The paper presents lecture materials given at the Nineteenth International Conference and School on Quantum Electronics "Laser Physics and Applications" (19th ICSQE) in 2016, Sozopol, Bulgaria and contains the results of the 10-year research of Belarusian Antarctic expeditions to study the atmospheric aerosol and Earth surface in Antarctica. The works focus on the studying variability and trends of aerosol, cloud and snow characteristics in the Antarctic and the links of these processes with the long range transport of atmospheric pollutants and climate changes.

A new algorithm to retrieve characteristics (albedo and melt pond fraction) of summer ice in the Arctic from optical satellite data is described. In contrast to other algorithms this algorithm does not use a priori values of the spectral albedo of the sea-ice constituents (such as melt ponds, white ice etc.). Instead, it is based on an analytical solution for the reflection from sea ice surface. The algorithm includes the correction of the sought-for ice and ponds characteristics with the iterative procedure based on the Newton–Raphson method. Also, it accounts for the bi-directional reflection from the ice/snow surface, which is particularly important for Arctic regions where the sun is low. The algorithm includes an original procedure for the atmospheric correction, as well. This algorithm is implemented as computer code called Melt Pond Detector (MPD). The input to the current version of the MPD algorithm is the MERIS Level 1B data, including the radiance coefficients at ten wavelengths and the solar and observation angles (zenith and azimuth). Also, specific parameters describing surface and atmospheric state can be set in a configuration input file. The software output is the map of the melt ponds area fraction and the spectral albedo of sea-ice in HDF5 format. The numerical verification shows that the MPD algorithm provides more accurate results for the light ponds than for the dark ones. The spectral albedo is retrieved with high accuracy for any type of ice and ponds.

The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences on the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo (Zege et al., 2014) from the MEdium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, ship borne and in situ campaign data. The result show the best correlation for landfast and multiyear ice of high ice concentrations (albedo: R = 0.92, RMS = 0.068, melt pond fraction: R = 0.6, RMS = 0.065). The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to complicated surface conditions and ice drift. Combining all aerial observations gives a mean albedo RMS equal to 0.089 and a mean melt pond fraction RMS equal to 0.22. The in situ melt pond fraction correlation is R = 0.72 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the ASPeCT protocol, which is the reason for discrepancy between the satellite value and observed value: mean R = 0.21, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data. The case studies and trend analysis for the whole MERIS period (2002–2011) show pronounced and reasonable spatial features of melt pond fractions and sea ice albedo. The most prominent feature is the melt onset shifting towards spring (starting already in weeks 3 and 4 of June) within the multiyear ice area, north to the Queen Elizabeth Islands and North Greenland.

Introduction Conclusions References

AbstractThis paper presents a new simple and efficient algorithm to retrieve the effective snow grain size and soot concentration from spectral reflectance on snow measured by optical instrument on a satellite. This algorithm was recently developed and will be used for integrated ice–atmosphere–ocean monitoring in the framework of the DAMOCLES program. The algorithm is based on an analytical approach to snow optics. In this approach snow is considered as a close-packed medium with irregularly shaped grains rather than with independent spherical particles. Unlike the known conventional algorithms, the developed algorithm uses no a priori snow optical model. The analytical nature of this algorithm provides a very fast inversion of the reflection data. The developed algorithm was realized and validated for the GLI and MODIS instruments. The algorithm can be generalized for other satellite instruments with appropriate spectral channels. Finally, the results of verifications using a computer simulation are discussed.