Cold Land Processes Research Articles

Observing System Simulation of Snow Microwave Emissions Over Data Sparse Regions—Part II: Multilayer Physics

A multilayer formulation of snow hydrological processes implemented in an existing snow hydrology-emission model (MLSHM-ML) was applied in observing system simulation mode (OSS) to two very different climatic and physiographic regions (Valdai, Russia and Colorado, USA) for both wet and dry snow regimes, and over multiple years. The results were evaluated against ground-based observations of snowpack physical properties and microwave radiometric observations from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) observations at 18-19-, 22-23-, and 36-37-GHz vertical and horizontal polarizations (V-pol and H-pol, respectively). Whereas snow water equivalent (SWE) results are similar to the results obtained with single-layer physics when the snow is dry, the multilayer physics have a better skill at capturing the overall temporal evolution of bulk density, snow temperature, and snow depth during the accumulation season, and at the onset and throughout the melting season. However, snow density profiles overestimate density at the bottom of the snowpack, consistent with the lack of an explicit representation of depth hoar in the rearrangement of mass and grain size distribution in the snowpack. Regarding the radiometric behavior, the multilayer SMMR OSS for Valdai shows improved results for nighttime simulations (descending SMMR paths, 11 P.M. LST) and H-pol (~ 3-5 K decrease in error statistics), particularly at 37 GHz. For daytime simulations (ascending SMMR paths, 11 A.M. LST), there are modest improvements at 18 (~ 1 K) and 37 GHz (~ 2-3 K) for H-pol, and generally loss of skill for V-pol at all frequencies. Systematic improvements at nighttime but not during daytime suggest that surface heterogeneities, including subgrid scale variability of transient melting, play an important role on surface emissivity. This is the case for cold land process experiment in Colorado, where spatial variability in fractional forest cover, geology, and complex topography explains the modest differences between the single and multilayer SSM/I OSS for H-pol, whereas significant gains (~ 4-8 K decrease in error statistics) were attained for V-pol at 37 GHz only. For AMSR-E, the multilayer OSS looses skill for H-pol, and only bias and mean absolute error improve for V-pol at all frequencies. These somewhat mixed results suggest that representation of snow stratigraphy alone is not sufficient to improve the OSS ability to describe the nonlinear interactions among hydrologic and electromagnetic processes. Chief among these are the temporal evolution of snow correlation length with depth and the representation of subgrid scale variability constrained by the spatial resolution and inherent uncertainty of the meteorological forcing. Nevertheless, the multilayer OSS improved performance at 37 GHz is an important finding toward reducing ambiguity in the sensitivity of 37-GHz H-pol brightness temperature to SWE in retrieval models.

The effects of variation in snow properties on passive microwave snow mass estimation

Estimating snow mass at continental scales is difficult, but important for understanding land-atmosphere interactions, biogeochemical cycles and the hydrology of the Northern latitudes. Remote sensing provides the only consistent global observations, but with unknown errors. We test the theoretical performance of the Chang algorithm for estimating snow mass from passive microwave measurements using the Helsinki University of Technology (HUT) snow microwave emission model. The algorithm's dependence upon assumptions of fixed and uniform snow density and grainsize is determined, and measurements of these properties made at the Cold Land Processes Experiment (CLPX) Colorado field site in 2002–2003 used to quantify the retrieval errors caused by differences between the algorithm assumptions and measurements. Deviation from the Chang algorithm snow density and grainsize assumptions gives rise to an error of a factor of between two and three in calculating snow mass. The possibility that the algorithm performs more accurately over large areas than at points is tested by simulating emission from a 25km diameter area of snow with a distribution of properties derived from the snow pit measurements, using the Chang algorithm to calculate mean snow-mass from the simulated emission. The snow mass estimation from a site exhibiting the heterogeneity of the CLPX Colorado site proves only marginally different than that from a similarly-simulated homogeneous site. The estimation accuracy predictions are tested using the CLPX field measurements of snow mass, and simultaneous SSM/I and AMSR-E measurements.

Sea Ice Emissivities and Effective Temperatures at MHS Frequencies: An Analysis of Airborne Microwave Data Measured During Two Arctic Campaigns

Satellite-based sounding of the temperature and humidity of the lower troposphere is only carried out over open sea surfaces because of large uncertainties in the surface emissivity and effective emitting temperature of other surfaces. The study of sea ice and snow surface emissivities at Microwave Humidity Sounder (MHS) frequencies has been the focus of two airborne campaigns carried out by the Met Office over the past decade to promote the assimilation of lower tropospheric sounding radiances over the polar regions. The Polar Experiment (POLEX) campaign occurred during March 2001. Five flights were carried out over the Arctic Ocean north of Svalbard. The Cold Land Processes II (CLPX-II) campaign took place during February 2008. The sea ice portion of CLPX-II consisted of four flights sampling the emissivities and effective temperatures in the Chukchi and Beaufort Seas and frozen wetlands near Barrow, Alaska. This paper summarizes the findings of a recent analysis from the CLPX-II and POLEX campaigns. First, time series of Lambertian emissivities and effective temperatures at MHS frequencies are retrieved for each flight. These time series and the satellite imagery are then used to classify the surfaces over flown. The behaviors of the emissivity spectra with changing surface type are discussed in terms of changes in the ice and snow conditions on the surface. The difference between 89- and 157-GHz emissivities is found to be related to both the snow depth and the relative amounts of depth hoar and wind slab within the snowpack.

A blended global snow product using visible, passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)

In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.

Cold Regions Hydrology High-Resolution Observatory for Snow and Cold Land Processes

Snow is a critical component of the global water cycle and climate system, and a major source of water supply in many parts of the world. There is a lack of spatially distributed information on the accumulation of snow on land surfaces, glaciers, lake ice, and sea ice. Satellite missions for systematic and global snow observations will be essential to improve the representation of the cryosphere in climate models and to advance the knowledge and prediction of the water cycle variability and changes that depend on snow and ice resources. This paper describes the scientific drivers and technical approach of the proposed Cold Regions Hydrology High-Resolution Observatory (CoReH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O) satellite mission for snow and cold land processes. The sensor is a synthetic aperture radar operating at 17.2 and 9.6 GHz, VV and VH polarizations. The dual-frequency and dual-polarization design enables the decomposition of the scattering signal for retrieving snow mass and other physical properties of snow and ice.

Comparison between a multi-scattering and multi-layer snow scattering model and its parameterized snow backscattering model

Snow Water Equivalent (SWE) is a crucial parameter in the study of climatology and hydrology. Active microwave remote sensing is one of the most promising techniques for estimating the distribution of SWE at high spatial resolutions in large areas. Development of reliable and accurate inversion techniques to recover SWE is one of the most important tasks in current microwave researches. However, a number of snow pack properties, including snow density, particle size, crystal shape, stratification, ground surface roughness and soil moisture, affect the microwave scattering signals and need to be properly modeled and exploited. In this paper, we developed a multi-layer, multi-scattering model for dry snow based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from NASA Cold Land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3) and SARALPS-2007 field experiment supported by ESA. The results indicated that model predictions were in good agreement with field observations. With the confirmed confidence, the analyses on multiple scattering, scatterer shape, and snow stratification effects were further made based on the model simulations. Furthermore, a parameterized snow backscattering model with a simple form and high computational efficiency was developed using a database generated by the multiple-scattering model. For a wide range of snow and soil properties, this parameterized model agrees well with the multiple-scattering model, with the root mean square error 0.20 dB, 0.24 dB and 0.43 dB for VV, HH and VH polarizations, respectively. This simplified model can be useful for the development of SWE retrieval algorithm and for fast simulations of radar signals over snow cover in land data assimilation systems.

Active Remote Sensing of Snow Using NMM3D/DMRT and Comparison With CLPX II Airborne Data

We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT.

Airborne Ku-Band Polarimetric Radar Remote Sensing of Terrestrial Snow Cover

Characteristics of the Ku-band polarimetric scatterometer (POLSCAT) data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15-0.5-dB increases in backscatter for every 1 cm of snow-water-equivalent (SWE) accumulation for areas with short vegetation (sagebrush and pasture). The region with the smaller amount of biomass, signified by the backscatter in November, seemed to have the stronger backscatter response to SWE in decibels. The data also showed the impact of surface hoar growth and freeze/thaw cycles, which created large snow-grain sizes, ice crust layers, and ice lenses and consequently increased the radar signals by a few decibels. The copolarized HH/VV backscatter ratio seems to indicate double-bounce scattering between the ground surface and snow or vegetation. The cross-polarized backscatter [vertical-horizontal (VH)] showed not only the influence of vegetation but also the strong response to snow accumulation. The observed HV/VV ratio suggests the importance of multiple scattering or nonspherical scattering geometry of snow grain in the dense-media radiative transfer scattering model. Comparison of the POLSCAT and QuikSCAT data was made and confirmed the effects of mixed terrain covers in the coarse-resolution QuikSCAT data.

Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar

In this study retrievals of forest canopy height were obtained through adjustment of a simple geometric-optical (GO) model against red band surface bidirectional reflectance estimates from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression relationships with the isotropic, geometric, and volume scattering kernel weights of a Li-Ross kernel-driven bidirectional reflectance distribution function (BRDF) model. The height retrievals were assessed using discrete return lidar data acquired over sites in Colorado as part of the Cold Land Processes Experiment (CLPX) and used with fractional crown cover retrievals to obtain aboveground woody biomass estimates. For all model runs with reasonable backgrounds and initial b/r (vertical to horizontal crown radii) values < 2.0, root mean square error (RMSE) distributions were centered between 2.5 and 3.7 m while R 2 distributions were centered between 0.4 and 0.7. The MISR/GO aboveground biomass estimates predicted via regression on fractional cover and mean canopy height for the CLPX sites showed good agreement with U.S. Forest Service Interior West map data (adjusted R 2 = 0.84). The implication is that multiangle sensors such as MISR can provide spatially contiguous retrievals of forest canopy height, cover, and aboveground woody biomass that are potentially useful in mapping distributions of aboveground carbon stocks, tracking disturbance, and in initializing, constraining, and validating ecosystem models. This is important because the MISR record is spatially comprehensive and extends back to the year 2000 and the launch of the NASA Earth Observing System (EOS) Terra satellite; it might thus provide a ~ 10-year baseline record that would enhance exploitation of data from the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, as well as furthering realization of synergies with active instruments.

Millimeter Microwave Emissivities and Effective Temperatures of Snow-Covered Surfaces: Evidence for Lambertian Surface Scattering

The remote sounding of atmospheric temperature and humidity via satelliteborne microwave instruments requires knowledge of the surface emissivity in order to separate the atmospheric and surface components of emission. To date, microwave sounding of the lower troposphere is only carried out over open sea surfaces because of large uncertainties in the surface emissivity and effective emitting temperature of other surfaces. In addition to this, recent literature has brought into doubt the assumption of specular reflection which is commonly used in retrieving surface emissivity. This paper presents analysis of airborne data in the 89-183-GHz frequency range taken over snow-covered surfaces during the second phase of the Cold Land Processes Experiment (CLPX-II). Two methods for retrieving emissivity and effective temperature using the three 183-GHz channels are introduced. Both methods give independent estimates of the emissivity and effective temperature. These methods can be applied to estimate emissivity under both specular reflection and diffuse scattering. Evaluation of emissivities under diffuse scattering requires the summing of contributions over the upper half sphere. An analysis of flight data from CLPX-II shows that Lambertian scattering gives much more consistent solutions for emissivities and effective temperatures than the traditional approach assuming specular reflection.

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.

NASA Cold Land Processes Experiment (CLPX 2002/03): Airborne Remote Sensing

Abstract This paper describes the airborne data collected during the 2002 and 2003 Cold Land Processes Experiment (CLPX). These data include gamma radiation observations, multi- and hyperspectral optical imaging, optical altimetry, and passive and active microwave observations of the test areas. The gamma observations were collected with the NOAA/National Weather Service Gamma Radiation Detection System (GAMMA). The CLPX multispectral optical data consist of very high-resolution color-infrared orthoimagery of the intensive study areas (ISAs) by TerrainVision. The airborne hyperspectral optical data consist of observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Optical altimetry measurements were collected using airborne light detection and ranging (lidar) by TerrainVision. The active microwave data include radar observations from the NASA Airborne Synthetic Aperture Radar (AIRSAR), the Jet Propulsion Laboratory’s Polarimetric Ku-band Scatterometer (POLSCAT), and airborne GPS bistatic radar data collected with the NASA GPS radar delay mapping receiver (DMR). The passive microwave data consist of observations collected with the NOAA Polarimetric Scanning Radiometer (PSR). All of the airborne datasets described here and more information describing data collection and processing are available online.

NASA Cold Land Processes Experiment (CLPX 2002/03): Ground-Based and Near-Surface Meteorological Observations

Abstract A short-term meteorological database has been developed for the Cold Land Processes Experiment (CLPX). This database includes meteorological observations from stations designed and deployed exclusively for CLPX as well as observations available from other sources located in the small regional study area (SRSA) in north-central Colorado. The measured weather parameters include air temperature, relative humidity, wind speed and direction, barometric pressure, short- and longwave radiation, leaf wetness, snow depth, snow water content, snow and surface temperatures, volumetric soil moisture content, soil temperature, precipitation, water vapor flux, carbon dioxide flux, and soil heat flux. The CLPX weather stations include 10 main meteorological towers, 1 tower within each of the nine intensive study areas (ISA) and one near the local scale observation site (LSOS); and 36 simplified towers, with one tower at each of the four corners of each of the nine ISAs, which measured a reduced set of parameters. An eddy covariance system within the North Park mesocell study area (MSA) collected a variety of additional parameters beyond the 10 standard CLPX tower components. Additional meteorological observations come from a variety of existing networks maintained by the U.S. Forest Service, U.S. Geological Survey, Natural Resource Conservation Service, and the Institute of Arctic and Alpine Research. Temporal coverage varies from station to station, but it is most concentrated during the 2002/03 winter season. These data are useful in local meteorological energy balance research and for model development and testing. These data can be accessed through the National Snow and Ice Data Center Web site.

NASA Cold Land Processes Experiment (CLPX 2002/03): Field Measurements of Snowpack Properties and Soil Moisture

Abstract A field measurement program was undertaken as part NASA’s Cold Land Processes Experiment (CLPX). Extensive snowpack and soil measurements were taken at field sites in Colorado over four study periods during the two study years (2002 and 2003). Measurements included snow depth, density, temperature, grain type and size, surface wetness, surface roughness, and canopy cover. Soil moisture measurements were made in the near-surface layer in snow pits. Measurements were taken in the Fraser valley, North Park, and Rabbit Ears Pass areas of Colorado. Sites were chosen to gain a wide representation of snowpack types and physiographies typical of seasonally snow-covered regions of the world. The data have been collected with rigorous protocol to ensure consistency and quality, and they have undergone several levels of quality assurance to produce a high-quality spatial dataset for continued cold lands hydrological research. The dataset is archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

Application of Target Decomposition Theorems Over Snow-Covered Forested Areas

This paper compares two well-known polarimetric decomposition theorems, Cloude-Pottier and Freeman-Durden, applied to L- and C-band Airborne Polarimetric Synthetic Aperture Radar (AIRSAR-POLSAR) data acquired during the Cold-Land Processes Field Experiments. Three field campaigns were carried out in February 2002, March 2002, and March 2003 over a snow-covered open terrain, a sparse coniferous forest, and a dense coniferous forest. The analysis evaluates the ability of the two target decomposition methods for the identification and understanding of the main scattering mechanisms.

Comparing Simulated and Measured Sensible and Latent Heat Fluxes over Snow under a Pine Canopy to Improve an Energy Balance Snowmelt Model

AbstractDuring the second year of the NASA Cold Land Processes Experiment (CLPX), an eddy covariance (EC) system was deployed at the Local Scale Observation Site (LSOS) from mid-February to June 2003. The EC system was located beneath a uniform pine canopy, where the trees are regularly spaced and are of similar age and height. In an effort to evaluate the turbulent flux calculations of an energy balance snowmelt model (SNOBAL), modeled and EC-measured sensible and latent heat fluxes between the snow cover and the atmosphere during this period are presented and compared. Turbulent fluxes comprise a large component of the snow cover energy balance in the premelt and ripening period (March–early May) and therefore control the internal energy content of the snow cover as melt accelerates in late spring. Simulated snow cover depth closely matched measured values (RMS difference 8.3 cm; Nash–Sutcliff model efficiency 0.90), whereas simulated snow cover mass closely matched the few measured values taken during the season. Over the 927-h comparison period using the default model configuration, simulated sensible heat H was within 1 W m−2, latent heat LυE within 4 W m−2, and cumulative sublimation within 3 mm of that measured by the EC system. Differences between EC-measured and simulated fluxes occurred primarily at night. The reduction of the surface layer specification in the model from 25 to 10 cm reduced flux differences between EC-measured and modeled fluxes to 0 W m−2 for H, 2 W m−2 for LυE, and 1 mm for sublimation. When only daytime fluxes were compared, differences were further reduced to 1 W m−2 for LυE and &amp;lt;1 mm for sublimation. This experiment shows that in addition to traditional mass balance methods, EC-measured fluxes can be used to diagnose the performance of a snow cover energy balance model. It also demonstrates the use of eddy covariance methods for measuring heat and mass fluxes from snow covers at a low-wind, below-canopy site.

NASA Cold Land Processes Experiment (CLPX 2002/03): Local Scale Observation Site

Abstract The local scale observation site (LSOS) is the smallest study site (0.8 ha) of the 2002/03 Cold Land Processes Experiment (CLPX) and is located within the Fraser mesocell study area. It was the most intensively measured site of the CLPX, and measurements here had the greatest temporal component of all CLPX sites. Measurements made at the LSOS were designed to produce a comprehensive assessment of the snow, soil, and vegetation characteristics viewed by the ground-based remote sensing instruments. The objective of the ground-based microwave remote sensing was to collect time series of active and passive microwave spectral signatures over snow, soil, and forest, which is coincident with the intensive physical characterization of these features. Ground-based remote sensing instruments included frequency modulated continuous wave (FMCW) radars operating over multiple microwave bandwidths; the Ground-Based Microwave Radiometer (GBMR-7) operating at channels 18.7, 23.8, 36.5, and 89 GHz; and in 2003, an L-, C-, X- and Ku-band scatterometer radar system. Snow and soil measurements included standard snow physical properties, snow wetness, snow depth transects, and soil moisture. The stem and canopy temperature and xylem sap flux of several trees were monitored continuously. Five micrometeorological towers monitored ambient conditions and provided forcing datasets for 1D snow and soil models. Arrays of pyranometers (0.3–3 μm) and a scanning thermal radiometer (8–12 μm) characterized the variability of radiative receipt in the forests. A field spectroradiometer measured the hyperspectral hemispherical-directional reflectance of the snow surface. These measurements, together with the ground-based remote sensing, provide the framework for evaluating and improving microwave radiative transfer models and coupling them to land surface models. The dataset is archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

The Impact of Snow Model Complexity at Three CLPX Sites

Abstract Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters. All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics. A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.

Comparison of FASST and SNTHERM in Three Snow Accumulation Regimes

AbstractNumerical experiments of snow accumulation and depletion were carried out as well as surface energy fluxes over four Cold Land Processes Experiment (CLPX) sites in Colorado using the Snow Thermal model (SNTHERM) and the Fast All-Season Soil Strength model (FASST). SNTHERM is a multilayer snow model developed to describe changes in snow properties as a function of depth and time, using a one-dimensional mass and energy balance. The model is intended for seasonal snow covers and addresses conditions found throughout the winter, from initial ground freezing in the fall to snow ablation in the spring. It has been used by many researchers over a variety of terrains. FASST is a newly developed one-dimensional dynamic state-of-the-ground model. It calculates the ground’s moisture content, ice content, temperature, and freeze–thaw profiles as well as soil strength and surface ice and snow accumulation/depletion. Because FASST is newer and not as well known, the authors wanted to determine its use as a snow model by comparing it with SNTHERM, one of the most established snow models available. It is demonstrated that even though FASST is only a single-layer snow model, the RMSE snow depth compared very favorably against SNTHERM, often performing better during the accumulation phase. The surface energy fluxes calculated by the two models were also compared and were found to be similar.