Time Series Processing and Analysis of Terrestrial Daily Snow Cover Datasets to Describe Status and Development of Global Snow Cover

Kurzfassung

Global snow cover is an important environmental parameter, as is influences hydrology, vegetation, radiation balance, and the living space of humans and animals. Snow is an essential source for freshwater in many regions of the world and at the same time, snow cover depends on precipitation and temperature during the snow season. As climate and weather varies, also the amount as well as the onset, duration, and offset of snow cover changes throughout the years. It is important to analyze this variability in order to identify possible trends, but also to predict the impact of the snow cover situation on local freshwater availability, floods, or the influences on vegetation. Snow cover information derived from medium resolution remote sensing data such as AVHRR, MODIS, and now Sentinel 3 can help to understand and analyze both the current situation as well as possible long term tendencies of global snow cover. Such data is available on a daily basis which is essential as the snow cover extent can change rapidly during snow fall or snow melt events induced by sudden temperature increase. Data gaps caused by cloud cover or polar darkness can cause uncertainties, which make a processing of the time series necessary in order to estimate the snow cover status within these gaps. The Global SnowPack has been developed at DLR to overcome the problem of data gaps, provide information of global snow cover statistics through DLRs Geoservice, and to support analyzing the connections between snow cover and hydrological, climatological, and all the other aspects mentioned already above. The presentation will outline the processing and validation steps, the different products, and the availability of the data in DLRs Geoservice. Examples for possible applications will be given, and an outlook on future activities and developments will also be included.