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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]

The recently observed reduction in perennial ice in the Arctic has given rise to a corresponding increase in seasonal ice, which includes young ice (YI). This type of ice has a major impact on the weather and climate systems. However, only a limited number of studies have been dedicated to explore its spatial coverage and duration. This is mainly due to the lack of remote sensing tools that can identify it. This study uses an ice type and concentration retrieval algorithm, namely, Environment Canada's Ice Concentration Extractor, to study YI distribution and duration in the Arctic during seven ice formation seasons: 2002–03 to 2008–09. Results on the YI area, peak period, duration, and its interannual variability are presented in six regions covering the Arctic Basin. Duration is presented in terms of two parameters that describe the peak period and the number of days when YI concentration exceeds 50%. Probability distribution of the latter parameter shows that YI survives very few days before it grows into first-year ice. The summer of the minimum ice record in 2007 did not leave a remarkable impact on the subsequent YI area or its duration, although a delay in ice formation is observed. YI in the North Water polynya is also studied and shows no particular trend, although it varies between years. Anomalies are explained in terms of modeled surface temperature and wind. [ABSTRACT FROM AUTHOR]

Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in Spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a sparial resolution of 15 km x 13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6% and 8.3% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS.2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4% and 1.5% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80% (AVHRR) and 60% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km x 12.5 km grid-cell size. [ABSTRACT FROM AUTHOR]

Focuses on a study which discussed the retrieval of precipitable water over the arctic region from the radiometric measurements of millimeter-wave imaging radiometer onboard the ER-2 aircraft. Measurements used in conducting the study; Implementation of the retrieval algorithm; Results and conclusions.