Spectra of poorly resolved ocean data

Standard temperature and pressure sensors on Aanderaa RCM8 current meters have a resolution of 0.024 °C and 0.6 bar, each equal to 1 digital number (value) over a range of 1024. It is shown that an 11-month deep-ocean temperature record using only four values can contain useful spectral information on internal wave motions. This is partially due to the modulation of high-frequency data by non-zero low-frequency (subinertial) variations. This result follows from the comparison of this record with artificial three- and four-value data constructed from temperature records observed in stronger stratified waters nearby. These artificial records show main features of the internal wave band similar to those observed in the original data spectra. Peaks at tidal harmonic frequencies and enhancements at sum-tidal-inertial interaction frequencies are preserved in the artificial data, but overall noise level (and thus the continuum spectral slope) is enhanced with respect to the properly resolved records (using 15 and 100 values). As a demonstration of the stable accuracy of the temperature sensors, the poorly resolved records provided an estimate of mean stratification to within 5% of the estimate using Seabird CTD data.Responsible Editor: Hans Burchard     

Building and Testing an <Emphasis Type="Italic">a priori</Emphasis> Geophysical Model for Western Eurasia and North Africa

We construct and evaluate a new three-dimensional model of crust and upper mantle structure in Western Eurasia and North Africa (WENA) extending to 700 km depth and having 1° parameterization. The model is compiled in an a priori fashion entirely from existing geophysical literature, specifically, combining two regionalized crustal models with a high-resolution global sediment model and a global upper mantle model. The resulting WENA1.0 model consists of 24 layers: water, three sediment layers, upper, middle, and lower crust, uppermost mantle, and 16 additional upper mantle layers. Each of the layers is specified by its depth, compressional and shear velocity, density, and attenuation (quality factors, Q _P and Q _S ). The model is tested by comparing the model predictions with geophysical observations including: crustal thickness, surface wave group and phase velocities, upper mantle _n velocities, receiver functions, P-wave travel times, waveform characteristics, regional 1-D velocities, and Bouguer gravity. We find generally good agreement between WENA1.0 model predictions and empirical observations for a wide variety of independent data sets. We believe this model is representative of our current knowledge of crust and upper mantle structure in the WENA region and can successfully be used to model the propagation characteristics of regional seismic waveform data. The WENA1.0 model will continue to evolve as new data are incorporated into future validations and any new deficiencies in the model are identified. Eventually this a priori model will serve as the initial starting model for a multiple data set tomographic inversion for structure of the Eurasian continent.     

Community preparedness for lava flows from Mauna Loa and Hualālai volcanoes,Kona, Hawai‘i

Lava flows from Mauna Loa and Huallai volcanoes are a major volcanic hazard that could impact the western portion of the island of Hawaii (e.g., Kona). The most recent eruptions of these two volcanoes to affect Kona occurred in a.d. 1950 and ca. 1800, respectively. In contrast, in eastern Hawaii, eruptions of neighboring Klauea volcano have occurred frequently since 1955, and therefore have been the focus for hazard mitigation. Official preparedness and response measures are therefore modeled on typical eruptions of Klauea.The combinations of short-lived precursory activity (e.g., volcanic tremor) at Mauna Loa, the potential for fast-moving lava flows, and the proximity of Kona communities to potential vents represent significant emergency management concerns in Kona. Less is known about past eruptions of Huallai, but similar concerns exist. Future lava flows present an increased threat to personal safety because of the short times that may be available for responding.Mitigation must address not only the specific characteristics of volcanic hazards in Kona, but also the manner in which the hazards relate to the communities likely to be affected. This paper describes the first steps in developing effective mitigation plans: measuring the current state of peoples knowledge of eruption parameters and the implications for their safety. We present results of a questionnaire survey administered to 462 high school students and adults in Kona. The rationale for this study was the long lapsed time since the last Kona eruption, and the high population growth and expansion of infrastructure over this time interval. Anticipated future growth in social and economic infrastructure in this area provides additional justification for this work.The residents of Kona have received little or no specific information about how to react to future volcanic eruptions or warnings, and short-term preparedness levels are low. Respondents appear uncertain about how to respond to threatening lava flows and overestimate the minimum time available to react, suggesting that personal risk levels are unnecessarily high. A successful volcanic warning plan in Kona must be tailored to meet the unique situation there.     

Reproduction of secondary data in projection pursuit transformation

A challenge when working with multivariate data in a geostatistical context is that the data are rarely Gaussian. Multivariate distributions may include nonlinear features, clustering, long tails, functional boundaries, spikes, and heteroskedasticity. Multivariate transformations account for such features so that they are reproduced in geostatistical models. Projection pursuit as developed for high dimensional data exploration can also be used to transform a multivariate distribution into a multivariate Gaussian distribution with an identity covariance matrix. Its application within a geostatistical modeling context is called the projection pursuit multivariate transform (PPMT). An approach to incorporate exhaustive secondary variables in the PPMT is introduced. With this approach the PPMT can incorporate any number of secondary variables with any number of primary variables. A necessary alteration to the approach to make this numerically practical was the implementation of a continuous probability estimator that relies on Bernstein polynomials for the transformation that takes place in the projections. Stopping criteria were updated to incorporate a bootstrap t test that compares data sampled from a multivariate Gaussian distribution with the data undergoing transformation.     

Fast and direct nonparametric procedures in the L-moment homogeneity test

Regional frequency analysis is an important tool to properly estimate hydrological characteristics at ungauged or partially gauged sites in order to prevent hydrological disasters. The delineation of homogeneous groups of sites is an important first step in order to transfer information and obtain accurate quantile estimates at the target site. The Hosking–Wallis homogeneity test is usually used to test the homogeneity of the selected sites. Despite its usefulness and good power, it presents some drawbacks including the subjective choice of a parametric distribution for the data and a poorly justified rejection threshold. The present paper addresses these drawbacks by integrating nonparametric procedures in the L-moment homogeneity test. To assess the rejection threshold, three resampling methods (permutation, bootstrap and Pólya resampling) are considered. Results indicate that permutation and bootstrap methods perform better than the parametric Hosking–Wallis test in terms of power as well as in time and procedure simplicity. A real-world case study shows that the nonparametric tests agree with the HW test concerning the homogeneity of the volume and the bivariate case while they disagree for the peak case, but that the assumptions of the HW test are not well respected.     

Impact of sensor measurement error on sensor positioning in water quality monitoring networks

This paper studies the impact of sensor measurement error on designing a water quality monitoring network for a river system, and shows that robust sensor locations can be obtained when an optimization algorithm is combined with a statistical process control (SPC) method. Specifically, we develop a possible probabilistic model of sensor measurement error and the measurement error model is embedded into a simulation model of a river system. An optimization algorithm is used to find the optimal sensor locations that minimize the expected time until a spill detection in the presence of a constraint on the probability of detecting a spill. The experimental results show that the optimal sensor locations are highly sensitive to the variability of measurement error and false alarm rates are often unacceptably high. An SPC method is useful in finding thresholds that guarantee a false alarm rate no more than a pre-specified target level, and an optimization algorithm combined with the thresholds finds a robust sensor network.     

Seasonal copula models for the analysis of glacier discharge at King George Island,Antarctica

Modelling glacier discharge is an important issue in hydrology and climate research. Glaciers represent a fundamental water resource when melting of ice and snow contributes to runoff. Glaciers are also studied as natural global warming sensors. GLACKMA association has implemented one of their Pilot Experimental Catchment areas at the King George Island in the Antarctica which records values of the liquid discharge from Collins glacier. In this paper, we propose the use of time-varying copula models for analyzing the relationship between air temperature and glacier discharge, which is clearly non constant and non linear through time. A seasonal copula model is defined where both the marginal and copula parameters vary periodically along time following a seasonal dynamic. Full Bayesian inference is performed such that the marginal and copula parameters are estimated in a one single step, in contrast with the usual two-step approach. Bayesian prediction and model selection is also carried out for the proposed model such that Bayesian credible intervals can be obtained for the conditional glacier discharge given a value of the temperature at any given time point. The proposed methodology is illustrated using the GLACKMA real data where there is, in addition, a hydrological year of missing discharge data which were not possible to measure accurately due to problems in the sounding.