Theoretical error formation and evaluation of acoustic source localization for cluster-based techniques

In this paper, the formation of theoretical error is presented to investigate the acoustic source localization (ASL) error that can be expected from traditional L-shaped, cross-shaped, square-shaped, and modified square-shaped sensor cluster arrangements. The response surface model based on the optimal Latin hypercube design is developed to theoretically study the effects of sensor placement parameters on the error evaluation index of root mean squared relative error (RMSRE) for the four techniques. The ASL results from the four techniques with the optimal placement parameters are analyzed theoretically. The relevant experiments are conducted for verifying the above theoretical research. The results show that the theoretical error, formed by the difference between the true and the predicted wave propagation directions is related to arrangement of sensors. The results also show that the sensor spacing and the cluster spacing are the two parameters that affect the ASL error most. Between these two parameters the sensor spacing has the stronger influence. The RMSRE increases with an increasing sensor spacing and a decreasing cluster spacing. Meanwhile, the interaction effect of placement parameters should be also emphasized, especially that between the sensor spacing and the cluster spacing for the L-shaped sensor cluster-based technique. Among the four cluster-based techniques, the newly modified square-shaped sensor cluster-based technique shows the smallest RMSRE and not the largest number of sensors. This research on error generation and analysis will provide guidance for the optimal sensor arrangements in cluster-based techniques.

Measurement error due to self-absorption in calibration-free laser-induced breakdown spectroscopy

Self-absorption of spectral lines is known to lower the performance of analytical measurements via calibration-free laser-induced breakdown spectroscopy. However, the error growth due to this effect is not clearly assessed. Here we propose a method to quantify the measurement error due to self-absorption based on the calculation of the spectral radiance of a plasma in local thermodynamic equilibrium. Validated through spectroscopic measurements for a binary alloy thin film of compositional gradient, the method evidences that measurement performance lowering due to self-absorption depends on the spectral shape of the analytical transition and on the intensity measurement method. Thus, line-integrated intensity measurements of Stark broadened lines enable accurate analysis, even at large optical thickness, if line width and plasma size are precisely known. The error growth due to self-absorption is significantly larger for line shapes dominated by Doppler broadening and for line-center intensity measurements. The findings present a significant advance in compositional measurements via calibration-free laser-induced breakdown spectroscopy, as they enable straightforward selection of most appropriate analytical lines.

Evaluation and Minimization of Uncertainty in ITC Binding Measurements: Heat Error, Concentration Error, Saturation, and Stoichiometry

BACKGROUND

Isothermal titration calorimetry (ITC) is uniquely useful for characterizing binding thermodynamics, because it straightforwardly provides both the binding enthalpy and free energy. However, the precision of the results depends on the experimental setup and how thermodynamic results are obtained from the raw data.

METHODS

Experiments and Monte Carlo analysis are used to study how uncertainties in injection heat and concentration propagate to binding enthalpies in various scenarios. We identify regimes in which it is preferable to fix the stoichiometry parameter, N, and evaluate the reliability of uncertainties provided by the least squares method.

RESULTS

The noise in the injection heat is mainly proportional in character, with ~1% and ~3% uncertainty at 27C and 65C, respectively; concentration errors are ~1%. Simulations of experiments based on these uncertainties delineate how experimental design and curve fitting methods influence the uncertainty in the final results.

CONCLUSIONS

In most cases, experimental uncertainty is minimized by using more injections and by fixing N at its known value. With appropriate technique, the uncertainty in measured binding enthalpies can be kept below ~2% under many conditions, including low C values.

GENERAL SIGNIFICANCE

We quantify uncertainties in ITC data due to heat and concentration error, and identify practices to minimize these uncertainties. The resulting guidelines are important when ITC data are used quantitatively, such as to test computer simulations of binding. Reproducibility and further study are supported by free distribution of the new software developed here.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling.