A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States

A frequently encountered difficulty in assessing model-predicted land–atmosphere exchanges of moisture and energy is the absence of comprehensive observations to which model predictions can be compared at the spatial and temporal resolutions at which the models operate. Various methods have been used to evaluate the land surface schemes in coupled models, including comparisons of model-predicted evapotranspiration with values derived from atmospheric water balances, comparison of model-predicted energy and radiative fluxes with tower measurements during periods of intensive observations, comparison of model-predicted runoff with observed streamflow, and comparison of model predictions of soil moisture with spatial averages of point observations. While these approaches have provided useful model diagnostic information, the observation-based products used in the comparisons typically are inconsistent with the model variables with which they are compared—for example, observations are for points or areas much smaller than the model spatial resolution, comparisons are restricted to temporal averages, or the spatial scale is large compared to that resolved by the model. Furthermore, none of the datasets available at present allow an evaluation of the interaction of the water balance components over large regions for long periods. In this study, a model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950–2000, and is at a 3-h time step with a spatial resolution of ⅛ degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins. On this basis, and given the physically based model parameterizations, it is argued that other terms in the surface water balance (e.g., soil moisture and evapotranspiration) are well represented, at least for the purposes of diagnostic studies such as those in which atmospheric model reanalysis products have been widely used. These characteristics make this dataset useful for a variety of studies, especially where ground observations are lacking

Synthetic aperture radar/LANDSAT MSS image registration

Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint

Using Sideband Transitions for Two-Qubit Operations in Superconducting Circuits

We demonstrate time resolved driving of two-photon blue sideband transitions between superconducting qubits and a transmission line resonator. Using the sidebands, we implement a pulse sequence that first entangles one qubit with the resonator, and subsequently distributes the entanglement between two qubits. We show generation of 75% fidelity Bell states by this method. The full density matrix of the two qubit system is extracted using joint measurement and quantum state tomography, and shows close agreement with numerical simulation. The scheme is potentially extendable to a scalable universal gate for quantum computation.Comment: 4 pages, 5 figures, version with high resolution figures available at http://qudev.ethz.ch/content/science/PubsPapers.htm

Vortex spectrum in superfluid turbulence: interpretation of a recent experiment

We discuss a recent experiment in which the spectrum of the vortex line density fluctuations has been measured in superfluid turbulence. The observed frequency dependence of the spectrum, $f^{-5/3}$, disagrees with classical vorticity spectra if, following the literature, the vortex line density is interpreted as a measure of the vorticity or enstrophy. We argue that the disagrement is solved if the vortex line density field is decomposed into a polarised field (which carries most of the energy) and an isotropic field (which is responsible for the spectrum).Comment: Submitted for publication http://crtbt.grenoble.cnrs.fr/helio/GROUP/infa.html http://www.mas.ncl.ac.uk/~ncfb

Close to Uniform Prime Number Generation With Fewer Random Bits

In this paper, we analyze several variants of a simple method for generating prime numbers with fewer random bits. To generate a prime $p$ less than $x$, the basic idea is to fix a constant $q\propto x^{1-\varepsilon}$, pick a uniformly random $a<q$ coprime to $q$, and choose $p$ of the form $a+t\cdot q$, where only $t$ is updated if the primality test fails. We prove that variants of this approach provide prime generation algorithms requiring few random bits and whose output distribution is close to uniform, under less and less expensive assumptions: first a relatively strong conjecture by H.L. Montgomery, made precise by Friedlander and Granville; then the Extended Riemann Hypothesis; and finally fully unconditionally using the Barban-Davenport-Halberstam theorem. We argue that this approach has a number of desirable properties compared to previous algorithms.Comment: Full version of ICALP 2014 paper. Alternate version of IACR ePrint Report 2011/48

Using a Gridded Global Dataset to Characterize Regional Hydroclimate in Central Chile

Central Chile is facing dramatic projections of climate change, with a consensus for declining precipitation, negatively affecting hydropower generation and irrigated agriculture. Rising from sea level to 6000 m within a distance of 200 km, precipitation characterization is difficult because of a lack of long-term observations, especially at higher elevations. For understanding current mean and extreme conditions and recent hydroclimatological change, as well as to provide a baseline for downscaling climate model projections, a temporally and spatially complete dataset of daily meteorology is essential. The authors use a gridded global daily meteorological dataset at 0.25° resolution for the period 1948–2008, adjusted by monthly precipitation observations interpolated to the same grid using a cokriging method with elevation as a covariate. For validation, daily statistics of the adjusted gridded precipitation are compared to station observations. For further validation, a hydrology model is driven with the gridded 0.25° meteorology and streamflow statistics are compared with observed flow. The high elevation precipitation is validated by comparing the simulated snow extent to Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results show that the daily meteorology with the adjusted precipitation can accurately capture the statistical properties of extreme events as well as the sequence of wet and dry events, with hydrological model results displaying reasonable agreement with observed streamflow and snow extent. This demonstrates the successful use of a global gridded data product in a relatively data-sparse region to capture hydroclimatological characteristics and extremes

Differential Dynamic Microscopy to characterize Brownian motion and bacteria motility

We have developed a lab work module where we teach undergraduate students how to quantify the dynamics of a suspension of microscopic particles, measuring and analyzing the motion of those particles at the individual level or as a group. Differential Dynamic Microscopy (DDM) is a relatively recent technique that precisely does that and constitutes an alternative method to more classical techniques such as dynamics light scattering (DLS) or video particle tracking (VPT). DDM consists in imaging a particle dispersion with a standard light microscope and a camera. The image analysis requires the students to code and relies on digital Fourier transform to obtain the intermediate scattering function, an autocorrelation function that characterizes the dynamics of the dispersion. We first illustrate DDM on the textbook case of colloids where we measure the diffusion coefficient. Then we show that DDM is a pertinent tool to characterize biologic systems such as motile bacteria i.e.bacteria that can self propel, where we not only determine the diffusion coefficient but also the velocity and the fraction of motile bacteria. Finally, so that our paper can be used as a tutorial to the DDM technique, we have joined to this article movies of the colloidal and bacterial suspensions and the DDM algorithm in both Matlab and Python to analyze the movies

Projected climate-induced faunal change in the western hemisphere

Climate change is predicted to be one of the greatest drivers of ecological change in the coming century. Increases in temperature over the last century have clearly been linked to shifts in species distributions. Given the magnitude of projected future climatic changes, we can expect even larger range shifts in the coming century. These changes will, in turn, alter ecological communities and the functioning of ecosystems. Despite the seriousness of predicted climate change, the uncertainty in climate-change projections makes it difficult for conservation managers and planners to proactively respond to climate stresses. To address one aspect of this uncertainty, we identified predictions of faunal change for which a high level of consensus was exhibited by different climate models. Specifically, we assessed the potential effects of 30 coupled atmosphere–ocean general circulation model (AOGCM) future-climate simulations on the geographic ranges of 2954 species of birds, mammals, and amphibians in the Western Hemisphere. Eighty percent of the climate projections based on a relatively low greenhouse-gas emissions scenario result in the local loss of at least 10% of the vertebrate fauna over much of North and South America. The largest changes in fauna are predicted for the tundra, Central America, and the Andes Mountains where, assuming no dispersal constraints, specific areas are likely to experience over 90% turnover, so that faunal distributions in the future will bear little resemblance to those of today

Variability and potential sources of predictability of North American runoff

Understanding the space-time variability of runoff has important implications for climate because of the linkage of runoff and evapotranspiration and is a practical concern as well for the prediction of drought and floods. In contrast to many studies investigating the space-time variability of precipitation and temperature, there has been relatively little work evaluating climate teleconnections of runoff, in part because of the absence of data sets that lend themselves to commonly used techniques in climate analysis like principal components analysis. We examine the space-time variability of runoff over North America using a 50-year retrospective spatially distributed data set of runoff and other land surface water cycle variables predicted using a calibrated macroscale hydrology model, thus avoiding some shortcomings of past studies based more directly on streamflow observations. We determine contributions to runoff variability of climatic teleconnections, soil moisture, and snow for lead times up to a year. High and low values of these sources of predictability are evaluated separately. We identify patterns of runoff variability that are not revealed by direct analysis of observations, especially in areas of sparse stream gauge coverage. The presence of nonlinear relationships between large-scale climate changes and runoff pattern variability, as positive and negative values of the large-scale climate indices rarely show opposite teleconnections with a runoff pattern. Dry soil moisture anomalies have a stronger influence on runoff variability than wet soil. Snow, and more so soil moisture, in many locations enhance the predictability due to climatic teleconnections