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The excavation of snow layers was used to compare observed and modeled plot-scale brightness temperature (TB) values for 19 and 37 GHz, with regard to snow water equivalent (SWE), snow type, grain size, and layered structure, for 3 land cover types acquired near Churchill, Manitoba, Canada in March of 2010. In situ snow geophysical measurements were input to the Helsinki University of Technology (HUT) multi-layer snow emission model, and performance was characterized by RMSE and MBE. Emission scattering from depth hoar was disproportionate to its SWE contribution when compared to other snow types, and it also masked observed scattering contributions from upper snow layers. The simulated and observed TB diverged above 130 mm SWE, as the model did not capture scattering extinction or snowpack emission. These may impact the initial effective grain size optimization process if applied to the GlobSnow SWE assimilation technique. Grain size is optimized to fit emission simulations to TB from satellite observations; the model's continued sensitivity to SWE above observed signal saturation may be attributed to the existing snowpack. These inaccuracies may then be carried forward in the assimilation process, through variance calculations used to weight the contributions of assimilation modules, leading to less accurate SWE retrievals. [ABSTRACT FROM AUTHOR]

Abstract: Plot-scale brightness temperature (TB) measurements at 6.9, 19, 37, and 89GHz were acquired in forest, open, and lake environments near Churchill, Manitoba, Canada with mobile sled-based microwave radiometers during the 2009–2010 winter season. Detailed physical snow measurements within the radiometer footprints were made to relate the microwave signatures to the seasonal evolution of the snowpack, and provide inputs for model simulations with the Helsinki University of Technology (HUT) snow emission model. Large differences in depth, density, and grain size were observed between the three land cover types. Plot-scale simulations with the HUT model showed a wide range in simulation accuracy between sites and frequencies. In general, model performance degraded when the effective grain size exceeded 2mm and/or there was an ice lens present in the pack. HUT model performance improved when simulations were run regionally at the satellite scale (using three proportional land cover tiles: open, forest, and lake) and compared to Advanced Microwave Scanning Radiometer (AMSR-E) measurements. Root mean square error (RMSE) values ranged from approximately 4 to 16K depending on the frequency, polarization, and land cover composition of the grid cell. Snow water equivalent (SWE) retrievals produced using forward TB simulations with the HUT model in combination with AMSR-E measurements produced RMSE values below 25mm for the intensive study area. Retrieval errors exceeded 50mm when the scheme was applied regionally. [Copyright &y& Elsevier]

Abstract: Airborne and satellite brightness temperature (T B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37GHz–19GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70×120m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37GHz T B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130mm at which point emission from the snowpack creates a positive but noisier relationship between 37GHz T B and SWE. The change from snowpack scattering to emission was also evident in the temporal evolution of 37GHz T B observed from satellite measurements. AMSR-E brightness temperatures (2002/03–2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37V|) in vertically polarized 37GHz T B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37V| was significantly related to greater (lower) ground measured SWE (r 2 =0.77 with monthly averages; r 2 =0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r 2 values calculated for January through April 2003–2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present). [Copyright &y& Elsevier]

A detailed evaluation of snow water equivalent (SWE) and snow cover extent (SCE) derived using the combined Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) brightness temperature records for the 1978–2002 period was carried out for a longitudinal transect in the continental interior of North America. Comparison with in situ SWE observations showed that the SMMR brightness temperature adjustments are required to produce SWE retrievals with similar bias and rmse as observed during the SSM/I period. Underestimation of SCE in the passive microwave dataset (relative to NOAA snow charts) was identified as a systematic problem, most pronounced in early winter and during seasons with above-average snow extent. The passive microwave data were successfully merged with historical data based on strong interdataset agreement for a 1978–92 overlap period. Analysis of SWE and SCE time series for the months of December through March 1915–2002 provided information on SWE and SCE variability over the interior of North America, but yielded no evidence of significant trends in either variable over the 88-yr period. Anomalies observed during the relatively recent period of passive microwave data acquisition did not exceed the range of anomalies observed in the historical data record. [ABSTRACT FROM AUTHOR]

Abstract: Areas of similar ecology are often delineated based on homogenous topography, temperature, and land cover. Once delineated, these zones become the basis for a wide variety of scientific research and management activities. For instance, in Canada, ecozones are commonly utilized ecological management units delineated using geographic, topographic, and climatic information aided by spring and summer vegetation conditions. Snow cover has an influence on local and regional hydrological conditions and climate, as well as on animal habitats. As such, we posit that inclusion of winter conditions, incorporating spatial- and temporal-variation in snow cover is an additional element for consideration when delineating areas with homogenous conditions. In our analysis we use satellite passive microwave brightness temperatures from 19years of Special Sensor Microwave/Imager (SSM/I) measurements to produce a daily time-series on snow cover, and demonstrate how these data can be used to delineate areas of similar winter conditions. We use splines and curve fitting to generalize the dense time-series (of over 6900days) to a set of metrics, and select three for use in cluster-based generalization of snow cover regimes: annual maximum difference between 37 and 19GHz SSM/I measurements (with differences in magnitudes indicative of snow accumulation), variation of 37–19GHz brightness temperatures (indicative of snow cover variability), and variation in the rate of brightness temperature change during the snow melt season (indicative of seasonal change). Our results indicate that these metrics produce spatial units that are unique, and not captured by conventional ecological management units, while also producing spatial units that cohere to those generated from summer conditions. Spatial units that are found to have spatial cohesion between summer and winter data sources are located in regions where the amount of snow tends to be low, and snow cover variability minimal. We propose that snow cover regimes may be used to augment typical vegetation-based ecological zonations or to provide insights on hydrology and animal habitat conditions. Inclusion of winter conditions is especially important when areal delineations are used to monitor impacts of climate change, and as a baseline for monitoring changes in snow cover amount, extent, and/or distribution. [Copyright &y& Elsevier]