Reef Rescue Marine Monitoring Program: Assessment of terrestrial run-off entering the Reef and inshore marine water quality monitoring using earth observation data. Final report for 2010/11 activities

This report delivers management relevant information of flood events and inshore water quality compliance based on tailored temporal and spatial analysis of remote sensing data, carried out by CSIRO as part of the Reef Rescue Marine Monitoring Program (MMP) from 2005 to 2010

Reef Rescue Marine Monitoring Program: Using remote sensing for GBR-wide water quality. Final report for 2011/12 activities

This report delivers management relevant information of flood events and inshore water quality compliance based on tailored temporal and spatial analysis of remote sensing data, carried out by CSIRO as part of the Reef Rescue Marine Monitoring Program (MMP) from 2005 to 2013

LABORATORY MEASUREMENTS OF THE K-SHELL TRANSITION ENERGIES IN L-SHELL IONS OF SI AND S

We have measured the energies of the strongest 1s–2ℓ (ℓ = s, p) transitions in He- through Ne-like silicon and sulfur ions to an accuracy of <1 eV using the Lawrence Livermore National Laboratory's electron beam ion traps, EBIT-I and SuperEBIT, and the NASA/GSFC EBIT Calorimeter Spectrometer (ECS). We identify and measure the energies of 18 and 21 X-ray features from silicon and sulfur, respectively. The results are compared to new Flexible Atomic Code calculations and to semi-relativistic Hartree–Fock calculations by Palmeri et al. (2008). These results will be especially useful for wind diagnostics in high-mass X-ray binaries, such as Vela X-1 and Cygnus X-1, where high-resolution spectral measurements using Chandra's high-energy transmission grating has made it possible to measure Doppler shifts of 100 km s[superscript -1]. The accuracy of our measurements is consistent with that needed to analyze Chandra observations, exceeding Chandra's 100 km s[superscript -1] limit. Hence, the results presented here not only provide benchmarks for theory, but also accurate rest energies that can be used to determine the bulk motion of material in astrophysical sources. We show the usefulness of our results by applying them to redetermine Doppler shifts from Chandra observations of Vela X-1.United States. National Aeronautics and Space Administration (work orders NNX/2AH84G)United States. Department of Energy (Contract DE-AC52-07NA27344)Smithsonian Astrophysical Observatory (contract SV3-73016

The Fourth SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-4)

Ten international laboratories specializing in the determination of marine pigment concentrations using high performance liquid chromatography (HPLC) were intercompared using in situ samples and a mixed pigment sample. Although prior Sea-viewing Wide Field-of-view Sensor (SeaWiFS) High Performance Liquid Chromatography (HPLC) Round-Robin Experiment (SeaHARRE) activities conducted in open-ocean waters covered a wide dynamic range in productivity, and some of the samples were collected in the coastal zone, none of the activities involved exclusively coastal samples. Consequently, SeaHARRE-4 was organized and executed as a strictly coastal activity and the field samples were collected from primarily eutrophic waters within the coastal zone of Denmark. The more restrictive perspective limited the dynamic range in chlorophyll concentration to approximately one and a half orders of magnitude (previous activities covered more than two orders of magnitude). The method intercomparisons were used for the following objectives: a) estimate the uncertainties in quantitating individual pigments and higher-order variables formed from sums and ratios; b) confirm if the chlorophyll a accuracy requirements for ocean color validation activities (approximately 25%, although 15% would allow for algorithm refinement) can be met in coastal waters; c) establish the reduction in uncertainties as a result of applying QA procedures; d) show the importance of establishing a properly defined referencing system in the computation of uncertainties; e) quantify the analytical benefits of performance metrics, and f) demonstrate the utility of a laboratory mix in understanding method performance. In addition, the remote sensing requirements for the in situ determination of total chlorophyll a were investigated to determine whether or not the average uncertainty for this measurement is being satisfied

Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development

This is the final version. Available from Frontiers Media via the DOI in this record.To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.ESA SEOM SY-4Sci Synergy projectNAS

A blind detection of a large, complex, Sunyaev--Zel'dovich structure

We present an interesting Sunyaev-Zel'dovich (SZ) detection in the first of the Arcminute Microkelvin Imager (AMI) 'blind', degree-square fields to have been observed down to our target sensitivity of 100{\mu}Jy/beam. In follow-up deep pointed observations the SZ effect is detected with a maximum peak decrement greater than 8 \times the thermal noise. No corresponding emission is visible in the ROSAT all-sky X-ray survey and no cluster is evident in the Palomar all-sky optical survey. Compared with existing SZ images of distant clusters, the extent is large (\approx 10') and complex; our analysis favours a model containing two clusters rather than a single cluster. Our Bayesian analysis is currently limited to modelling each cluster with an ellipsoidal or spherical beta-model, which do not do justice to this decrement. Fitting an ellipsoid to the deeper candidate we find the following. (a) Assuming that the Evrard et al. (2002) approximation to Press & Schechter (1974) correctly gives the number density of clusters as a function of mass and redshift, then, in the search area, the formal Bayesian probability ratio of the AMI detection of this cluster is 7.9 \times 10^4:1; alternatively assuming Jenkins et al. (2001) as the true prior, the formal Bayesian probability ratio of detection is 2.1 \times 10^5:1. (b) The cluster mass is MT,200 = 5.5+1.2\times 10^14h-1M\odot. (c) Abandoning a physical model with num- -1.3 70 ber density prior and instead simply modelling the SZ decrement using a phenomenological {\beta}-model of temperature decrement as a function of angular distance, we find a central SZ temperature decrement of -295+36 {\mu}K - this allows for CMB primary anisotropies, receiver -15 noise and radio sources. We are unsure if the cluster system we observe is a merging system or two separate clusters.Comment: accepted MNRAS. 12 pages, 9 figure

Data sheets aiding identification of phytoplankton carotenoids and chlorophylls

9 pagesSince the publication of 47 key phytoplankton pigment data sheets in the volume by Jeffrey et al. (1997b), several new algal groups and pigments have been reported. To reflect this and the increased use of mass spectrometry for phytoplankton pigment characterisation we have compiled revised and expanded data sheets documenting 47 carotenoids and 21 chlorophylls. These new data sheets complement the ones produced for the 1997 volume. They are also available online, at www.cambridge.org/phytoplankton, for ease of consultation. We do not include data sheets for the many chlorophyll transformation products found particularly in sediments; for information on these pigments readers should refer to the comprehensive review by Keely (2006). Similarly, our coverage of pigments contained in phototrophic bacteria is limited mostly to cyanobacteria found in the water column of freshwater and marine environments (see Chapter 1, this volume), hence we exclude the newly discovered chlorophyll f in stromatolites (Chen et al., 2010). Readers interested in anoxygenic phototrophic bacteria should consult the reviews by Takaichi (1999) and Scheer (2006)N

Erratum: Author Correction: Identification of genes required for eye development by high-throughput screening of mouse knockouts.

[This corrects the article DOI: 10.1038/s42003-018-0226-0.]