Estimate the influence of snow grain size and black carbon on albedo

Estimation of the influence of snow grain size and black carbon on albedo is essential in obtaining the accurate albedo. In this paper, field measurement data, including snow grain size, snow depth and density was obtained. Black carbon samples were collected from the snow surface. A simultaneous observation using Analytical Spectral Devices was employed in the Qiyi Glacier located in the Qilian Mountain. Analytical Spectral Devices spectrum data were used to analyze spectral re- flectance of snow for different grain size and black carbon content. The measurements were compared with the results obtained from the Snow, Ice, and Aerosol Radiation model, and the simulation was found to correlate well with the ob- served data. However, the simulated albedo was near to 0.98 times of the measured albedo, so the other factors were as- sumed to be constant using the corrected Snow, Ice, and Aerosol Radiation model to estimate the influence of measured snow grain size and black carbon on albedo. Field measurements were controlled to fit the relationship between the snow grain size and black carbon in order to estimate the influence of these factors on the snow albedo.

Review of snow grain size retrieval from remote sensing

In this paper, the significance and history of studying snow grain size is introduced. Based on the assumption that high reflectivity in the visible band and significant decreasing reflectivity of snow surface in the infrared band, the grain size of snow, spherical and non-spherical, is sensitive to changes in remote sensing retrieval foundation. Also, models and algorithms applied in current studies are reviewed, together with their advantages and disadvantages. In addition, in order to obtain retrieval accuracy, some factors that may affect grain size are also discussed, such as temperature, wavelength, arid particle shape, as well as method authentication.

Characterization of sea ice and its snow cover in the Arctic Pacific sector during the summer of 2016

A comprehensive analysis of sea ice and its snow cover during the summer in the Arctic Pacific sector was conducted using the observations recorded during the 7th Chinese National Arctic Research Expedition(CHIANRE-2016)and the satellite-derived parameters of the melt pond fraction(MPF)and snow grain size(SGS)from MODIS data.The results show that there were many low-concentration ice areas in the south of 78°N,while the ice concentration and thickness increased significantly with the latitude above the north of 78°N during CHIANRE-2016.The average MPF presented a trend of increasing in June and then decreasing in early September for 2016.The average snow depth on sea ice increased with latitude in the Arctic Pacific sector.We found a widely developed depth hoar layer in the snow stratigraphic profiles.The average SGS generally increased from June to early August and then decreased from August to September in 2016,and two valley values appeared during this period due to snowfall incidents.

Snow features on sea ice in the western Arctic Ocean during summer 2016

Arctic sea ice and its snow cover are important components of the cryosphere and the climate system.A series of in situ snow measurements were conducted during the seventh Chinese Arctic expedition in summer 2016 in the western Arctic Ocean.In this study,we made an analysis of snow features on Arctic sea ice based on in situ observations and the satellite-derived parameter of snow grain size from MODIS spectral reflectance data.Results indicate that snow depth on Arctic sea ice varied between 19 and 241 mm,with a mean value of 100 mm.The mean density of the snow was 340.4 kg/m^(3)during the expedition,which was higher than that reported in previous literature.The measurements revealed that a depth hoar layer was widely developed in the snow,accounting for 30%∼50%of the total snow depth.The equivalent snow grain size was small on the surface and large at the bottom in snow pits.The average relative error between MODIS-derived snow grain size and in situ measured surface snow grain size is 14.6%,indicating that remote sensing is a promising method to obtain large-scale information of snow grain size on Arctic sea ice.