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

received an initial quality assurance/quality control (QA/QC) procedure to remove compromised data. Instrument specifications are available by contacting the provider listed on the data archive Web site. c. North Park eddy covariance system Two eddy covariance measurements programs were completed. The first program, Flux Over Snow Surfaces, phase I (FLOSS), was completed from 1 December 2001 to 27 March 2002 using an instrumented 20-m scaffold tower. The second program, FLOSS, phase 2 (FLOSS II), was

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

. Data processing and storage Field books were collected from the survey teams each day as they returned from the field. They were immediately examined for quality, and the data was entered into prescribed data formats on computers by the data team. Survey teams were queried that evening or the following morning for anomalies to reduce problems as a result of memory loss. The entire dataset was then reviewed by the data team and project scientists for quality control before a final dataset was

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Glen E. Liston and Christopher A. Hiemstra

as knowledge of the data collection instruments and methods, quality control and quality assurance measures, and statistical quantities. The same weighting procedures can be used to control the relative influence of the observed and model-simulated fields on the final assimilated distributions. The data assimilation methodology relies on the land surface model to account for the physical evolution of the snow cover. For example, in the simulations presented herein, SnowModel accounted for snow

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

-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) or the Weather Research and Forecasting (WRF) Model can also be used for a 4D variational application if lateral boundary conditions are not critical]. The LAPS analysis is a series of routines that then takes the local observations with other nationally disseminated data and modifies the background field to match those observations. In addition, quality control measures (buddy checking and weighting by measurement

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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

approximately 1280 m above ground level (AGL) via airborne lidar, normalized to ground controls and processed to remove noise and redundancies ( Corbley 2003 ). The elevation observations have approximately 1.5-m horizontal spacing and approximately 0.05-m vertical tolerances. The pixel size of the orthophotographs is 0.15 m. The snow-free and snow-covered elevation data with the orthoimagery provide detailed information about the distribution of snow depth in relation to vegetation distribution and height

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

cells that covered a CLPX MSA was used in the MicroMet–SnowModel–SnowAssim simulations ( Fig. 1b ). To prepare the meteorological station and LAPS datasets for the model simulations, the MicroMet preprocessor ( Liston and Elder 2006b ) was used to analyze and correct the original data. First, missing values were identified. Second, the preprocessor performed three quality assurance/quality control (QA/QC) data tests following Meek and Hatfield (1994) . Test 1 checked for values outside acceptable

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

of w ′ s ′ denotes the time average of the instantaneous covariance of w and s . Raw 10-Hz data were collected and postprocessed. For this analysis, postprocessing included statistical analysis for quality control, determination of the appropriate averaging period ( Vickers and Mahrt 2003 ), correction for sonic temperature ( Schotanus et al. 1983 ) and density effects ( Webb et al. 1980 ), and tilt correction ( Mahrt et al. 2000 ). Time series data were first processed using Quality Control

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Nick Rutter, Don Cline, and Long Li

snow surface and then at the boundaries of each 10-cm interval. 3) Quality control and data manipulation CLPX meteorological measurements were quality controlled to a level 1 standard ( Williams et al. 1999 ) where data at a 10-min frequency were calibrated, outliers were removed, and supporting metadata were supplied. These level 1 standard data are publicly available ( Elder and Goodbody 2005 ). In addition, estimates of radiometric snow surface temperature were made from measured outgoing

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

. The advantage of using lidar data is that canopy information derived from it can be obtained over a larger area than is practical using in situ measurements. The use of lidar data to derive canopy cover, height, and crown radius information for GORT model parameterization is explored here. Lidar data for the Fool Creek ISA were collected on 19 September 2003 at approximately 4500 feet elevation, normalized to ground controls and processed to remove noise and redundancies ( Miller 2003 ). The data

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

generally recognized that there are several features of radiation transmission, extinction, and reflectance that might cause persistent spatial patterns in irradiance to melting snow. These features are strongly controlled by the three-dimensional spatial distribution and the arrangement of forest foliage at both the individual tree and forest stand scales ( Anderson 1966 ; Nilson 1971 ; Pukkala et al. 1991 ). These in turn are related to ecological factors such as species composition ( Jarvis et al

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