Enhanced observing capabilities of the GOES-R ABI as compared to the previous generation GOES imaging instruments will allow for improved retrievals of atmosphere, land surface and ocean properties and in particular, improved retrievals of snow cover. Enhancements in snow mapping are expected primarily owing to additional spectral channels centered in the near-infrared, short-wave infrared and split-window infrared bands. Higher rate of observations and better navigation may also be beneficial and may contribute to the improvement of snow monitoring with ABI.

This poster presents the progress in the development of the operational snow identification and mapping algorithm for GOES-R ABI. The technique utilizes both spectral signatures and temporal variability of the scene response in order to distinguish between snow, snow-free land surface and clouds. A number of climatology-based tests and spatial consistency tests are applied to flag questionable snow identifications. The output of the algorithm is a snow cover map generated on a daily basis. Potentials for generation of snow cover products at higher temporal rate are being studied.

In order to assess the performance of the developed technique we have applied it to observations of SEVIRI instrument onboard Meteosat Second Generation (MSG) satellites. Daily MSG-based snow cover maps over Europe have been generated since 2006. In 2008 we started mapping snow over South Africa. Snow retrievals have been compared both with ground-based data and NOAA interactive snow cover charts over Northern Hemisphere. Examples of snow cover maps and estimates of the accuracy of snow mapping with MSG are also presented in the poster.