Klein AG and Barnett A. 2003. Validation of daily MODIS snowKongoli C, Dean CA, Helfrich SR and Ferraro R. 2006. The retKrenke A. 1998 updated 2004. Former Soviet Union hydrologicaNghiem SV and Tsai W-Y. 2001. Global snow cover monitoring wRamsay B. 1998. The interactive multisensor snow and ice mapSimic A, Fernandes R, Brown R, Romanov P, Park W. 2004. ValiZhou X, Xie H, Hendrickx JMH. 2005. Statistical evaluation o64th EASTERN SNOW CONFERENCE St. Johns, Newfoundland, Canada 2007 Preliminary Evaluation of the AFWA-NASA Blended Snow-Cover Product over the Lower Great Lakes region D.K. HALL1, P.M. MONTESANO2, J.L. FOSTER1, G.A. RIGGS2, R.E.J. KELLY3 AND K. CZAJKOWSKI4 ABSTRACT A new snow product created using the standard Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow cover and snow-water equivalent products has been evaluated for the Lower Great Lakes region during the winter of 2002-03. National Weather Service Co-Operative Observing Network stations and student-acquired snow data were used as ground truth. An interpolation scheme was used to map snow cover on the ground from the station measurements for each day of the study period. It is concluded that this technique does not represent the actual ground conditions adequately to permit evaluation of the new snow product in an absolute sense. However, use of the new product was found to improve the mapping of snow cover as compared to using either the MODIS or AMSR-E product, alone. Plans for further analysis are discussed. Keywords: snow-cover map, MODIS, passive microwave, AMSR-E, Great Lakes region INTRODUCTION A preliminary blended-snow product has been developed jointly by the U.S. Air Force Weather Agency (AFWA) and the Hydrospheric and Biospheric Sciences Laboratory (HBSL) at NASA / Goddard Space Flight Center. A description of the preliminary product, called the AFWA – NASA or ANSA blended snow-cover product, may be found in Foster et al. (this volume). The product utilizes the Moderate-Resolution Imaging Spectroradiometer (MODIS) standard daily global (5-km resolution) snow-cover product (Hall and Riggs, 2007) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) standard daily global (25 km resolution) snow-water equivalent (SWE) product (Kelly et al., 2003) to map snow cover and SWE. Future enhancements will include the use of scatterometry data (see Nghiem and Tsai, 2001). We have undertaken an analysis of the ability of the ANSA blended product to map snow-cover extent in the Lower Great Lakes region for the winter of 2002 – 2003. Some of the benefits and challenges of validating this new product for mapping snow cover as compared to using the MODIS or AMSR-E products alone, are discussed. BACKGROUND In previous work, the MODIS snow-cover and cloud-masking products were validated in the Lower Great Lakes region using student-acquired data from the Global Learning and Observations to Benefit the Environment (GLOBE) and Students and Teachers Evaluating Local Landscapes to Interpret The Earth from Space (SATELLITES) (a K-12 program developed at the University of Toledo), or GLOBE-SAT, and National Weather Service (NWS) Co-Operative Observing Network observations (Ault et al., 2006). Quantitative analysis of the Version 4 MODIS snow algorithm produced an accuracy of 94% when compared to student observations, the largest errors being associated with partly cloudy conditions during the winters of 2001-2002 and 2002-2003 (Ault et al., 2006). ______________________________ 1Hydrospheric and Biospheric Sciences Laboratory, NASA/Goddard Space Flight Center, Greenbelt, MD 20771 USA 2SSAI, Inc., Lanham, MD 20706 USA 3Department of Geography, University of Waterloo, Waterloo, Ontario N2L 3G1 Canada 4Department of Geography and Planning, University of Toledo, Toledo, OH 43606 USA2 Other work reports on the accuracy of the MODIS snow-cover products (Hall and Riggs, 2007). Various researchers (e.g., Bussières et al., 2002; Klein and Barnett, 2003; Simic et al., 2004; Zhou et al., 2005) have studied the accuracy of the products under a variety of snow- and land-cover conditions. Most studies show an overall accuracy of ~94% compared with ground measurements, but lower accuracies are reported in the fall and spring, and under thin-snow conditions and in dense forests. Omission errors (misclassifying snow as non-snow-covered land) tend to be low. A significant source of error in the MODIS snow products is due to the overly conservative nature of the cloud-masking algorithm (Ackerman et al., 1998), used as an input to the snow algorithm, which may result in more cloud obscuration than is actually present. Currently, the SWE of a dry snowpack can be estimated with passive-microwave sensors such as the Special Sensor Microwave/Imager (SSM/I) (Chang et al., 1987), and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (Kelly et al., 2003), which was launched on the Aqua satellite in May of 2002. In Canada, SSM/I data have been used to provide operational SWE map products (Derksen et al., 2002) for the Canadian prairie region. Forest cover can adversely affect the SWE retrieval accuracy by reducing the characteristic scattering response from snow. Foster et al. (2005) showed that dense vegetation is the major source of systematic error in passive microwave algorithms, and in static algorithms, the assumption of a constant snow grain size also contributes significant errors. Kelly et al. (2003) developed a methodology to estimate snow grain size and density as they evolve through the season using SSM/I and simple statistical growth models. The current version of the algorithm estimates snow depth first and then calculates SWE from climatology data from Brown and Braaten (1998) and Krenke (1998). The approach uses the scattering signal determined by the difference in brightness temperature between 10 and 36 GHz at vertical and horizontal polarizations (Tb10V-Tb36V and Tb10H-Tb36H). A variable parameter (a) is calculated from the polarization difference at 36 GHz brightness temperatures which is used to multiply the brightness temperature differences (Tb10V-Tb36V and Tb10H-Tb36H). The overall approach is split into two parts with one part retrieving snow depth for the fraction of a footprint that is forest covered and the other retrieving the fraction that is forest-free. There have