Processing and analysis of Global snow cover time series for climate change assessment

Abstract

Remote sensing data offer the opportunity to detect terrestrial snow cover in high temporal and spatial resolution. Such information is essential for various applications – ranging from small scale predictions of runoff or floods, ground water recharge and hydro power generation to large scale planetary processes connected to climate change. The processing of globally available time series of remote sensing data constitutes a challenging task due to the huge data volume and computational demand. We are currently developing procedures to meet these challenges by re-processing available MODIS snow cover products and facilitating a processing chain to derive snow cover from AVHRR data. First results include products containing information about snow cover duration as well as snow cover start/stop for time series lasting up to 27 years. These results are based on daily satellite observations with spatial resolutions between 500m and 1000m (therefore meeting the requirements made by GCOS) and allow for in-depth analyses of both: long term changes in snow cover characteristics and short term variability/extreme events.