Understanding Snow Microstructure for Microwave Remote Sensing

Scientists from the snow and soil remote sensing communities met to build on recent developments in objective snow microstructure measurement techniques by improving the understanding of their application in remote sensing at microwave frequencies

Implementation and analysis of the generalised new Mersenne number transforms for encryption

PhD ThesisEncryption is very much a vast subject covering myriad techniques to conceal and safeguard data and communications. Of the techniques that are available, methodologies that incorporate the number theoretic transforms (NTTs) have gained recognition, specifically the new Mersenne number transform (NMNT). Recently, two new transforms have been introduced that extend the NMNT to a new generalised suite of transforms referred to as the generalised NMNT (GNMNT). These two new transforms are termed the odd NMNT (ONMNT) and the odd-squared NMNT (O2NMNT). Being based on the Mersenne numbers, the GNMNTs are extremely versatile with respect to vector lengths. The GNMNTs are also capable of being implemented using fast algorithms, employing multiple and combinational radices over one or more dimensions. Algorithms for both the decimation-in-time (DIT) and -frequency (DIF) methodologies using radix-2, radix-4 and split-radix are presented, including their respective complexity and performance analyses. Whilst the original NMNT has seen a significant amount of research applied to it with respect to encryption, the ONMNT and O2NMNT can utilise similar techniques that are proven to show stronger characteristics when measured using established methodologies defining diffusion. Analyses in diffusion using a small but reasonably sized vector-space with the GNMNTs will be exhaustively assessed and a comparison with the Rijndael cipher, the current advanced encryption standard (AES) algorithm, will be presented that will confirm strong diffusion characteristics. Implementation techniques using general-purpose computing on graphics processing units (GPGPU) have been applied, which are further assessed and discussed. Focus is drawn upon the future of cryptography and in particular cryptology, as a consequence of the emergence and rapid progress of GPGPU and consumer based parallel processing

Economic impact of reduced mortality due to increased cycling.

Increasing regular physical activity is a key public health goal. One strategy is to change the physical environment to encourage walking and cycling, requiring partnerships with the transport and urban planning sectors. Economic evaluation is an important factor in the decision to fund any new transport scheme, but techniques for assessing the economic value of the health benefits of cycling and walking have tended to be less sophisticated than the approaches used for assessing other benefits. This study aimed to produce a practical tool for estimating the economic impact of reduced mortality due to increased cycling. The tool was intended to be transparent, easy to use, reliable, and based on conservative assumptions and default values, which can be used in the absence of local data. It addressed the question: For a given volume of cycling within a defined population, what is the economic value of the health benefits? The authors used published estimates of relative risk of all-cause mortality among regular cyclists and applied these to levels of cycling defined by the user to produce an estimate of the number of deaths potentially averted because of regular cycling. The tool then calculates the economic value of the deaths averted using the "value of a statistical life." The outputs of the tool support decision making on cycle infrastructure or policies, or can be used as part of an integrated economic appraisal. The tool's unique contribution is that it takes a public health approach to a transport problem, addresses it in epidemiologic terms, and places the results back into the transport context. Examples of its use include its adoption by the English and Swedish departments of transport as the recommended methodologic approach for estimating the health impact of walking and cycling

NASA Cold Land Processes Experiment (CLPX 2002/03): ground-based and near-surface meteorological observations

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 CLPXas 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 long-wave 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

Multi-physics ensemble snow modelling in the western Himalaya

Combining multiple data sources with multi-physics simulation frameworks offers new potential to extend snow model inter-comparison efforts to the Himalaya. As such, this study evaluates the sensitivity of simulated regional snow cover and runoff dynamics to different snowpack process representations. The evaluation is based on a spatially distributed version of the Factorial Snowpack Model (FSM) set up for the Astore catchment in the upper Indus basin. The FSM multi-physics model was driven by climate fields from the High Asia Refined Analysis (HAR) dynamical downscaling product. Ensemble performance was evaluated primarily using MODIS remote sensing of snow-covered area, albedo and land surface temperature. In line with previous snow model inter-comparisons, no single FSM configuration performs best in all of the years simulated. However, the results demonstrate that performance variation in this case is at least partly related to inaccuracies in the sequencing of inter-annual variation in HAR climate inputs, not just FSM model limitations. Ensemble spread is dominated by interactions between parameterisations of albedo, snowpack hydrology and atmospheric stability effects on turbulent heat fluxes. The resulting ensemble structure is similar in different years, which leads to systematic divergence in ablation and mass balance at high elevations. While ensemble spread and errors are notably lower when viewed as anomalies, FSM configurations show important differences in their absolute sensitivity to climate variation. Comparison with observations suggests that a subset of the ensemble should be retained for climate change projections, namely those members including prognostic albedo and liquid water retention, refreezing and drainage processes

Simulated single-layer forest canopies delay Northern Hemisphere snowmelt

Single-layer vegetation schemes in modern land surface models have been found to overestimate diurnal cycles in longwave radiation beneath forest canopies. This study introduces an empirical correction, based on forest stand-scale simulations, which reduces diurnal cycles of sub-canopy longwave radiation. The correction is subsequently implemented in land-only simulations of the Community Land Model version 4.5 (CLM4.5) in order to assess the impact on snow cover. Nighttime underestimations of sub-canopy longwave radiation outweigh daytime overestimations, which leads to underestimated averages over the snow cover season. As a result, snow temperatures are underestimated and snowmelt is delayed in CLM4.5 across evergreen boreal forests. Comparison with global observations confirms this delay and its reduction by correction of sub-canopy longwave radiation. Increasing insolation and day length change the impact of overestimated diurnal cycles on daily average subcanopy longwave radiation throughout the snowmelt season. Consequently, delay of snowmelt in land-only simulations is more substantial where snowmelt occurs early

Spatio-temporal influence of tundra snow properties on Ku-band (17.2 GHz) backscatter

During the 2010/11 boreal winter, a distributed set of backscatter measurements was collected using a ground-based Ku-band (17.2 GHz) scatterometer system at 26 open tundra sites. A standard snow-sampling procedure was completed after each scan to evaluate local variability in snow layering, depth, density and water equivalent (SWE) within the scatterometer field of view. The shallow depths and large basal depth hoar encountered presented an opportunity to evaluate backscatter under a set of previously untested conditions. Strong Ku-band response was found with increasing snow depth and snow water equivalent (SWE). In particular, co-polarized vertical backscatter increased by 0.82 dB for every 1 cm increase in SWE (R2 = 0.62). While the result indicated strong potential for Ku-band retrieval of shallow snow properties, it did not characterize the influence of sub-scan variability. An enhanced snow-sampling procedure was introduced to generate detailed characterizations of stratigraphy within the scatterometer field of view using near-infrared photography along the length of a 5m trench. Changes in snow properties along the trench were used to discuss variations in the collocated backscatter response. A pair of contrasting observation sites was used to highlight uncertainties in backscatter response related to short length scale spatial variability in the observed tundra environment

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size and temperature) were used as inputs to the multi-layer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical SSA to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%

Evaluation of Operation IceBridge quick-look snow depth estimates on sea ice

We evaluate Operation IceBridge (OIB) ‘quick-look’ (QL) snow depth on sea ice retrievals using in situ measurements taken over immobile first-year ice (FYI) and multi-year ice (MYI) during March of 2014. Good agreement was found over undeformed FYI (-4.5 cm mean bias) with reduced agreement over deformed FYI (-6.6 cm mean bias). Over MYI, the mean bias was -5.7 cm but 54% of retrievals were discarded by the OIB retrieval process as compared to only 10% over FYI. Footprint scale analysis revealed a root mean square error (RMSE) of 6.2 cm over undeformed FYI with RMSE of 10.5 cm and 17.5 cm in the more complex deformed FYI and MYI environments. Correlation analysis was used to demonstrate contrasting retrieval uncertainty associated with spatial aggregation and ice surface roughness