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

cycles within land, atmospheric, hydrologic, and ecologic systems, it is essential that models used to describe these systems include snow-related processes. Key snow distribution and evolution features include the considerable spatial and temporal variability that characterize snow accumulation and ablation processes. These variations are controlled by a combination of spatially and temporally variable atmospheric forcing conditions and how those forcings interact with relatively static local

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Janet Hardy, Robert Davis, Yeohoon Koh, Don Cline, Kelly Elder, Richard Armstrong, Hans-Peter Marshall, Thomas Painter, Gilles Castres Saint-Martin, Roger DeRoo, Kamal Sarabandi, Tobias Graf, Toshio Koike, and Kyle McDonald

on the brightness temperature of the snow cover, and 2) develop a scheme, which couples a radiative transfer model with a physical-based snow model using data assimilation. To develop and validate the assimilation scheme, a very detailed dataset was collected, which comprised forcing data, radiometer observations, and snowpack properties. The radiometer was mounted on a flexible positioning system, which allowed for variety in the observation direction and incident angle. Two observation areas

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Glen E. Liston and Christopher A. Hiemstra

patterns, but the magnitudes are often deficient. This error can be the result of limitations in the model physics, errors in meteorological forcing (e.g., snow precipitation; Liston and Sturm 2004 ), and deficiencies in boundary conditions [e.g., relatively low-resolution topography and vegetation data; Liston and Sturm (1998) ; Hiemstra et al. (2006) ; Liston et al. (2007) ]. These inadequacies can propagate the associated errors in many ways ( Burrough and McDonnell 1998 ). For example

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Kelly Elder, Angus Goodbody, Don Cline, Paul Houser, Glen E. Liston, Larry Mahrt, and Nick Rutter

, unpublished manuscript). The purpose of measuring the meteorological parameters was to quantify variability from local to regional scales within various snow environments and to archive forcing data for algorithm and model development and verification. The network included 10 main meteorological towers and one eddy covariance site. Data from these sites and other existing meteorological networks provided a high-quality dataset with nested spatial coverage. Additional spatial datasets were collected during

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

updated daily using (in operations) a direct insertion approach. The observations are the Air Force Weather Agency (AFWA) global snow depth analysis (SNODEP), which is based on the interpolation of daily station reports (K. Mitchell 2007, personal communication). The approach we propose to develop in this paper is a part of a broader National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Joint Center for Satellite Data Assimilation (JCSDA) sponsored

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Nick Rutter, Don Cline, and Long Li

meteorological forcings at points. The operational version of the NSM (used by the NOHRSC for the 2004–06 snow seasons) is a one-dimensional (vertical) model run over 1 km × 1 km grid cells that are spatially uncoupled from each other (where advection of mass from one cell to another is not considered). This operational version of the NSM was forced with observed meteorological data at each of the five sites (air temperature, relative humidity, wind speed, incoming and outgoing shortwave radiation, and

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Susan Frankenstein, Anne Sawyer, and Julie Koeberle

). FASST, a year-round state-of-the-ground model, was initially developed to provide information to mobility and sensor performance algorithms for military purposes. It has since been used in nonmilitary situations ( Holcombe 2004 ; Sawyer 2007 ; Frankenstein et al. 2007 ). FASST predicts soil moisture, ice and vapor content, and temperature as a function of depth as well as snow and ice accretion/depletion as a function of meteorological forcing and site characteristics. Incorporated into the model

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Robert E. Davis, Thomas H. Painter, Rick Forster, Don Cline, Richard Armstrong, Terry Haran, Kyle McDonald, and Kelly Elder

river basins. The coarse spatial resolution also leads to complex mixed pixels over much of the temperate latitudes, which presents some of the more difficult current challenges to algorithm developers ( Chang et al. 1996 ). Passive microwave sensing has also shown promise for assessing the freeze–thaw state of the land surface ( Zhang and Armstrong 2001 ; McDonald et al. 2004 ). The Air Force Space and Missile Systems Center runs the Defense Meteorological Satellite Program (DMSP). As part of the

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

set from the initial snow conditions or calculated at the end of the last model time step. The cold contents are calculated from the specific heat of ice, layer temperature, and specific mass. The specific heat of ice for each layer is calculated as a function of layer temperature. Table 1 presents the state and forcing variables required by the model. State variables are input as initial conditions and then updated by the model during the run. Forcing variables are used by the model to predict

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John Pomeroy, Chad Ellis, Aled Rowlands, Richard Essery, Janet Hardy, Tim Link, Danny Marks, and Jean Emmanuel Sicart

CVs can be evaluated as a function of sampling interval for both stands and sky conditions. The slope between spatial standard deviation ( Y ) and mean ( X ), forcing an intercept of zero, was used to estimate the CVs for each day of observations. The CVs were calculated for the full range of intervals from 1 min to 1 day. The R 2 values for these linear regressions varied from 0.91–0.97 for the discontinuous canopy to 0.87–0.95 for the uniform canopy on clear days, with lower R 2 values of 0

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