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Don Cline, Simon Yueh, Bruce Chapman, Boba Stankov, Al Gasiewski, Dallas Masters, Kelly Elder, Richard Kelly, Thomas H. Painter, Steve Miller, Steve Katzberg, and Larry Mahrt

1. Introduction Airborne sensors provide many unique observing capabilities to help understand cold land processes. Aircraft platforms provide flexibility in data collection not generally found with spaceborne systems, improving opportunities for coordinating remote sensing observations with ground observations and for adapting to changing conditions. Seven airborne sensors ( Table 1 ) were used to observe the surface and near-surface of the study areas of the Cold Land Processes Experiment

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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

-related features that include considerable spatial variability at scales below those resolved by most models (e.g., Loth and Graf 1998 ; Pomeroy et al. 1998 ; Slater et al. 2001 ; Takata et al. 2003 ; Liston 2004 ). In light of the role snow cover plays in influencing land, atmospheric, hydrological, and ecosystem processes, it is essential that local, regional, and global models used to simulate these processes be capable of accurately describing seasonal snow evolution. In contrast to many atmospheric

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Jicheng Liu, Curtis E. Woodcock, Rae A. Melloh, Robert E. Davis, Ceretha McKenzie, and Thomas H. Painter

1. Introduction Snow, because of its unique properties such as high albedo and low thermal conductivity, affects land surface radiation budgets and water balance ( Yang et al. 1999 ). Significant gains have been made in snow cover mapping using remotely sensed data in recent decades, but the presence of forests continues to present challenges ( Simpson et al. 1998 ; Hall et al. 1998 ; Hall et al. 2002 ; Dozier and Painter 2004 ). An understanding of the manner in which forest canopies

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Susan Frankenstein, Anne Sawyer, and Julie Koeberle

the Snow Thermal model (SNTHERM), a well-established snow model, and the Fast All-Season Soil Strength model (FASST), a new soil–vegetation transfer model, capture observed snow surface temperatures as well as snow depth, SWE, and the individual surface energy terms. SNTHERM is a multilayered one-dimensional energy and water balance point model designed to predict temperature profiles within strata of snow and frozen soil at nonforested sites ( Jordan 1991 ). SNTHERM uses time series

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

National Oceanic and Atmospheric Administration’s (NOAA) Earth System Research Laboratory (ESRL), combines numerous observed meteorological datasets into a unified atmospheric analysis, typically with a time interval of an hour or less. An analysis contains both spatially and temporally continuous atmospheric state variables, in addition to special atmospheric- and land-based fields over Colorado, Wyoming, and parts of the surrounding states ( Fig. 1 ). The quasi-operational analysis used for

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

of the snow cover is not easily observed, the mass balance (depth, density, and melt rates) is; if the internal energetics are accounted for, they can be effectively modeled. If the EC-measured fluxes can be used to evaluate the accuracy of the simulated turbulent fluxes, then this will provide a mechanism for improving the modeling approach. 2. Site description The research described in this paper was undertaken as part of the NASA Cold Land Processes Experiment (CLPX; Elder et al. 2009

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

difference snow index. Remote Sens. Environ. , 89 , 351 – 360 . 10.1016/j.rse.2003.10.016 Tait, A. B. , 1998 : Estimation of snow water equivalent using passive microwave data. Remote Sens. Environ. , 64 , 286 – 291 . 10.1016/S0034-4257(98)00005-4 Zhang, T. , and Armstrong R. L. , 2001 : Soil freeze/thaw cycles over snow-free land detected by passive microwave remote sensing. Geophys. Res. Lett. , 28 , 763 – 766 . 10.1029/2000GL011952 Fig . 1. Snow and vegetation cover fractions

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

land use agreements. Figure 2 shows a schematic of relative locations of instruments on the towers. Figure 3 shows the Michigan River meteorological tower and precipitation gauge. b. Corner site meteorological towers A secondary network of meteorological towers was deployed at each of the four corners of each of the nine ISAs, giving a total of 36 sites where a reduced set of parameters were measured. Each tower consisted of a guyed 4-m aluminum pole with a cross arm for instruments

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