Search

A method for infering bed topography beneath glaciers from surface measurements (elevation from altimetry and velocity from InSAR) and sparse thickness measurements is developed and evaluated. The method is based on an original non-isothermal Reduced Uncertainty (RU) version of the Shallow Ice Approximation (SIA) equation that natively incorporates the surface measurements. The flow model has a single dimensionless multi-physics parameter γ. This parameter takes into account the basal slipperiness and the variable vertical rate factor profiles, thus the vertical thermal variations. The inversions are based on three steps involving: an Artificial Neural Network (ANN) and two Variational Data Assimilation (VDA) processes. The ANN-based stage aims at estimating the multi-physics number γ from the thickness measurements; the resulting estimator is remarkably robust. The full inversion method is valid for half-sheared flows (presenting a moderate basal slipperiness): it can be applied to inland ice-sheets areas. Also these estimates connect continuously with estimates from mass conservation only, i.e. with areas of sliding flows. Numerical results are presented for areas of the East Antarctica Ice Sheet where bed elevation can be very uncertain (Bedmap2 values). Estimates are valid for wavelengths longer than $\sim 10 \bar h$ (due to the long wave assumption, shallow flow model) with resolution at $\sim \bar h$ ( $\bar h$ a characteristic thickness value).

The advent of high-resolution imaging has made data on surface shape widespread. Methods for the analysis of shape based on landmarks are well established but high-resolution data require a functional approach. The starting point is a systematic and consistent description of each surface shape and a method for creating this is described. Three innovative forms of analysis are then introduced. The first uses surface integration to address issues of registration, principal component analysis and the measurement of asymmetry, all in functional form. Computational issues are handled through discrete approximations to integrals, based in this case on appropriate surface area weighted sums. The second innovation is to focus on sub-spaces where interesting behaviour such as group differences are exhibited, rather than on individual principal components. The third innovation concerns the comparison of individual shapes with a relevant control set, where the concept of a normal range is extended to the highly multivariate setting of surface shape. This has particularly strong applications to medical contexts where the assessment of individual patients is very important. All of these ideas are developed and illustrated in the important context of human facial shape, with a strong emphasis on the effective visual communication of effects of interest.

The impact of the data assimilation process of air temperature and relative humidity from surface meteorological stations and sounding at airports in the terminal area of Rio de Janeiro is evaluated using the Weather Research and Forecast Data Assimilation system. Synthetic data of temperature, relative humidity and wind are generated in the locations of airport sensors by applying a white-noise perturbation in the forecast data. Results show a positive overall impact of the assimilation process with the removal of part of the noise in the observation data but keeping the effect of local conditions in the later timesteps of the simulation. In addition, with the assimilation process there is a global reduction of the error between the analysis data and the observation data. In the future, a neural network will be trained to emulate the data assimilation process to speed-up the assimilation process in the WRF model. Resumo O impacto do processo de assimilação de dados de temperatura do ar e umidade relativa de estações meteorológicas de superfície e sondagem em aeroportos na área terminal do Rio de Janeiro é avaliado usando o sistema Weather Research and Forecast Data Assimilation. Dados sintéticos de temperatura, umidade relativa e vento, são gerados nas localidades dos sensores dos aeroportos a partir da aplicação de uma perturbação gaussiana nos dados de previsão. Resultados mostram um resultado positivo do processo de assimilação com remoção de parte do ruído nos dados de observação, mas mantendo o efeito das condições locais nos instantes posteriores da simulação. Além disso, com o processo de assimilação há uma redução global no domínio do erro entre os dados da análise e os dados de observação. No futuro, uma rede neural será treinada para emular o processo de assimilação de dados para acelerar o processo de assimilação no modelo WRF.

Digital elevation models (DEMs) represents the elevation of the earth’s surface in the form of a digital image where each pixel contains an elevation value of the center point of the pixel. DEMs are a primary input to any modeling or process quantification involving the earth’s topography and are used across several areas of development. Access to elevation, and slope maps enable responders to assess where floods will infill the landscape, create inaccessible areas, or create health risks, example, cholera. DEMs are also used prominently in infrastructure planning and mapping; road design and construction for transportation; urban environmental planning to assess construction, drainage, and green landscaping; agriculture planting and irrigation strategies; ecological modeling to assess ecosystem flora and fauna; and geological applications such as seismic and coastal monitoring. Accurate elevation information is therefore key for a wide range of development projects related to poverty reduction, urban development, water management, and other concerns. Thus, the ability to design and commission or acquire DEMs is increasing in relevance across the globe. This DEMs guidance note aims to: (a) provide sufficient information to understand the overall processes involved in the acquisition of DEMs and their uses, and (b) inform and guide the decision-making criteria; different design and implementation strategies; and options and costs that exist when acquiring DEMs.

Mud motor failure is a significant contributor to non-productive time in North American land drilling operations. Currently, there is limited work on mud motor failure analysis using surface drilling data. The objective of this work was to apply data analytics methodologies on historical drilling datasets to identify features and trends in surface data that contribute most to indicating impending mud motor failure. The methodology involved investigating a large dataset of mud motor runs for certain on-bottom and off-bottom drilling events that are generally known to be leading causes of motor failure. Spikes in differential pressure, pick up practices, drill-off time, etc., were all investigated. Additionally, the impact of temperature on mud motor failure was studied. A combination of the investigated features was analyzed using statistical measures and supervised machine learning algorithms to determine the leading contributors to motor failure. The dataset consisted of 32 motor runs drilled in the lateral section of wells from early-to-mid 2019. These motor runs represented a mix of both failure and non-failure cases. The motor stalls were categorized as either low, medium, or high impact depending on the severity of the differential pressure spike. It was observed that high impact motor stalls and the rate of stalling correlated strongly with eventual failure. Likewise, prolonged exposure to high bottom-hole temperature (exceeding the motor temperature limit) increased the risk of motor damage, most likely through thermal expansion of the stator elastomer. The drill-off time was also found to have some influence on motor failure for the investigated cases. Using these features, statistical significance tests and supervised machine learning models were trained on the dataset to determine the features that contribute most to motor failure. This work shows the feasibility of performing mud motor failure analysis using readily available surface data. It is expected to inform drilling engineers on data analysis methodologies for efficient failure analysis and help them plan future wells more efficiently.

As high-dimensional and high-frequency data are being collected on a large scale, the development of new statistical models is being pushed forward. Functional data analysis provides the required statistical methods to deal with large-scale and complex data by assuming that data are continuous functions, e.g., a realization of a continuous process (curves) or continuous random fields (surfaces), and that each curve or surface is considered as a single observation. Here, we provide an overview of functional data analysis when data are complex and spatially correlated. We provide definitions and estimators of the first and second moments of the corresponding functional random variable. We present two main approaches: The first assumes that data are realizations of a functional random field, i.e., each observation is a curve with a spatial component. We call them 'spatial functional data'. The second approach assumes that data are continuous deterministic fields observed over time. In this case, one observation is a surface or manifold, and we call them 'surface time series'. For the two approaches, we describe software available for the statistical analysis. We also present a data illustration, using a high-resolution wind speed simulated dataset, as an example of the two approaches. The functional data approach offers a new paradigm of data analysis, where the continuous processes or random fields are considered as a single entity. We consider this approach to be very valuable in the context of big data., Some typos fixed and new references added

A method to infer the bed elevation from glaciers surface measurements (eleva-tion, velocity) and sparse in-situ thickness values is developed and assessed. This inversion method relies on: a statistical model (Deep Neural Network) based on the in-situ thickness measurements, the dedicated RU-SIA flow model (RU for Reduced Uncertainty) natively integrating the surface measurements (altimetry, InSAR) and advanced Variational Data Assimilation processes. The RU-SIA model takes into account basal slipperiness and non uniform vertical profiles (including thermal gradients) via an unique dimensionless parameter. The inversion method is robust; it may be applied to very poorly covered and uncovered areas during airborne campaigns as soon as flows are moderately sheared. Numerical inversions are performed for some large East Antarctica Ice Sheet areas presenting surface velocities ranging from ∼ 5 to 80 m/y. Estimations are provided in uncovered areas during airborne campaigns hence presenting up to now highly uncertain bed elevation values. The estimations are valid for wave lengths greater than ∼ 10 ¯ h due to the considered shallow flow assumption, with a resolution at ∼ ¯ h (¯ h a characteristic thickness value). Detailed analysis and comparisons with the bed topography BedMap2 are presented.

We present a comprehensive estimate of nitrous oxide (N2O) emissions using observations and models from 1995 to 2008. High-frequency records of tropospheric N2O are available from measurements at Cape Grim, Tasmania; Cape Matatula, American Samoa; Ragged Point, Barbados; Mace Head, Ireland; and at Trinidad Head, California using the Advanced Global Atmospheric Gases Experiment (AGAGE) instrumentation and calibrations. The Global Monitoring Division of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) has also collected discrete air samples in flasks and in situ measurements from remote sites across the globe and analyzed them for a suite of species including N2O. In addition to these major networks, we include in situ and aircraft measurements from the National Institute of Environmental Studies (NIES) and flask measurements from the Tohoku University and Commonwealth Scientific and Industrial Research Organization (CSIRO) networks. All measurements show increasing atmospheric mole fractions of N2O, with a varying growth rate of 0.1–0.7% per year, resulting in a 7.4% increase in the background atmospheric mole fraction between 1979 and 2011. Using existing emission inventories as well as bottom-up process modeling results, we first create globally gridded a priori N2O emissions over the 37 years since 1975. We then use the three-dimensional chemical transport model, Model for Ozone and Related Chemical Tracers version 4 (MOZART v4), and a Bayesian inverse method to estimate global as well as regional annual emissions for five source sectors from 13 regions in the world. This is the first time that all of these measurements from multiple networks have been combined to determine emissions. Our inversion indicates that global and regional N2O emissions have an increasing trend between 1995 and 2008. Despite large uncertainties, a significant increase is seen from the Asian agricultural sector in recent years, most likely due to an increase in the use of nitrogenous fertilizers, as has been suggested by previous studies.

International audience; This paper deals with an inverse scattering problem in an acoustic waveguide. The data consist of time domain signals given by sources and receivers located on the boundary of the waveguide. After transforming the data to the frequency domain, the obstacle is then recovered by using a modal formulation of the Linear Sampling Method. The impact of many parameters are analyzed, such as the numbers of sources/receivers and the distance between them, the time shape of the incident wave and the number and the values of the frequencies that are used. Some numerical experiments illustrate such analysis.

GeoSTARe is a proposed geosynchronous synthetic aperture radar (GEO SAR) mission as a hosted payload on a commercial communication satellite. GEO SAR missions have been studied for many years because of their potential for high temporal resolution imaging over continental regions. The large range means that longer signal integration times than for low Earth orbit SAR are required and/or large antennas. The GeoSTARe study includes an analysis of potential user applications for the atmospheric and surface data expected, assuming a mission concept of a broad, L-band, fixed beam plus a steerable X-band beam. A system design methodology is used to propose an outline mission design. This is then used to estimate performance accounting for expected surface backscatter in the different wavebands and the likely incidence angles. Results suggest that GeoSTARe is feasible with an antenna diameter of 6 m and electrical power demand of around 2 kW, and that practically all the identified user requirements can be met. Further work is underway to assess the effects of clutter, typical atmospheric perturbations, and image focussing algorithms.

Alguns parâmetros da atmosfera superior são calculados a partir de dados meteorológicos de superfície, coletados em alta resolução na cidade de Campina Grande, PB. Verificando-se, a cada dez minutos, os valores médios de temperatura, umidade e saldo de radiação, é possível ter-se uma idéia, nessa resolução temporal, de parâmetros importantes, tais como efeitos da cobertura de nuvem, altura da base das nuvens, energia potencial convectiva disponível e energia de inibição de convecção. Foi mostrado como a evolução desses parâmetros pode ser usada como ferramenta de análise meteorológica. A presença de nuvens altera bastante o valor dos parâmetros que podem ser estimados com dados de superfície.Some parameters of the upper atmosphere are calculated from high-resolution surface data collected in the city of Campina Grande, PB. By verifying ten-minute mean values of temperature, moisture and radiation budget, it is possible to have an idea, in this time resolution, of parameters like: cloud cover effects, cloud base height, convective available potential energy and convection inhibition energy. It is shown how the evolution of these parameters can be used as a meteorological analysis tool. The presence of clouds changes the values of the parameters that can be estimated from surface data.

This paper examines issues related to planning, programming and execution of machining operations by a robot in the context of machining large parts with complex geometries by a gantry-mounted robotic system. Parts were created from surface data in a CAD/CAM environment. The same environment was used to generate tool paths using a conventional machine tool approach. These paths were converted to robot trajectories and validated using mathematical kinematic models of the robotic system. Validation was performed according to various criteria related to process performance. Associated robot programs were then automatically generated. The manufacturing cell was progressively integrated according to requirements resulting from iterative process characterization. A metrology-based calibration procedure was designed that considerably improved the system's positioning precision. A custom user interface for online process monitoring was created as well as online cell program management capabilities to accommodate the large quantity of trajectory information. The system went from conceptual, to prototype-level to full production mode, thereby covering several Manufacturing Readiness Levels (MRL). The various strategies used in planning the process and programming the system demonstrate the viability of CAM-based approach for robot machining path generation without resorting to the use of OLP packages for validation of program generation. This could create a shift in the dependency on robotic specialists, allowing machine tool experts to carry about the programming of robot machining cells using the conventional machine tool approach.

The Harşit granitoid in northeastern Turkey, comprises four separate granite units that are apparently unfoliated. The Harşit granitoid was investigated here by using microstructural, petrofabric and anisotropy of magnetic susceptibility (AMS) data. The structural data of the granitoid were found to be highly compatible with the zonation recognized from AMS measurements. The orientation of magnetic fabrics within the granite units indicates that tectonic deformation might have occurred coevally with the magmatic emplacement of the intrusion. When we evaluated the manners on the scale of the pluton that the disruption took place in the form of uplifting, probably related to a rapid migration of the volcanic front and the documented change from deep sea sedimentation predominant until late Cretaceous to shallower environments during the Paleocene. The possible tectonic control of fault systems on the ascent and emplacement of the granitic magma in the study area, however, cannot be completely ruled out because the Harşit granitoid is situated at or very near the NAF systems in northern Anatolia. In any case, the intrusion is clearly discordant to the regional deformational features formed during the collisional events between the Eurasia and Anatolia plates in northern Turkey. © 2011 Springer Basel AG.

Bangladesh is one of the most climate vulnerable countries in the world. Situated in the delta of the Ganges, Brahmaputra, and Meghna (GBM) rivers, the country is exposed to a range of river and rainwater flood hazards due to climate variability, the timing, location, and extent of which depend on precipitation in the entire GBM basin. The Government of Bangladesh is fully committed to global climate-change advocacy and action, having already invested heavily in adaptation measures and policies. In recent decades, the government has invested more than US$10 billion to protect its population and assets in the floodplains. Given the uncertain magnitude and timing of the added risks from climate change, it is essential to identify the costs of climate proofing Bangladesh's critical infrastructure from intensified monsoon floods and cyclonic storm surges. Previously, few if any detailed studies have been developed on the costs of climate-proofing the country's infrastructure assets from inland monsoon floods and cyclones. Most analytical work to date has been confined to case studies, with relatively limited sets of locations, impacts, and adaptation measures. This study aims to fill that knowledge gap by providing detailed vulnerable population estimates and estimates of the incremental costs of asset adaptation out to the year 2050. It is part of a larger World Bank-supported study, entitled Economics of Adaptation to Climate Change (EACC), funded by the governments of the United Kingdom, the Netherlands, and Switzerland.

Alguns parâmetros da atmosfera superior são calculados a partir de dados meteorológicos de superfície, coletados em alta resolução na cidade de Campina Grande, PB. Verificando-se, a cada dez minutos, os valores médios de temperatura, umidade e saldo de radiação, é possível ter-se uma idéia, nessa resolução temporal, de parâmetros importantes, tais como efeitos da cobertura de nuvem, altura da base das nuvens, energia potencial convectiva disponível e energia de inibição de convecção. Foi mostrado como a evolução desses parâmetros pode ser usada como ferramenta de análise meteorológica. A presença de nuvens altera bastante o valor dos parâmetros que podem ser estimados com dados de superfície. Some parameters of the upper atmosphere are calculated from high-resolution surface data collected in the city of Campina Grande, PB. By verifying ten-minute mean values of temperature, moisture and radiation budget, it is possible to have an idea, in this time resolution, of parameters like: cloud cover effects, cloud base height, convective available potential energy and convection inhibition energy. It is shown how the evolution of these parameters can be used as a meteorological analysis tool. The presence of clouds changes the values of the parameters that can be estimated from surface data.

International audience; This paper presents a method of control developed in order to calculate the currents corresponding to the pressure measured at the level of the undisturbed sea surface in an oceanic domain Ω ⊂ IR3 and during a time T. We use a perturbation model suitable for tropical oceans, where the mean flow is known and its variability is of great interest for oceanography. The equations satisfied by the variability of the velocity and pressure (u,p) are of nonlinear Navier-Stokes type. The control is the variability of the wind-stress f which acts as the forcing of the perturbation. The cost function measures the distance between the observed and computed pressures on a part of the boundary. We prove, by means of minimizing sequences, the existence of an optimal control (u, f). In order to characterize it, we define a penalized control problem corresponding to a linearization of the direct problem satisfied by (u, p). Then the optimal penalized control is characterized by a set of linear equations: direct and adjoint problems, linked by an inequality. The set of equations and inequality characterizing the optimal control (u,f) is obtained as the limit of the penalization. The choice of the surface pressure as the observation makes the problem more difficult: it makes it necessary to use a mixed velocity-pressure formulation for the Navier-Stokes type problems and to satisfy regularity conditions in order to define the trace of the pressure on the boundary. Regularity p E L2(0,T,H1(Ω)) must be satisfied by all intermediate problems and preserved for the limit.

This paper is concerned with the development of a system to support Scientific Visualisation by utilising the sound channel. The mappings presented here are intended to support understanding and interaction with surface data. This is done by using sound signals associated with methods of analysis of surfaces to help the interpretation of surface shape, data, and classification. Several examples of sound mappings are presented and the implications for data interaction are discussed.