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

analysis systems, begins with a first guess or a background field interpolated to a finer grid from a coarser large-scale forecast model output. The source for LAPS background fields is generally Rapid Update Cycle (RUC) forecasts (described below), but it is also configured to use Medium-Range Forecasts (MRFs), the National Centers for Environmental Prediction (NCEP) Eta Model, and Navy Operational Global Atmospheric Prediction System (NOGAPS) forecasts [high-resolution backgrounds from the fifth

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

, vegetation, and air) between the soil and the aircraft. The measurement of the attenuation (i.e., the difference between measurements over dry soil and measurements over moist soil or snow) has been found to be a reliable basis for soil moisture and SWE measurements ( Jones and Carroll 1983 ). Using the Gamma Radiation Detection System (GAMMA), this technique has been used operationally by the National Weather Service (NWS) since 1979 to measure mean areal SWE and soil moisture along aircraft flight

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

retrieval of atmospheric moisture in which the SWE problem is incidental. However, even where there is a motivation to update land surface variables, such as SWE, the assimilation of brightness temperatures ( T b ), rather than derived the SWE products, requires knowledge of snow physical properties because they affect the (surface) emissivity. National Centers for Environmental Prediction (NCEP) operational models currently use the Community Radiative Transfer Model (CRTM), which predicts TOA microwave

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Glen E. Liston and Christopher A. Hiemstra

Data Assimilation System. Bull. Amer. Meteor. Soc. , 85 , 381 – 394 . 10.1175/BAMS-85-3-381 Rutherford, I. D. , 1972 : Data assimilation by statistical interpolation of forecast error fields. J. Atmos. Sci. , 29 , 809 – 815 . 10.1175/1520-0469(1972)029<0809:DABSIO>2.0.CO;2 Seo, D-J. , Koren V. , and Cajina N. , 2003 : Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting. J. Hydrometeor. , 4 , 627 – 641 . 10

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

spatial context but provides no information on spatial structure below the scale of tens to hundreds of kilometers. Plus, the limited spatial information provided by snow courses and SNOTEL sites is rarely used in any operational hydrological context. Recently, there has been an increased interest in cold regions hydrology, and spatial information on SWE distribution is critical to improving runoff forecasting. Several extensive spatial datasets are available for researchers (e.g., Williams et al

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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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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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