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a high degree of subjectivity in this measurement scheme but time, logistics, and cost precluded more robust sampling methods. The a and b axes of a small, medium, and large grain out of each layer were recorded to the nearest 0.1 mm. 4) Soil moisture Two soil cores were taken at the base of each snow pit. An attempt was made to sample the top 0.20 m of soil but rocks, ice, and other factors resulted in variable achievable depths. The actual depth of the core was recorded in each case
a high degree of subjectivity in this measurement scheme but time, logistics, and cost precluded more robust sampling methods. The a and b axes of a small, medium, and large grain out of each layer were recorded to the nearest 0.1 mm. 4) Soil moisture Two soil cores were taken at the base of each snow pit. An attempt was made to sample the top 0.20 m of soil but rocks, ice, and other factors resulted in variable achievable depths. The actual depth of the core was recorded in each case
1. Introduction Water stored in snowpacks and soils in the western United States is particularly important for natural ecosystems, public consumption, and industry because snowmelt accounts for approximately 80% of the soil moisture in semiarid environments in the western United States ( Marks and Winstral 2001 ). The intricate process of snow cover depletion and soil moisture recharge is spatially and physically complex, and an assessment of its behavior is essential for water balance
1. Introduction Water stored in snowpacks and soils in the western United States is particularly important for natural ecosystems, public consumption, and industry because snowmelt accounts for approximately 80% of the soil moisture in semiarid environments in the western United States ( Marks and Winstral 2001 ). The intricate process of snow cover depletion and soil moisture recharge is spatially and physically complex, and an assessment of its behavior is essential for water balance
lower crossarm height. Radiation and leaf wetness were measured at 10 m. Radiation was measured by two net radiometers, one of which partitioned the radiation into component values (incoming and outgoing short- and longwave). Barometric pressure, snow depth, and surface temperature were measured at the level of the lower cross arm. Volumetric soil moisture content and soil temperature were measured at 0.05, 0.20, and 0.50-m depth below ground level. Snowpack temperature profiles were measured on a
lower crossarm height. Radiation and leaf wetness were measured at 10 m. Radiation was measured by two net radiometers, one of which partitioned the radiation into component values (incoming and outgoing short- and longwave). Barometric pressure, snow depth, and surface temperature were measured at the level of the lower cross arm. Volumetric soil moisture content and soil temperature were measured at 0.05, 0.20, and 0.50-m depth below ground level. Snowpack temperature profiles were measured on a
, two in discontinuous canopy, and one approximately 500 m west of the LSOS) and an eddy covariance (EC) system in 2003 ( Marks et al. 2008 ). The measured meteorological data included solar and longwave downward radiation, snow, soil and air temperature, relative humidity, wind speed and direction, precipitation, soil heat flux, and soil moisture. Arrays of ten solar and two longwave radiometers sampled energy beneath the uniform coniferous canopy and the discontinuous canopy. The U.S. Forest
, two in discontinuous canopy, and one approximately 500 m west of the LSOS) and an eddy covariance (EC) system in 2003 ( Marks et al. 2008 ). The measured meteorological data included solar and longwave downward radiation, snow, soil and air temperature, relative humidity, wind speed and direction, precipitation, soil heat flux, and soil moisture. Arrays of ten solar and two longwave radiometers sampled energy beneath the uniform coniferous canopy and the discontinuous canopy. The U.S. Forest
February 2002, IOP2 from 24 to 30 March 2002, IOP3 from 17 to 25 February 2003, and IOP4 from 25 March through 1 April 2003. In this paper, we summarize the CLPX airborne remote sensing datasets from four categories that span three spectral regions: gamma radiation observations, multi- and hyperspectral optical imaging and optical altimetry, and passive and active microwave. 2. Gamma radiation snow and soil moisture surveys Natural terrestrial gamma radiation is emitted from the potassium, uranium, and
February 2002, IOP2 from 24 to 30 March 2002, IOP3 from 17 to 25 February 2003, and IOP4 from 25 March through 1 April 2003. In this paper, we summarize the CLPX airborne remote sensing datasets from four categories that span three spectral regions: gamma radiation observations, multi- and hyperspectral optical imaging and optical altimetry, and passive and active microwave. 2. Gamma radiation snow and soil moisture surveys Natural terrestrial gamma radiation is emitted from the potassium, uranium, and
et al. 1998 ; Nelson et al. 1998 ). Snow’s low thermal conductivity insulates the soil from low winter air temperatures, leaving soils much warmer than they would be otherwise, and leading to lower nighttime air temperatures ( Taras et al. 2002 ; Zhang et al. 2003 ). Snow cover affects soil–moisture conditions, runoff, and active-layer characteristics ( Kane et al. 1991 ; Hinzman et al. 1996 ; Marsh 1999 ), and the spatial distribution of snow can influence spring snowmelt runoff timing
et al. 1998 ; Nelson et al. 1998 ). Snow’s low thermal conductivity insulates the soil from low winter air temperatures, leaving soils much warmer than they would be otherwise, and leading to lower nighttime air temperatures ( Taras et al. 2002 ; Zhang et al. 2003 ). Snow cover affects soil–moisture conditions, runoff, and active-layer characteristics ( Kane et al. 1991 ; Hinzman et al. 1996 ; Marsh 1999 ), and the spatial distribution of snow can influence spring snowmelt runoff timing
process, data assimilation is used to constrain and enhance a model-simulated field (e.g., snow depth or water equivalent) using available observations that are irregularly (or regularly) distributed in time and space. Data assimilation applications are common in the atmospheric sciences (see Daley 1991 ) and are becoming more frequent in land surface applications, such as those related to soil moisture (e.g., Houser et al. 1998 ; Reichle et al. 2002 ; Rodell et al. 2004 ). In contrast, snow
process, data assimilation is used to constrain and enhance a model-simulated field (e.g., snow depth or water equivalent) using available observations that are irregularly (or regularly) distributed in time and space. Data assimilation applications are common in the atmospheric sciences (see Daley 1991 ) and are becoming more frequent in land surface applications, such as those related to soil moisture (e.g., Houser et al. 1998 ; Reichle et al. 2002 ; Rodell et al. 2004 ). In contrast, snow
: conservation of momentum (equations of motion), conservation of energy (first law of thermodynamics), and conservation of mass for dry air and moisture (continuity equations). The resulting analysis is an optimal combination of the available observations and model representation. Thus, the analysis dataset contains the advantage of spatial and temporal continuity but also includes the possible disadvantage of being removed from the original observations. In this paper, we summarize the atmospheric analysis
: conservation of momentum (equations of motion), conservation of energy (first law of thermodynamics), and conservation of mass for dry air and moisture (continuity equations). The resulting analysis is an optimal combination of the available observations and model representation. Thus, the analysis dataset contains the advantage of spatial and temporal continuity but also includes the possible disadvantage of being removed from the original observations. In this paper, we summarize the atmospheric analysis
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
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
the soil was significantly warmer than the snow. Here, R n was near zero, with thermal and solar radiation canceling each other. During the mid period, as solar radiation increased with higher sun angles, net radiation R n replaced soil heat and turbulent fluxes G and H + L υ E , respectively, as the dominant energy balance processes. As the snow cover warms, the temperature gradient between the soil and snow is reduced, as are the temperature and moisture gradients between the atmosphere
the soil was significantly warmer than the snow. Here, R n was near zero, with thermal and solar radiation canceling each other. During the mid period, as solar radiation increased with higher sun angles, net radiation R n replaced soil heat and turbulent fluxes G and H + L υ E , respectively, as the dominant energy balance processes. As the snow cover warms, the temperature gradient between the soil and snow is reduced, as are the temperature and moisture gradients between the atmosphere
