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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.

Abstract The size of snow grains plays an important role for modeling the snow albedo in polar regions. The Snow Grain Size and Pollution (SGSP) retrieval computes the snow grain size using spectral reflectances from the Moderate Imaging Spectrometer (MODIS): the channels at 0.47 μm, 0.86 μm, and 1.24 μm. The unique feature of the SGSP retrieval is that it does not require a-priori information on the snow grain shape. In this paper, the SGSP-retrieved snow grain size is validated with six data sets from ground measurement campaigns in the Arctic, the Antarctic, Greenland, and Japan. Almost half of the comparison cases have a difference below 15%; the correlation coefficient is 0.86. The application of the SGSP retrieval on the Antarctic Ross ice shelf demonstrates the potential to detect snow fall events by analyzing the snow grain size time series for sudden grain size decreases.

Abstract We present an algorithm for retrieval of the effective Snow Grain Size and Pollution amount (SGSP) from satellite measurements. As well as our previous version (Zege et al., 2008, 1998), the new algorithm is based on the analytical solution for snow reflectance within the asymptotic radiative transfer theory. The SGSP algorithm does not use any assumptions on snow grain shape and allows for the snow pack bidirectional reflectance distribution function (BRDF). The algorithm includes a new atmospheric correction procedure that allows for snow BRDF. This SGSP algorithm has been thoroughly validated with computer simulations. Its sensitivity to the atmosphere model has been investigated. It is shown that the inaccuracy of the snow characteristic retrieval due to the uncertainty in the aerosol and molecular atmosphere model is negligible, as compared to that due to the measurement errors at least for aerosol loads typical for polar regions. The significant advantage of the SGSP over conventional algorithms, which use a priori assumptions about particle shape and (or) not allow for the BRDF of the individual snow pack, is that the developed retrieval still works at low sun elevations, which are typical for polar regions.

Cloud top heights as derived using measurements of two instruments (GOME and ATSR-2) onboard the ESA ERS-2 space platform are intercompared. It was found that cloud altitudes obtained using thermal IR measurements of ATSR-2 highly correlate with those obtained from top-of-atmosphere backscattered solar light measurements of GOME in O2 A-band using both the semi-analytical cloud retrieval algorithm SACURA and the neural network cloud retrieval algorithm ROCINN. The average cloud top heights as obtained by these diverse techniques for the whole data set analyzed (931 GOME pixels) differ by 0.6km on average, with larger values given by SACURA as applied to GOME data. We found that the standard deviation of SACURA-derived results for cloud top heights from those of ATSR-2 is equal to 1.8km, while the standard deviation of ROCINN-derived results is equal to 2.2km. The respective average deviations are 0.6km (SACURA) and 0.5km (ROCINN). © 2006 Elsevier Inc. All rights reserved.