potential tool to study the Arctic low-level temperature inversions as well as improve our understanding of Arctic climate change. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is twofold: 1) to propose an approach based on optical flow for the estimation of sea ice motion as an accurate, dense, and computationally efficient alternative to state-of-the-art pattern matching approaches and 2) to investigate the potential of moderate resolution imaging spectroradiometer (MODIS) optical satellite data for combined daily and high-resolution motion estimation. A series of MODIS image pairs for a selected region in Arctic Ocean is employed, and sparse pairwise correspondences between nonrigid patches are calculated. An edge-preserving sparse-to-dense interpolation is applied followed by variational energy minimization to compute the final optical flow. A state-of-the-art multiresolution pattern matching method based on phase correlation and normalized cross correlation is also implemented and evaluated for comparison. Thorough experimentation with different settings of input data preprocessing as well as varying scales of motion is performed. The derived motion vectors are compared with coarser resolution operational sea ice motion vector products from combined buoy and microwave satellite data. The proposed optical flow approach clearly outperforms the pattern matching method in most cases, both in terms of accuracy and motion vector consistency. The estimated motion vectors from the MODIS images highly correlate with the operational vector products. In addition, MODIS provides a vector field with spatial resolution two orders of magnitude higher than the operational products that are able to detect significantly smaller sea ice drifts. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model. [ABSTRACT FROM PUBLISHER]

In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements. [ABSTRACT FROM AUTHOR]

Sky-wave over-the-horizon radar (OTHR) propagates radio waves off the ionosphere to provide long-range surveillance around the Earth’s curvature. Frequency selection for high-latitude and polar OTHRs is challenging unless there is an environmental monitor that addresses the significant ionospheric variability in high-latitude regions, a spectrum monitor that finds unoccupied frequencies in the high-frequency (HF) radio waveband, and a frequency management system (FMS) that selects an optimal frequency from the merged results of the environmental monitor and spectrum monitor. This article describes the first FMS for high-latitude OTHR that merges results from the environmental monitor and spectrum monitor in real-time. The environmental monitor uses the assimilative Canadian high-arctic ionospheric model (A-CHAIM), which assimilates near-real-time data and is, to date, the most advanced ionospheric model for high-latitude regions. Despite remaining limitations, demonstrations of this real-time FMS between a transmitter and four targets during the day and night and during fall and summer show that it could be one of the tools for selecting frequencies for operational scenarios. [ABSTRACT FROM AUTHOR]

Manifold calibration improves parametric angle estimator accuracy and resolution performance by reducing the mismatch between the model of an array’s response to directional sources and truth. This article presents nonparametric array manifold calibration for a multichannel ice-penetrating synthetic aperture radar (SAR) sounder used for imaging subglacial morphology with parametric angle estimation in tomography. In this study, we outline a methodology for identifying scatterers at known angles from multichannel imagery by aligning our measurements to an independent fine-resolution satellite-derived digital elevation model of the Arctic that extends beyond the swath of the SAR. We adopt a support statistic based on our partial knowledge of the array response to identify approximately single-source measurements in our scenes. This technique is a departure from traditional approaches to the sounder array characterization problem that require measurements of flat, specular surface reflections from a maneuvering platform. We aggregate observations of single sources and measure manifold corrections relative to our nominal model from the principal eigenvector of our array covariance. We demonstrate the application of three measured manifolds in tomography and compare performance to a nominal manifold that assumes isotropic radiators and known array geometry. We present radar-derived topography of exposed rock and sea ice in the Canadian Arctic Archipelago under the measured and nominal manifolds and report improved vertical accuracy realized with a measured manifold model assumed by the MUltiple SIgnal Classification angle estimators in 3-D image formation. [ABSTRACT FROM AUTHOR]

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth’s Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations. [ABSTRACT FROM AUTHOR]