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The Australian Integrated Marine Observing System (IMOS) is funded by the Australian Government, and designed to be a fully-integrated national array of observing equipment to monitor the open oceans and coastal marine environment around Australia. IMOS delivers physical, chemical and biological data comprising of observations from a wide spectrum of platforms including weather stations, oceanographic moorings, underway ship observations, seagliders, ocean surface radar, satellite image reception and reef based sensor networks. When data from ocean observing systems can be provided in near real-time, the operational aspects are further enhanced and provide potential for a range of value added products to be developed. Here we provide three examples of co-invested partnerships that have facilitated the development of real-time moored ocean observing systems in the coastal zone, operated by the Queensland IMOS node. For each of these examples, the project is introduced, a detailed technical description of the system is provided, operational aspects are summarised, and the uptake of data from stakeholders is discussed. These examples demonstrate the benefits of having a national collaborative approach to marine observing with a clear focus on open access to data. It is also demonstrated that the benefits and opportunities offered by real-time ocean observing can outweigh the technical challenges of developing and maintaining these complex systems. [ABSTRACT FROM AUTHOR]

A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data. [ABSTRACT FROM AUTHOR]

A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A balanced bootstrap is used to create replicate datasets, with the same model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability yet also be very biased. These results are likely dependent on characteristics of the data. [ABSTRACT FROM AUTHOR]

We report on quantitative, spatially resolved density, temperature, and velocity measurements on ground-state atomic hydrogen in an expanding thermal Ar–H plasma using two-photon excitation laser-induced fluorescence (LIF). The method’s diagnostic value for application in this plasma is assessed by identifying and evaluating the possibly disturbing factors on the interpretation of the LIF signal in terms of density, temperature, and velocity. In order to obtain quantitative density numbers, the LIF setup is calibrated for H measurements using two different methods. A commonly applied calibration method, in which the LIF signal from a, by titration, known amount of H generated by a flow-tube reactor is used as a reference, is compared to a rather new calibration method, in which the H density in the plasma jet is derived from a measurement of the two-photon LIF signal generated from krypton at a well-known pressure, using a known Kr to H detection sensitivity ratio. The two methods yield nearly the same result, which validates the new H density calibration. Gauging the new “rare gas method” by the “flow-tube reactor method,” we find a krypton to hydrogen two-photon excitation cross section ratio σ[sub Kr][sup (2)]/σ[sub H][sup (2)] of 0.56, close to the reported value of 0.62. Since the H density calibration via two-photon LIF of krypton is experimentally far more easy than the one using a flow-tube reactor, it is foreseen that the “rare gas method” will become the method of choice in two-photon LIF experiments. The current two-photon LIF detection limit for H in the Ar–H plasma jet is 10[sup 15] m[sup -3]. The accuracy of the density measurements depends on the accuracy of the calibration, which is currently limited to 33%. The reproducibility depends on the signal-to-noise (S/N) ratio in the LIF measurements and is orders of magnitude better. The accuracy in the temperature determina... [ABSTRACT FROM AUTHOR]

Consider a Toda chain with uniform friction. Starting with an initial condition that represents a soliton, we investigate its decay. The main result is that the solitary character is almost completely preserved. During the decay the wave activates other nonlinear modes. The corresponding actions appear to be bounded uniformly in time, proportional to the square of the friction coefficient. We focus upon the interaction with the same soliton but opposite direction of propagation. Comparing the numerical observations with an analytical model we conclude that the activated wave is well described by a linear equation, inhomogeneously driven by the main wave. The main wave itself decays as a nonlinear damped oscillator with one degree of freedom. [ABSTRACT FROM AUTHOR]

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