Snow assimilation using EOS snow products

1. Snow assimilation using EOS snow products • Why do we need snow data assimilation? • Models are imperfect, so are observations: we need to combine model and observation in an optimal way (by taking into account their errors) to provide: • Best estimate of snowpack properties (water content for water resource management, • Initial conditions for hydrological, weather forecasting and climate prediction models • Historical reanalysis data (for better understanding of how seasonal snowpacks evolved over time and provide modelers with much needed climatology to help mdoel development) • Challenges: • Close the water cycle – water balance issue in hydrological models • Quantify observation error and validation (need field measurements such as CLPX, SNOTEL, and the MET stations) • Quantify forcing error and model error, especially model bias (consistency check, on-line bias correction scheme…) • Quantify snow mass with multiple data sources (visible, thermal and passive microwave data) : take advantage of various EOS snow products in snow assimilation (data fusion before assimilation & simultaneous assimilation of different observations) • Better models

2. SWE (mm) Snow Depth (mm) Snow Temp. (oC) Snow Fraction (%) a) Open loop b) Truth c) Assimilation Figure 1: Snapshot of different experiments at 1/15/1987.

3. Feb 91 Oct 90 Nov 90 Mar 91 Apr 91 Dec 90 Jan 91 May 91 Figure 2: Uncertainty associated with the new SSM/I SWE algorithm for October 1990 through May 1991.