GLM permutation - nonparametric inference for arbitrary general linear models

Introduction: Permutation methods are finding growing use in neuroimag- ing data analyses (e.g. randomise in FSL, SnPM in SPM, XBAMM/BAMM/CAMBA, etc). These methods provide ex- act control of false positives, make only weak assumptions, and allow nonstandard types of statistics (e.g. smoothed variance t- test). With fast and inexpensive computing, there would seem few reasons not to use nonparametric methods. A significant limitation of these methods, however, is the lack of flexibility with respect to the experimental design and nuisance variables. Each specific design dictates the type of exchange- ability of null data, and hence how to permute. Nuisance effects (e.g. age) render data non-exchangeable even when the effect of interest is null. Hence, even something as simple as ANCOVA has no exact permutation test. Recently there has been an active literature on approximate– but accurate–permutation tests for 2-variable regression, one effect of interest, one nuisance (see review by Anderson & Robinson [1]). Here we extend and evaluate these methods for use with an arbitrary General Linear Model (GLM)

A SVD-based transient error method for analyzing noisy multicomponent exponential signals

The problem of estimating the parameters of noisy multicomponent signals using parametric modeling technique is considered in this paper. The multicomponent signal of interest is formed by a superposition of basic functions having the same location in time but different widths and amplitudes. Based on the modified Gardner transformation, some samples of deconvolved data are derived from the multicomponent signals. The deconvolved data are then modeled using a special nonstationary autoregressive moving average (ARMA) process in which the parameters of the ARMA model are obtained by linear least-squares procedure. The least-squares procedure is based on the singular value decomposition (SVD) to overcome the limitations of the transient error method (TEM) of analysis that uses cholesky decomposition to determine its AR coefficients. The moving average (MA) coefficients corresponds to the initial residual error sequences so as to account for the nonstationary noise in the deconvolved data. This new method of analysis, termed the SVD-based transient error method, produces high resolution estimates of the exponents of multicomponent signals at both low and high signal to noise (SNR) ratios

Heavy ion induced Single Event Phenomena (SEP) data for semiconductor devices from engineering testing

The accumulation of JPL data on Single Event Phenomena (SEP), from 1979 to August 1986, is presented in full report format. It is expected that every two years a supplement report will be issued for the follow-on period. This data for 135 devices expands on the abbreviated test data presented as part of Refs. (1) and (3) by including figures of Single Event Upset (SEU) cross sections as a function of beam Linear Energy Transfer (LET) when available. It also includes some of the data complied in the JPL computer in RADATA and the SPACERAD data bank. This volume encompasses bipolar and MOS (CMOS and MHNOS) device data as two broad categories for both upsets (bit-flips) and latchup. It also includes comments on less well known phenomena, such as transient upsets and permanent damage modes

Estimating forecast error covariances for strongly coupled atmosphere-ocean 4D-Var data assimilation

Strongly coupled data assimilation emulates the real world pairing of the atmosphere and ocean by solving the assimilation problem in terms of a single combined atmosphere-ocean state. A significant challenge in strongly coupled variational atmosphere-ocean data assimilation is a priori specification of the cross-covariances between the errors in the atmosphere and ocean model forecasts. These covariances must capture the correct physical structure of interactions across the air-sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, they will allow observations in one medium to improve the analysis in the other. Here we investigate the nature and structure of atmosphere-ocean forecast error cross-correlations using an idealised strongly coupled single-column atmosphere-ocean 4D-Var assimilation system. We present results from a set of identical twin experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere-ocean error cross-correlations. Our results show significant variation in the strength and structure of cross-correlations in the atmosphere-ocean boundary layer between summer and winter and between day and night. These differences provide a valuable insight into the nature of coupled atmosphere-ocean correlations for different seasons and points in the diurnal cycle

The role of cross-domain error correlations in strongly coupled 4D-Var atmosphere-ocean data assimilation

Strongly coupled atmosphere-ocean data assimilation offers the ability to improve information exchange across the modelled air-sea interface by enabling observations in one domain to have a direct influence on the analysis in the other. For incremental 4D-Var assimilation a strongly coupled approach enables both domains to be updated at the beginning of the assimilation window, whether they are observed or not, and is hence more likely to produce consistent initial model states. This is made possible by the explicit inclusion of cross-domain forecast error covariance information in the coupled forecast error covariance matrix. In this study we use an idealised 1D single column coupled atmosphere-ocean model to examine the extent to which explicit cross-domain forecast error covariances play a role in shaping the coupled analysis increments compared to those implicitly generated in the inner-loop of the incremental formulation of the 4D-Var algorithm. This is done via a set of single observation experiments with and without initial cross-domain forecast error covariances prescribed. Using single observations allows us to obtain explicit expressions for the atmosphere and ocean analysis updates, separating out the individual effects of the explicitly prescribed and implicitly generated cross-domain covariances. Our experiments show that when only one domain is observed,including explicit cross-domain error covariances allows more consistent adjustment of the unobserved domain. Neglecting the cross-domain terms and relying solely on the covariances implicitly generated by the coupled tangent linear and adjoint models restricts the ability of the covariance matrix to impose balance between the two domains. In this case the coupling is essentially one-way; the update to the observed domain is independent of the unobserved domain and so is likely to produce atmosphere and ocean updates that are inconsistent with one another. As we show, this has important consequences for the balance of the coupled analysis state

Application of ARMA modeling to multicomponent signals

This paper investigates the problem of estimating the parameters of a multicomponent signal observed in noise. The process is modeled las a special nonstationary autoregressive moving average (ARMA) process. The parameters of the multicomponent signal are determined from the spectral estimate of the ARMA model The spectral lines are closely spaced and the ARMA model must be determined from very short data records. Two high-resolution ARMA algorithms are developed for determining the spectral estimates. The first ARMA algorithm modifies the extended Prony method to account for the nonstationary aspects of noise in the model.For comPonents signals with good signal to noise ratio (SNR) this algorithm provides excellent results, but for a lower SNR the performance degrades resulting in a loss in resolution. The second algorithm is based on the work of Cadzow. The algorithm presented overcomes the difficulties of Cadzow's and Kaye's algorithms and provides the coefficients for the complete model not just the spen ral estimate. This algorithm performs well in resolving multicomponent signals when the SNR is low

Age and sex-specific rates of leaf regeneration in the Mojave Desert moss Syntrichia caninervis

The extremely skewed female-biased sex ratio in the desert moss Syntrichia caninervis was investigated by assessing the regeneration capacity of detached leaves. Juvenile, green, yellow-green, and brown leaves equating to approximately 0, 2, 6, and 12 yr of age, respectively, were detached from individuals of S. caninervis collected from 10 field populations and grown in a growth chamber for 58 d at a light intensity of 33–128 µmol · m–2 · s–1. Younger leaves (0–2 yr old) tended to have a greater viability, regenerate more quickly, extend their protonemal filaments farther, produce shoots (gametophores) more quickly, produce more shoots, and accumulate a greater biomass than older leaves (6 and 12 yr old). Among younger leaf classes, regenerating female leaves were more likely to produce a shoot than male leaves and produced more shoots than male leaves. The sexes did not differ significantly in time until protonemal emergence, linear extension of protonemata, or rate of biomass accumulation. However, protonemata of male leaves tended to emerge more quickly and produce a greater total biomass, ultimately consisting mostly of protonemata, than did female leaves. The more rapid proliferation of shoots by female leaf regenerants may help to explain the rarity of males in this species

Observation operators for assimilation of satellite observations in fluvial inundation forecasting

Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of flood water during a river flood event. This observational information typically covers a large spatial area, but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way a suitable observation operator is required. In this paper we show that different types of observation operator can produce very different corrections to predicted water levels; this impacts on the quality of the forecast produced.We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead 10 designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms by which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain

Observation impact, domain length and parameter estimation in data assimilation for flood forecasting

Accurate inundation forecasting provides vital information about the behaviour of fluvial flood water. Using data assimilation with an Ensemble Transform Kalman Filter we combine forecasts from a numerical hydrodynamic model with synthetic observations of water levels. We show that reinitialising the model with corrected water levels can cause an initialisation shock and demonstrate a simple novel solution. In agreement with others, we find that although assimilation can accurately correct water levels at observation times, the corrected forecast quickly relaxes to the open loop forecast. Our new work shows that the time taken for the forecast to relax to the open loop case depends on domain length; observation impact is longer-lived in a longer domain. We demonstrate that jointly correcting the channel friction parameter as well as water levels greatly improves the forecast. We also show that updating the value of the channel friction parameter can compensate for bias in inflow