An unexpected outburst from A0535+262

A0535+262 is a transient Be/X-ray binary system which was in a quiescent phase from 1994 to 2005. In this paper we report on the timing and spectral properties of the INTEGRAL detection of the source in 2003 October. The source is detected for ∼6000 s in the 18–100 keV energy band at a luminosity of  ∼3.8 × 10³⁵ erg s⁻¹  ; this is compatible with the high end of the range of luminosities expected for quiescent emission. The system is observed to be outside of the centrifugal inhibition regime and pulsations are detected with periodicity,   P = 103.7 ± 0.1 s  . An examination of the pulse history of the source shows that it had been in a constant state of spin-down since it entered the quiescent phase in 1994. The rate of spin-down implies the consistent presence of an accretion disc supplying torques to the pulsar. The observations show that the system is still active and highly variable even in the absence of recent Type I or Type II X-ray outbursts.     

Book reviews

Folsom: New Archaeological Investigations of a Classic Paleoindian Bison Kill. David J. Meltzer, 2006, University of California Press, Berkeley, California, xiv _ 374 pp., $55.00 (hardcover)     

Practical Use of Computationally Frugal Model Analysis Methods

Three challenges compromise the utility of mathematical models of groundwater and other environmental systems: (1) a dizzying array of model analysis methods and metrics make it difficult to compare evaluations of model adequacy, sensitivity, and uncertainty; (2) the high computational demands of many popular model analysis methods (requiring 1000's, 10,000 s, or more model runs) make them difficult to apply to complex models; and (3) many models are plagued by unrealistic nonlinearities arising from the numerical model formulation and implementation. This study proposes a strategy to address these challenges through a careful combination of model analysis and implementation methods. In this strategy, computationally frugal model analysis methods (often requiring a few dozen parallelizable model runs) play a major role, and computationally demanding methods are used for problems where (relatively) inexpensive diagnostics suggest the frugal methods are unreliable. We also argue in favor of detecting and, where possible, eliminating unrealistic model nonlinearities—this increases the realism of the model itself and facilitates the application of frugal methods. Literature examples are used to demonstrate the use of frugal methods and associated diagnostics. We suggest that the strategy proposed in this paper would allow the environmental sciences community to achieve greater transparency and falsifiability of environmental models, and obtain greater scientific insight from ongoing and future modeling efforts.     

Carrier phase-based integrity monitoring for high-accuracy positioning

Pseudorange-based integrity monitoring, for example receiver autonomous integrity monitoring (RAIM), has been investigated for many years and is used in various applications such as non-precision approach phase of flight. However, for high-accuracy applications, carrier phase-based RAIM (CRAIM), an extension of pseudorange-based RAIM (PRAIM) must be used. Existing CRAIM algorithms are a direct extension of PRAIM in which the carrier phase ambiguities are estimated together with the estimation of the position solution. The main issues with the existing algorithms are reliability and robustness, which are dominated by the correctness of the ambiguity resolution, ambiguity validation and error sources such as multipath, cycle slips and noise correlation. This paper proposes a new carrier phase-based integrity monitoring algorithm for high-accuracy positioning, using a Kalman filter. The ambiguities are estimated together with other states in the Kalman filter. The double differenced pseudorange, widelane and carrier phase observations are used as measurements in the Kalman filter. This configuration makes the positioning solution both robust and reliable. The integrity monitoring is based on a number of test statistics and error propagation for the determination of the protection levels. The measurement noise and covariance matrices in the Kalman filter are used to account for the correlation due to differencing of measurements and in the construction of the test statistics. The coefficient used to project the test statistic to the position domain is derived and the synthesis of correlated noise errors is used to determine the protection level. Results from four cases based on limited real data injected with simulated cycle slips show that residual cycle slips have a negative impact on positioning accuracy and that the integrity monitoring algorithm proposed can be effective in detecting and isolating such occurrences if their effects violate the integrity requirements. The CRAIM algorithm proposed is suitable for use within Kalman filter-based integrated navigation systems.

Stability Dependence of the Eddy-Accumulation Coefficients for Momentum and Scalars

From a set of turbulence data collected with a three-axis sonic anemometer/thermometer and described in a companion paper, we simulate the eddy-accumulation process for sensible heat and momentum fluxes. The resulting eddy-accumulation coefficient for momentum clearly depends on surface-layer stability; at neutral stability, its value is 0.63. On supplementing the scalar eddy-accumulation coefficients that we derive from our sensible heat flux data with values of sensible and latent heat flux coefficients reported by Businger and Oncley, we also find that scalar eddy-accumulation coefficients depend on stability, though more weakly than does the momentum coefficient. The coefficients for sensible and latent heat show no significant difference; we, thus, fit them with one function of stability whose value is 0.52 for neutral stratification.     

Using beamforming to maximise the detection capability of small,sparse seismometer arrays deployed to monitor oil field activities

Like most other industrial activities that affect the subsurface, hydraulic fracturing carries the risk of reactivating pre‐existing faults and thereby causing induced seismicity. In some regions, regulators have responded to this risk by imposing traffic light scheme‐type regulations, where fracture stimulation programs must be amended or shut down if events larger than a given magnitude are induced. Some sites may be monitored with downhole arrays and/or dense near‐surface arrays, capable of detecting very small microseismic events. However, such monitoring arrangements will not be logistically or economically feasible at all sites. Instead, operators are using small, sparse arrays of surface seismometers to meet their monitoring obligations. The challenge we address in this paper is to maximise the detection thresholds of such small, sparse, surface arrays so that they are capable of robustly identifying small‐magnitude events whose signal‐to‐noise ratios may be close to 1. To do this, we develop a beamforming‐and‐stacking approach, computing running short‐term/long‐term average functions for each component of each recorded trace (P, S_H, and S_V), time‐shifting these functions by the expected travel times for a given location, and performing a stack. We assess the effectiveness of this approach with a case study using data from a small surface array that recorded a multi‐well, multi‐stage hydraulic fracture stimulation in Oklahoma over a period of 8 days. As a comparison, we initially used a conventional event‐detection algorithm to identify events, finding a total of 17 events. In contrast, the beamforming‐and‐stacking approach identified a total of 155 events during this period (including the 17 events detected by the conventional method). The events that were not detected by the conventional algorithm had low‐signal‐to‐noise ratios to the extent that, in some cases, they would be unlikely to be identified even by manual analysis of the seismograms. We conclude that this approach is capable of improving the detection thresholds of small, sparse arrays and thus can be used to maximise the information generated when deployed to monitor industrial sites.     

Death valley regional ground-water flow model calibration using optimal parameter estimation methods and geoscientific information systems

A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing high contrasts and abrupt contacts. This characteristic, combined with the large scale of the model, make zonation the logical choice for representing the hydraulic-conductivity distribution. Different conceptual models were evaluated using sensitivity analysis and were tested by using nonlinear regression to determine parameter values that are optimal, in that they provide the best match between the measured and simulated heads and flows. The different conceptual models were judged based both on the fit achieved to measured heads and spring flows, and the plausibility of the optimal parameter values. One of the conceptual models considered appears to represent the system most realistically. Any apparent model error is probably caused by the coarse vertical and horizontal discretization.     

Acoustic sounder observations from an elevated location

Details of the top of the atmospheric boundary layer obtained using an acoustic sounder at the summit of an 847 m mountain are presented.