These albedo visualizations are discussed here and here.
[Readers, be sure to see the comments at the end.]
About the Data
Stroeve et al. (2006) concluded that the MOD10A1 data product captured the natural seasonal cycle in albedo, but exhibited significantly more temporal variability than recorded by ground observations. We now understand that a dominant component of this assessed error is the failure of the MODIS data product to completely remove cloud effects. Inspection of the raw MOD10A1 images reveal an abundance of residual cloud artifacts (shadows, contrails, thin clouds, cloud edges) in the albedo product, presumably because the similar spectral properties between snow and some clouds results in obvious cloud structures. Another problem consists of spuriously low values, for example below 0.4 in the accumulation area where albedo is not observed by pyranometers at the surface to drop below 0.7, seen as linear stripe artifacts in the imagery. Because both the cloud shadows and stripes introduce abrupt daily departures from the actual albedo time series, it is possible to reject them using a multi-day sample. Thus, on a pixel-by-pixel basis, 11-day running statistics are used to identify and reject values that exceed 2 standard deviations (2 sigma) from an 11-day average. To prevent rejecting potentially valid cases data within 0.04 of the median are not rejected. The 11-day median is taken to represent each pixel in the daily data and has a smoothing effect on the albedo time series. June–August (JJA or summer) seasonal averages are generated from monthly averages of the daily filtered and smoothed data. Redundant data from the Aqua satellite MODIS instrument are not used in this study for simplicity, to reduce computational burdens, and given an Aqua MODIS instrument near infrared (channel 6) failure (Hall et al., 2008) that reduces the cloud detection capability. (http://nsidc.org/data/modis/ms2gt/). The interpolation method employs a trend surface through the surrounding four 500-m grid cell values closest to the grid points. The resulting 5-km spatial resolution permits resolving the ablation area within the goals of this study. Major gaps in the time series occur July 29–August 18, 2000, and June 14–July 7, 2001. The frequency and quality of spaceborne albedo retrievals decreases in non-summer months as the amount of solar irradiance and solar incidence angles decrease. Also, in non-melting periods before
- Box, J. E., Fettweis, X., Stroeve, J. C., Tedesco, M., Hall, D. K., and Steffen, K.: Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers, The Cryosphere Discuss., 6, 593-634, doi:10.5194/tcd-6-593-2012, 2012.
- Hall, D. K., J. E. Box, K. Casey, S. J. Hook, C. A. Shuman, K. Steffen, Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland, Remote Sensing of Environment, 2008.
- Hall, D. K., Riggs, G. A., and Salomonson, V. V.: MODIS/Terra Snow Cover Daily L3 Global 500m Grid V004, January to March 2003, Digital media, updated daily. National Snow and Ice Data Center, Boulder, CO, USA, 2011.
- Klein, A. G. and Barnett, A. C.: Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000–2001 snow year, Remote Sens. Environ., 86(2), 162–176, 2003.
- Klein, A. G. and Stroeve, J. C.: Development and validation of a snow albedo algorithm for the MODIS instrument, edited by: Winther, J. G. S. R., Ann. Glaciol., 34, 45–52, 2002.
- Konzelmann, T. and Ohmura, A.: Radiative fluxes and their impact on the energy-balance of the Greenland ice-sheet, J. Glaciol., 41(139), 490–502, 1995.