Representativity error for temperature and humidity using the Met Office high-resolution model

The observation-error covariance matrix used in data assimilation contains contributions from instrument errors, representativity errors and errors introduced by the approximated observation operator. Forward model errors arise when the observation operator does not correctly model the observations or when observations can resolve spatial scales that the model cannot. Previous work to estimate the observation-error covariance matrix for particular observing instruments has shown that it contains signifcant correlations. In particular, correlations for humidity data are more significant than those for temperature. However it is not known what proportion of these correlations can be attributed to the representativity errors. In this article we apply an existing method for calculating representativity error, previously applied to an idealised system, to NWP data. We calculate horizontal errors of representativity for temperature and humidity using data from the Met Office high-resolution UK variable resolution model. Our results show that errors of representativity are correlated and more significant for specific humidity than temperature. We also find that representativity error varies with height. This suggests that the assimilation scheme may be improved if these errors are explicitly included in a data assimilation scheme. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Improved multi-beam neural network scatterometer forward models

Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in ¸mod, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the mid-beam and using a common model for the fore- and aft-beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Improved neural network scatterometer forward models

Current methods for retrieving near-surface winds from scatterometer observations over the ocean surface require a forward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in CMOD4, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the midbeam and using a common model for the fore and aft beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds. Copyright 2001 by the American Geophysical Union.

R-script modified VS-Lite model

Drought is a key factor affecting forest ecosystem processes at different spatio-temporal scales. For accurately modeling tree functioning ? and thus for producing reliable simulations of forest dynamics ? the consideration of the variability in the timing and extent of drought effects on tree growth is essential, particularly in strongly seasonal climates such as in the Mediterranean area. Yet, most dynamic vegetation models (DVMs) do not include this intra-annual variability of drought effects on tree growth. We present a novel approach for linking tree-ring data to drought simulations in DVMs. A modified forward model of tree-ring width (VS-Lite) was used to estimate seasonal- and site-specific growth responses to drought of Scots pine (Pinus sylvestris L.), which were subsequently implemented in the DVM ForClim. Ring-width data from sixteen sites along a moisture gradient from Central Spain to the Swiss Alps, including the dry inner Alpine valleys, were used to calibrate the forward ring-width model, and inventory data from managed Scots pine stands were used to evaluate ForClim performance. The modified VS-Lite accurately estimated the year-to-year variability in ring-width indices and produced realistic intra-annual growth responses to soil drought, showing a stronger relationship between growth and drought in spring than in the other seasons and thus capturing the strategy of Scots pine to cope with drought. The ForClim version including seasonal variability in growth responses to drought showed improved predictions of stand basal area and stem number, indicating the need to consider intra-annual differences in climate-growth relationships in DVMs when simulating forest dynamics. Forward modeling of ring-width growth may be a powerful tool to calibrate growth functions in DVMs that aim to simulate forest properties in across multiple environments at large spatial scales.

Swarm intelligence-based solver for parameter estimation of laboratory through-diffusion transport of contaminants

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated be fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. Thes parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision.

Implementation of a phase array diffuse optical tomographic imager

Diffuse optical tomography (DOT) using near-infrared (NIR) light is a promising tool for noninvasive imaging of deep tissue. This technique is capable of quantitative reconstructions of absorption coefficient inhomogeneities of tissue. The motivation for reconstructing the optical property variation is that it, and, in particular, the absorption coefficient variation, can be used to diagnose different metabolic and disease states of tissue. In DOT, like any other medical imaging modality, the aim is to produce a reconstruction with good spatial resolution and accuracy from noisy measurements. We study the performance of a phase array system for detection of optical inhomogeneities in tissue. The light transport through a tissue is diffusive in nature and can be modeled using diffusion equation if the optical parameters of the inhomogeneity are close to the optical properties of the background. The amplitude cancellation method that uses dual out-of-phase sources (phase array) can detect and locate small objects in turbid medium. The inverse problem is solved using model based iterative image reconstruction. Diffusion equation is solved using finite element method for providing the forward model for photon transport. The solution of the forward problem is used for computing the Jacobian and the simultaneous equation is solved using conjugate gradient search. The simulation studies have been carried out and the results show that a phase array system can resolve inhomogeneities with sizes of 5 mm when the absorption coefficient of the inhomogeneity is twice that of the background tissue. To validate this result, a prototype model for performing a dual-source system has been developed. Experiments are carried out by inserting an inhomogeneity of high optical absorption coefficient in an otherwise homogeneous phantom while keeping the scattering coefficient same. The high frequency (100 MHz) modulated dual out-of-phase laser source light is propagated through the phantom. The interference of these sources creates an amplitude null and a phase shift of 180° along a plane between the two sources with a homogeneous object. A solid resin phantom with inhomogeneities simulating the tumor is used in our experiment. The amplitude and phase changes are found to be disturbed by the presence of the inhomogeneity in the object. The experimental data (amplitude and the phase measured at the detector) are used for reconstruction. The results show that the method is able to detect multiple inhomogeneities with sizes of 4 mm. The localization error for a 5 mm inhomogeneity is found to be approximately 1 mm.

Generalized eigenvalue decomposition of the field autocorrelation in correlation diffusion of photons in turbid media

We propose a novel numerical method based on a generalized eigenvalue decomposition for solving the diffusion equation governing the correlation diffusion of photons in turbid media. Medical imaging modalities such as diffuse correlation tomography and ultrasound-modulated optical tomography have the (elliptic) diffusion equation parameterized by a time variable as the forward model. Hitherto, for the computation of the correlation function, the diffusion equation is solved repeatedly over the time parameter. We show that the use of a certain time-independent generalized eigenfunction basis results in the decoupling of the spatial and time dependence of the correlation function, thus allowing greater computational efficiency in arriving at the forward solution. Besides presenting the mathematical analysis of the generalized eigenvalue problem on the basis of spectral theory, we put forth the numerical results that compare the proposed numerical method with the standard technique for solving the diffusion equation.

I. The Superstition Hills, California, earthquakes of 24 November 1987. II. Three-dimensional velocity structure of southern California.

Part 1 of this thesis is about the 24 November, 1987, Superstition Hills earthquakes. The Superstition Hills earthquakes occurred in the western Imperial Valley in southern California. The earthquakes took place on a conjugate fault system consisting of the northwest-striking right-lateral Superstition Hills fault and a previously unknown Elmore Ranch fault, a northeast-striking left-lateral structure defined by surface rupture and a lineation of hypocenters. The earthquake sequence consisted of foreshocks, the M_s 6.2 first main shock, and aftershocks on the Elmore Ranch fault followed by the M_s 6.6 second main shock and aftershocks on the Superstition Hills fault. There was dramatic surface rupture along the Superstition Hills fault in three segments: the northern segment, the southern segment, and the Wienert fault.

In Chapter 2, M_L≥4.0 earthquakes from 1945 to 1971 that have Caltech catalog locations near the 1987 sequence are relocated. It is found that none of the relocated earthquakes occur on the southern segment of the Superstition Hills fault and many occur at the intersection of the Superstition Hills and Elmore Ranch faults. Also, some other northeast-striking faults may have been active during that time.

Chapter 3 discusses the Superstition Hills earthquake sequence using data from the Caltech-U.S.G.S. southern California seismic array. The earthquakes are relocated and their distribution correlated to the type and arrangement of the basement rocks. The larger earthquakes occur only where continental crystalline basement rocks are present. The northern segment of the Superstition Hills fault has more aftershocks than the southern segment.

An inversion of long period teleseismic data of the second mainshock of the 1987 sequence, along the Superstition Hills fault, is done in Chapter 4. Most of the long period seismic energy seen teleseismically is radiated from the southern segment of the Superstition Hills fault. The fault dip is near vertical along the northern segment of the fault and steeply southwest dipping along the southern segment of the fault.

Chapter 5 is a field study of slip and afterslip measurements made along the Superstition Hills fault following the second mainshock. Slip and afterslip measurements were started only two hours after the earthquake. In some locations, afterslip more than doubled the coseismic slip. The northern and southern segments of the Superstition Hills fault differ in the proportion of coseismic and postseismic slip to the total slip.

The northern segment of the Superstition Hills fault had more aftershocks, more historic earthquakes, released less teleseismic energy, and had a smaller proportion of afterslip to total slip than the southern segment. The boundary between the two segments lies at a step in the basement that separates a deeper metasedimentary basement to the south from a shallower crystalline basement to the north.

Part 2 of the thesis deals with the three-dimensional velocity structure of southern California. In Chapter 7, an a priori three-dimensional crustal velocity model is constructed by partitioning southern California into geologic provinces, with each province having a consistent one-dimensional velocity structure. The one-dimensional velocity structures of each region were then assembled into a three-dimensional model. The three-dimension model was calibrated by forward modeling of explosion travel times.

In Chapter 8, the three-dimensional velocity model is used to locate earthquakes. For about 1000 earthquakes relocated in the Los Angeles basin, the three-dimensional model has a variance of the the travel time residuals 47 per cent less than the catalog locations found using a standard one-dimensional velocity model. Other than the 1987 Whittier earthquake sequence, little correspondence is seen between these earthquake locations and elements of a recent structural cross section of the Los Angeles basin. The Whittier sequence involved rupture of a north dipping thrust fault bounded on at least one side by a strike-slip fault. The 1988 Pasadena earthquake was deep left-lateral event on the Raymond fault. The 1989 Montebello earthquake was a thrust event on a structure similar to that on which the Whittier earthquake occurred. The 1989 Malibu earthquake was a thrust or oblique slip event adjacent to the 1979 Malibu earthquake.

At least two of the largest recent thrust earthquakes (San Fernando and Whittier) in the Los Angeles basin have had the extent of their thrust plane ruptures limited by strike-slip faults. This suggests that the buried thrust faults underlying the Los Angeles basin are segmented by strike-slip faults.

Earthquake and explosion travel times are inverted for the three-dimensional velocity structure of southern California in Chapter 9. The inversion reduced the variance of the travel time residuals by 47 per cent compared to the starting model, a reparameterized version of the forward model of Chapter 7. The Los Angeles basin is well resolved, with seismically slow sediments atop a crust of granitic velocities. Moho depth is between 26 and 32 km.

Using Earth Deformation Caused by Surface Mass Loading to Constrain the Elastic Structure of the Crust and Mantle

Surface mass loads come in many different varieties, including the oceans, atmosphere, rivers, lakes, glaciers, ice caps, and snow fields. The loads migrate over Earth's surface on time scales that range from less than a day to many thousand years. The weights of the shifting loads exert normal forces on Earth's surface. Since the Earth is not perfectly rigid, the applied pressure deforms the shape of the solid Earth in a manner controlled by the material properties of Earth's interior. One of the most prominent types of surface mass loading, ocean tidal loading (OTL), comes from the periodic rise and fall in sea-surface height due to the gravitational influence of celestial objects, such as the moon and sun. Depending on geographic location, the surface displacements induced by OTL typically range from millimeters to several centimeters in amplitude, which may be inferred from Global Navigation and Satellite System (GNSS) measurements with sub-millimeter precision. Spatiotemporal characteristics of observed OTL-induced surface displacements may therefore be exploited to probe Earth structure. In this thesis, I present descriptions of contemporary observational and modeling techniques used to explore Earth's deformation response to OTL and other varieties of surface mass loading. With the aim to extract information about Earth's density and elastic structure from observations of the response to OTL, I investigate the sensitivity of OTL-induced surface displacements to perturbations in the material structure. As a case study, I compute and compare the observed and predicted OTL-induced surface displacements for a network of GNSS receivers across South America. The residuals in three distinct and dominant tidal bands are sub-millimeter in amplitude, indicating that modern ocean-tide and elastic-Earth models well predict the observed displacement response in that region. Nevertheless, the sub-millimeter residuals exhibit regional spatial coherency that cannot be explained entirely by random observational uncertainties and that suggests deficiencies in the forward-model assumptions. In particular, the discrepancies may reveal sensitivities to deviations from spherically symmetric, non-rotating, elastic, and isotropic (SNREI) Earth structure due to the presence of the South American craton.

Internal models in the cerebellum.

This review will focus on the possibility that the cerebellum contains an internal model or models of the motor apparatus. Inverse internal models can provide the neural command necessary to achieve some desired trajectory. First, we review the necessity of such a model and the evidence, based on the ocular following response, that inverse models are found within the cerebellar circuitry. Forward internal models predict the consequences of actions and can be used to overcome time delays associated with feedback control. Secondly, we review the evidence that the cerebellum generates predictions using such a forward model. Finally, we review a computational model that includes multiple paired forward and inverse models and show how such an arrangement can be advantageous for motor learning and control.

Modulation of somatosensory processing by action.

Psychophysical evidence suggests that sensations arising from our own movements are diminished when predicted by motor forward models and that these models may also encode the timing and intensity of movement. Here we report a functional magnetic resonance imaging study in which the effects on sensation of varying the occurrence, timing and force of movements were measured. We observed that tactile-related activity in a region of secondary somatosensory cortex is reduced when sensation is associated with movement and further that this reduction is maximal when movement and sensation occur synchronously. Motor force is not represented in the degree of attenuation but rather in the magnitude of this region's response. These findings provide neurophysiological correlates of previously-observed behavioural forward-model phenomena, and advocate the adopted approach for the study of clinical conditions in which forward-model deficits have been posited to play a crucial role.