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Glen E. Liston, Daniel L. Birkenheuer, Christopher A. Hiemstra, Donald W. Cline, and Kelly Elder

development at ESRL utilizes a 10-km horizontal grid (125 × 105) with 21 isobaric vertical levels and an hourly temporal resolution. The purpose of a system such as LAPS is to not only provide an up-to-date atmospheric state representation for nowcasting and assessment but also serve as a mechanism to initialize local-scale mesoscale weather forecast models. LAPS makes use of a wide range of observational datasets as part of its analyses, including 1) surface observations from regional surface networks

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Rafał Wójcik, Konstantinos Andreadis, Marco Tedesco, Eric Wood, Tara Troy, and Dennis Lettenmeier

eigenvalues and eigenvectors technique (e.g., Jin 1993 ). The brightness temperatures are obtained by considering the boundary conditions, which provide the weights of the elements of the base of eigenvectors. c. The Microwave Emission Model of Layered Snowpack model In MEMLS ( Mätzler and Wiesmann 1999 ; Wiesmann and Mätzler 1999 ), the snow cover is thought to be a stack of horizontal layers. Each layer is characterized by a thickness, a correlation length, its density, liquid water content, and

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Kelly Elder, Don Cline, Glen E. Liston, and Richard Armstrong

1. Introduction Snowpack measurements have been taken in North America for nearly 100 years, with the objective of increasing our ability to forecast runoff from snow-covered regions. Point measurements have been the norm, although short transects from snow courses provide a limited representation of the variability of the spatial nature of snow water equivalent (SWE). The snowpack telemetry (SNOTEL) system of the Natural Resources Conservation Service (NRCS) gives regional SWE information in a

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Glen E. Liston, Christopher A. Hiemstra, Kelly Elder, and Donald W. Cline

observations. Thus, one of the values of this scheme is improved simulation of snow-related distributions throughout the entire snow season, even when observations are only available sporadically or late in the accumulation and/or ablation periods. Because of this, the technique is particularly applicable to reanalysis applications, such as those presented herein. The methodology includes the ability to stratify the assimilation into regions where either the observations and/or model has unique error

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Glen E. Liston and Christopher A. Hiemstra

. Although we have focused on snow-related variables, the same techniques could be applied to other variables as part of other applications. In addition, the general methodologies are adaptable for use in a wide range of terrestrial modeling systems, including those focusing on land surface hydrology and ecosystem processes. Acknowledgments The authors would like to thank Svetlana Berezovskaya and Kelly Elder for their thorough and insightful reviews of this paper. This work was supported by NASA Grants

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D. Marks, A. Winstral, G. Flerchinger, M. Reba, J. Pomeroy, T. Link, and K. Elder

already limited water resources in the western United States ( Barnett et al. 2005 ) and will require improved monitoring ( Schaefer and Werner 1996 ; Abramovich and Pattee 1999 ). Furthermore, as empirical methods calibrated on past climate conditions become less reliable, a more physically based spatially explicit approach to forecasting melt from the seasonal snow cover across the region ( Garen and Marks 1998 , 2005 ) is essential. A number of studies have focused on both measuring and modeling

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Susan Frankenstein, Anne Sawyer, and Julie Koeberle

approximations, climate-feedback research, remote sensing applications, and hydrological modeling and forecasting ( Hinzman and Kane 1991 ; Shook et al. 1993 ; Baral and Gupta 1997 ; Harms and Chanasyk 1998 ; Liston, 1999 ; Cline et al. 2003 ). Because it is impossible to physically measure the full extent and characteristics of the snowpack, numerical models are needed to help estimate the water content of the snow [snow water equivalent (SWE)] and melt-out dates. In this paper, we investigate how well

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