Statistical Evaluation of Three Distinct Automated Doppler Dealiasing Algorithms Using a Hand-Dealiased Shipborne Radar Dataset

No abstract availabl

Marshall Space Flight Center and the Open-Source Radar Software Revolution

No abstract availabl

A guide to phylogenetic metrics for conservation, community ecology and macroecology

The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub-disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub-disciplines hampers potential meta-analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo-diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information.S.B.C. was funded by a postdoctoral grant from Fundac¸ao para a ˜ Ciencia e a Tecnologia (FCT) (SFRH/BPD/74423/2010), ˆ and through the project PTDC/BIA-BIC/118624/2010- FCOMP-01-0124-FEDER-019676, supported by Fonds Europeen de D ´ eveloppement ´ Economique et R ´ egional ´ (FEDER) funds through the Operational Programme for Competitiveness Factors (COMPETE) and by National Funds through FCT. M.R.H. is supported by the Netherlands Organisation for Scientific Research (858.14.040). F.M. received funding from the European Research Council under the European Community’s Seventh Framework Programme FP7/2007-2013 Grant Agreement no. 281422 (TEEMBIO). S.A.F. acknowledges funding by the LOEWE Zentrum AdRIA funding program, of Hesse’s Ministry of Higher Education, Research, and the Arts

Soil pH mediates the balance between stochastic and deterministic assembly of bacteria

Little is known about the factors affecting the relative influences of stochastic and deterministic processes that govern the assembly of microbial communities in successional soils. Here, we conducted a meta-analysis of bacterial communities using six different successional soil datasets distributed across different regions. Different relationships between pH and successional age across these datasets allowed us to separate the influences of successional age (i.e., time) from soil pH. We found that extreme acidic or alkaline pH conditions lead to assembly of phylogenetically more clustered bacterial communities through deterministic processes, whereas pH conditions close to neutral lead to phylogenetically less clustered bacterial communities with more stochasticity. We suggest that the influence of pH, rather than successional age, is the main driving force in producing trends in phylogenetic assembly of bacteria, and that pH also influences the relative balance of stochastic and deterministic processes along successional soils. Given that pH had a much stronger association with community assembly than did successional age, we evaluated whether the inferred influence of pH was maintained when studying globally distributed samples collected without regard for successional age. This dataset confirmed the strong influence of pH, suggesting that the influence of soil pH on community assembly processes occurs globally. Extreme pH conditions likely exert more stringent limits on survival and fitness, imposing strong selective pressures through ecological and evolutionary time. Taken together, these findings suggest that the degree to which stochastic vs. deterministic processes shape soil bacterial community assembly is a consequence of soil pH rather than successional age

The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. The source code for the toolkit is available on GitHub and is distributed under a BSD license

pyart-1.6.0.zip

Complete source code for the Python ARM Radar Toolkit (Py-ART) version 1.6.0.<div><br></div><div>The Python ARM Radar Toolkit, Py-ART, is a Python package for reading, visualizing, correcting and analysis of weather radar data.  Building on top of libraries in the scientific Python stack (NumPy, SciPy, matplotlib), Py-ART, provides a powerful environment for working with weather radar data.</div><div><br></div><div>Additional details on Py-ART can be found at: http://arm-doe.github.io/pyart/</div

A general study of packing in oxide glass systems containing alkali

The packing fraction, defined as the ionic volume divided by the molar volume, appears to hold promise as a general measure of the structure of alkali oxide modified glasses. For modifying ions having volumes larger than oxygen (K, Rb, and Cs), the packing is dominated by the modifier as alkali oxide concentration increases and is largely independent of glass former; we define this as ionic packing. For alkali smaller than oxygen (Li and Na) the packing is controlled by the oxygen covalent network and is heavily dependent on glass former; we call this covalent packing. In this paper we compare the packing fractions of alkali borate, silicate, germanate, phosphate, and vanadate glass systems. © 2004 Elsevier B.V. All rights reserved

Data for AMS Short Course on Open Source Radar Software

<p>Data used to support the teaching of the AMS Radar Short course. NOTE: Not for research purposes, please go to the original providers (NOAA and ARM DOE) to download data so the use and metrics can be captured</p