Some Loessoid Deposits of Central Iowa

During the course of an investigation of the bedrock geology of western Story County, Iowa, our attention was directed to the occurrence of some unusual Pleistocene deposits. These deposits consist of calcareous silts, which from the viewpoint of mineralogy, textural analysis, physiographic expression, and paleontology embrace most of the criteria by which loess is generally described or recognized. These deposits in Story County have been described as loess by Beyer (1898, and 1899). The most apparent but unusual feature of these deposits is their vertical and lateral gradation into till or alluvial material. Similar deposits are now known to occur also in Boone County. Only one of these deposits has been extensively studied, and the purpose of this report is to outline the progress of our investigation and to discuss some of the problems presented by these deposits. Location of area. The silts, described in this report, are known to extend along the course of Onion Creek from the NW 1/4 Sec. 32, upstream through section 29, and into the SE 1/4 section 30, T84N., R24W., Story County, (see figures 1 and 2)

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008

Spatial Patterns of Snow Cover in North Carolina: Surface and Satellite Perspectives

Snow mapping is a common practice in regions that receive large amounts of snowfall annually, have seasonally-continuous snow cover, and where snowmelt contributes significantly to the hydrologic cycle. Although higher elevations in the southern Appalachian Mountains average upwards of 100 inches of snow annually, much of the remainder of the Southeast U.S. receives comparatively little snowfall (< 10 inches). Recent snowy winters in the region have provided an opportunity to assess the fine-grained spatial distribution of snow cover and the physical processes that act to limit or improve its detection across the Southeast. In the present work, both in situ and remote sensing data are utilized to assess the spatial distribution of snow cover for a sample of recent snowfall events in North Carolina. Specifically, this work seeks to determine how well ground measurements characterize the fine-grained patterns of snow cover in relation to Moderate- Resolution Imaging Spectroradiometer (MODIS) snow cover products (in this case, the MODIS Fractional Snow Cover product)

On the origin of non-monotonic doping dependence of the in-plane resistivity anisotropy in Ba(Fe1−xTx_{1-x}T_x)2_2As2_2, TT = Co, Ni and Cu

The in-plane resistivity anisotropy has been measured for detwinned single crystals of Ba(Fe$_{1-x}$Ni$_x$)$_2$As$_2$ and Ba(Fe$_{1-x}$Cu$_x$)$_2$As$_2$. The data reveal a non-monotonic doping dependence, similar to previous observations for Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$. Magnetotransport measurements of the parent compound reveal a non-linear Hall coefficient and a strong linear term in the transverse magnetoresistance. Both effects are rapidly suppressed with chemical substitution over a similar compositional range as the onset of the large in-plane resistivity anisotropy. It is suggested that the relatively small in-plane anisotropy of the parent compound in the spin density wave state is due to the presence of an isotropic, high mobility pocket of reconstructed Fermi surface. Progressive suppression of the contribution to the conductivity arising from this isotropic pocket with chemical substitution eventually reveals the underlying in-plane anisotropy associated with the remaining FS pockets.Comment: 12 pages, 9 figure

Relationship Between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing in the Wind River Range, Wyoming

MODIS-derived snow cover measured on 30 April in any given year explains approximately 89 % of the variance in stream discharge for maximum monthly streamflow in that year. Observed changes in streamflow appear to be related to increasing maximum air temperatures over the last four decades causing lower spring snow-cover extent. The majority (>70%) of the water supply in the western United States comes from snowmelt, thus analysis of the declining spring snowpack (and resulting declining stream discharge) has important implications for streamflow management in the drought-prone western U.S

The Overlooked Potential of Generalized Linear Models in Astronomy-III: Bayesian Negative Binomial Regression and Globular Cluster Populations

In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for expected $N_{\rm GC}$comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35% smaller than other types with similar brightness.Comment: 14 pages, 12 figures. Accepted for publication in MNRA

Short small-polaron lifetime in the mixed-valence perovskite Cs2_2Au2_2I6_6 from high-pressure pump-probe experiments

We study the ultrafast phonon response of mixed-valence perovskite Cs$_2$Au$_2$I$_6$ using pump-probe spectroscopy under high-pressure in a diamond anvil cell. We observed a remarkable softening and broadening of the Au - I stretching phonon mode with both applied pressure and photoexcitation. Using a double-pump scheme we measured a lifetime of the charge transfer excitation into single valence Au$^{2+}$ of less than 4 ps, which is an indication of the local character of the Au$^{2+}$ excitation. Furthermore, the strong similarity between the pressure and fluence dependence of the phonon softening shows that the inter-valence charge transfer plays an important role in the structural transition.Comment: 4 pages, 4 figure

A Comparison of Satellite-Derived Snow Maps with a Focus on Ephemeral Snow in North Carolina

In this paper, we focus on the attributes and limitations of four commonly-used daily snowcover products with respect to their ability to map ephemeral snow in central and eastern North Carolina. We show that the Moderate-Resolution Imaging Spectroradiometer (MODIS) fractional snow-cover maps can delineate the snow-covered area very well through the use of a fully-automated algorithm, but suffer from the limitation that cloud cover precludes mapping some ephemeral snow. The semi-automated Interactive Multi-sensor Snow and ice mapping system (IMS) and Rutgers Global Snow Lab (GSL) snow maps are often able to capture ephemeral snow cover because ground-station data are employed to develop the snow maps, The Rutgers GSL maps are based on the IMS maps. Finally, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provides some good detail of snow-water equivalent especially in deeper snow, but may miss ephemeral snow cover because it is often very thin or wet; the AMSR-E maps also suffer from coarse spatial resolution. We conclude that the southeastern United States represents a good test region for validating the ability of satellite snow-cover maps to capture ephemeral snow cover

Use of MODIS Snow-Cover Maps for Detecting Snowmelt Trends in North America

Research has shown that the snow season in the Northern Hemisphere has been getting shorter in recent decades, consistent with documented global temperature increases. Specifically, the snow is melting earlier in the spring allowing for a longer growing season and associated land-cover changes. Here we focus on North America. Using the Moderate-Resolution Imaging Radiometer (MODIS) cloud-gap-filled standard snow-cover data product we can detect a trend toward earlier spring snowmelt in the approx 12 years since the MODIS launch. However, not all areas in North America show earlier spring snowmelt over the study period. We show examples of springtime snowmelt over North America, beginning in March 2000 and extending through the winter of 2012 for all of North America, and for various specific areas such as the Wind River Range in Wyoming and in the Catskill Mountains in New York. We also compare our approx 12-year trends with trends derived from the Rutgers Global Snow Lab snow cover climate-data record