Role of Li_2B_(12)H_(12) for the Formation and Decomposition of LiBH_4

By in situ X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) spectroscopy, the role of Li_2B_(12)H_(12) for the sorption of LiBH_4 is analyzed. We demonstrate that Li_2B_(12)H_(12) and an amorphous Li_2B_(10)H_(10) phase are formed by the reaction of LiBH_4 with diborane (B_2H_6) at 200 °C. Based on our present results, we propose that the Li -2B - (12)H_(12) formation in the desorption of LiBH_4 can be explained as a result of reaction of diborane and LiBH_4. This reaction of the borohydride with diborane may also be observed for other borohydrides, where B_(12)H_(12) phases are found during decomposition

Hypoxia forecasts as a tool for Chesapeake Bay fisheries

The Estuarine Hypoxia component of the U.S. Integrated Ocean Observing System Coastal and Ocean Modeling Testbed (COMT) is evaluating existing hydrodynamic and water quality models used, or likely to be used, for operations in the Chesapeake Bay. As a proof-of-concept, an implementation of the Regional Ocean Modeling System in the Chesapeake Bay (ChesROMS) is linked to a simple respiration model for hypoxia (Hypoxia_SRM). The modeling system is presently being used to produce real-time nowcasts and short-term (3-day) hypoxia forecasts for the Chesapeake Bay, which are currently available on the Virginia Institute of Marine Science (VIMS) website. Workshops with citizen stakeholders have explored potential applications of the estuarine hypoxia nowcast/forecast products in support of recreational and commercial fishing. Interest in this product is high, particularly by recreational fishermen and charter boat captains, since reduced catch per unit effort in the Bay is clearly associated with regions where dissolved oxygen is low. This presentation reviews the insights gained at these stakeholder workshops, including how these stakeholders might apply these products to improve the efficiency and success of their fishing activities and what forecast presentation formats are most useful. Future work involves transporting the hypoxia forecast tool to the Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS), and eventually linking the product with NOAA’s Chesapeake Bay Operational Forecasting System (CBOFS)

Combining observations and numerical model results to improve estimates of hypoxic volume within the Chesapeake Bay, USA

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 118 (2013): 4924–4944, doi:10.1002/jgrc.20331.The overall size of the “dead zone” within the main stem of the Chesapeake Bay and its tidal tributaries is quantified by the hypoxic volume (HV), the volume of water with dissolved oxygen (DO) less than 2 mg/L. To improve estimates of HV, DO was subsampled from the output of 3-D model hindcasts at times/locations matching the set of 2004–2005 stations monitored by the Chesapeake Bay Program. The resulting station profiles were interpolated to produce bay-wide estimates of HV in a manner consistent with nonsynoptic, cruise-based estimates. Interpolations of the same stations sampled synoptically, as well as multiple other combinations of station profiles, were examined in order to quantify uncertainties associated with interpolating HV from observed profiles. The potential uncertainty in summer HV estimates resulting from profiles being collected over 2 weeks rather than synoptically averaged ∼5 km3. This is larger than that due to sampling at discrete stations and interpolating/extrapolating to the entire Chesapeake Bay (2.4 km3). As a result, sampling fewer, selected stations over a shorter time period is likely to reduce uncertainties associated with interpolating HV from observed profiles. A function was derived that when applied to a subset of 13 stations, significantly improved estimates of HV. Finally, multiple metrics for quantifying bay-wide hypoxia were examined, and cumulative hypoxic volume was determined to be particularly useful, as a result of its insensitivity to temporal errors and climate change. A final product of this analysis is a nearly three-decade time series of improved estimates of HV for Chesapeake Bay.Funding for this study was provided by the IOOS COMT Program through NOAA grants NA10NOS0120063 and NA11NOS0120141. Additional funding was provided by NSF grant OCE-1061564

Binary black hole spacetimes with a helical Killing vector

Binary black hole spacetimes with a helical Killing vector, which are discussed as an approximation for the early stage of a binary system, are studied in a projection formalism. In this setting the four dimensional Einstein equations are equivalent to a three dimensional gravitational theory with a $SL(2,\mathbb{C})/SO(1,1)$ sigma model as the material source. The sigma model is determined by a complex Ernst equation. 2+1 decompositions of the 3-metric are used to establish the field equations on the orbit space of the Killing vector. The two Killing horizons of spherical topology which characterize the black holes, the cylinder of light where the Killing vector changes from timelike to spacelike, and infinity are singular points of the equations. The horizon and the light cylinder are shown to be regular singularities, i.e. the metric functions can be expanded in a formal power series in the vicinity. The behavior of the metric at spatial infinity is studied in terms of formal series solutions to the linearized Einstein equations. It is shown that the spacetime is not asymptotically flat in the strong sense to have a smooth null infinity under the assumption that the metric tends asymptotically to the Minkowski metric. In this case the metric functions have an oscillatory behavior in the radial coordinate in a non-axisymmetric setting, the asymptotic multipoles are not defined. The asymptotic behavior of the Weyl tensor near infinity shows that there is no smooth null infinity.Comment: to be published in Phys. Rev. D, minor correction

The Continuous 1.5{D} Terrain Guarding Problem: {D}iscretization, Optimal Solutions, and {PTAS}

In the NP-hard continuous 1.5D Terrain Guarding Problem (TGP) we are given an x-monotone chain of line segments in the plain (the terrain $T$), and ask for the minimum number of guards (located anywhere on $T$) required to guard all of $T$. We construct guard candidate and witness sets $G, W \subset T$ of polynomial size, such that any feasible (optimal) guard cover $G' \subseteq G$ for $W$ is also feasible (optimal) for the continuous TGP. This discretization allows us to: (1) settle NP-completeness for the continuous TGP; (2) provide a Polynomial Time Approximation Scheme (PTAS) for the continuous TGP using the existing PTAS for the discrete TGP by Gibson et al.; (3) formulate the continuous TGP as an Integer Linear Program (IP). Furthermore, we propose several filtering techniques reducing the size of our discretization, allowing us to devise an efficient IP-based algorithm that reliably provides optimal guard placements for terrains with up to 1000000 vertices within minutes on a standard desktop computer

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters

Ultracoherence and Canonical Transformations

The (in)finite dimensional symplectic group of homogeneous canonical transformations is represented on the bosonic Fock space by the action of the group on the ultracoherent vectors, which are generalizations of the coherent states.Comment: 24 page