Comparison of spatial downscaling methods of general circulation model results to study climate variability during the last glacial maximum

The extent to which climate conditions influenced the spatial distribution of hominin populations in the past is highly debated. General circulation models (GCMs) and archaeological data have been used to address this issue. Most GCMs are not currently capable of simulating past surface climate conditions with sufficiently detailed spatial resolution to distinguish areas of potential hominin habitat, however. In this paper, we propose a statistical downscaling method (SDM) for increasing the resolution of climate model outputs in a computationally efficient way. Our method uses a generalised additive model (GAM), calibrated over present-day climatology data, to statistically downscale temperature and precipitation time series from the outputs of a GCM simulating the climate of the Last Glacial Maximum (19 000–23 000 BP) over western Europe. Once the SDM is calibrated, we first interpolate the coarse-scale GCM outputs to the final resolution and then use the GAM to compute surface air temperature and precipitation levels using these interpolated GCM outputs and fine-resolution geographical variables such as topography and distance from an ocean. The GAM acts as a transfer function, capturing non-linear relationships between variables at different spatial scales and correcting for the GCM biases. We tested three different techniques for the first interpolation of GCM output: bilinear, bicubic and kriging. The resulting SDMs were evaluated by comparing downscaled temperature and precipitation at local sites with paleoclimate reconstructions based on paleoclimate archives (archaeozoological and palynological data) and the impact of the interpolation technique on patterns of variability was explored. The SDM based on kriging interpolation, providing the best accuracy, was then validated on present-day data outside of the calibration period. Our results show that the downscaled temperature and precipitation values are in good agreement with paleoclimate reconstructions at local sites, and that our method for producing fine-grained paleoclimate simulations is therefore suitable for conducting paleo-anthropological research. It is nonetheless important to calibrate the GAM on a range of data encompassing the data to be downscaled. Otherwise, the SDM is likely to overcorrect the coarse-grain data. In addition, the bilinear and bicubic interpolation techniques were shown to distort either the temporal variability or the values of the response variables, while the kriging method offered the best compromise. Since climate variability is an aspect of the environment to which human populations may have responded in the past, the choice of interpolation technique is therefore an important consideration.</p

Solving the Direction Field for Discrete Agent Motion

Models for pedestrian dynamics are often based on microscopic approaches allowing for individual agent navigation. To reach a given destination, the agent has to consider environmental obstacles. We propose a direction field calculated on a regular grid with a Moore neighborhood, where obstacles are represented by occupied cells. Our developed algorithm exactly reproduces the shortest path with regard to the Euclidean metric.Comment: 8 pages, 4 figure

Multiple detection using the eigenvalues of the spectral matrix

In this study we treat the problem of detecting from multidimensional data, the number of uncorrelated signais in passive array treatment as it is the case in underwater acoustics, array processing and seismology . We use four detection criteria. Some of them are known, like AIC and MDL criteria where direct Kullback's divergence is the information measure; we prolong them using the inverse Kullback's divergence. We also adapt a new criterion using the logarithm of the likelihood ratio that has a chi square distribution and we suggest a simplified threshold criterion that uses the eigenvalues of the spectral matrix of the data . We study and compare the performances of these criteria in realistic simulations . The first one is inspired by the problems of array processing and the second one by seismic problems. Finally we study the robustness of these criteria when the classical hypothesis of uncorrelated noises having equal variances is not fulfilled . Thus we outline some application limits of these criteria .Critères de détection, résultats sur des simulation

Improvement of passive array treatment by estimation of the spectral matrix of noises

Array processing aims to characterize impinging sources front recorded data ; a model of the noise spectral matrix is necessary for the treatment . One usually suppose either that this matrix is known or that the noises are uncorrelated and have equal variances on each sensor. We present here an algorithm to estimate the noise spectral matrix when the noises are uncorrelated and have différent variances on each sensor. It needs technics of the principal components analysis; thus it uses the eigensystem of the spectral matrix of the received signais (the number of impinging signais is assumed known) . We show on simulations that, if the spectral matrix of the noises is estimated with this algorithm, the following array processing treatments give improved results .Présentation d'une méthode pour estimer la matrice spectrale des bruits lorsqu'ils sont non corrélés et ont des puissances différentes sur les capteurs. Utilisation des techniques d'analyse en composantes principales et donc des éléments propres de la matrice spectrale des signaux reçus. Simulations justifiant l'emploi de cet algorithm

Decomposition-based mission planning for fixed-wing UAVs surveying in wind

This paper presents a new method for planning fixed-wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back-and-forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top-down recursive greedy approach is used to traverse the search space in order to minimise flight time of the survey. This optimisation can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offer greatly reduced survey times in wind. Other improved cost functions have been developed to take into account real world problems, e.g. No Fly Zones, in addition to flight time. A number of real surveys are performed in order to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real-world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multi-rotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of a Unmanned Aerial Vehicle (UAV) aerial survey.</div

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Localization of correlated sources by array processing using spatial smoothing

In this paper, the classical array processing methods are separated in two classes : uncoupled solutions and global solutions . We expose the method that uses the spatial smooting to decorrelate the received signais . Then we apply these array processing methods to signais that are recorded in an underwater acoustics experiment ; in this situation the spatial smoothing is compulsary . Results are discussed .Dans cet article, nous regroupons les diverses méthodes connues de traitement d'antenne en deux catégories : méthodes découplées, méthodes globales . Nous présentons la méthode du lissage spatial qui permet de décorréler les sources à la réception . Nous appliquons ensuite ces méthodes de traitement d'antenne à des signaux enregistrés au cours d'une expérimentation en acoustique sous-marine dans laquelle une onde monochromatique a été émise dans différentes configurations géométriques et météorologiques . Dans cette situation, le lissage spatial doit être utilisé pour décorréler les trajets multiples

Multi-site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species

1. Generalised dissimilarity modelling (GDM) applies pairwise beta diversity as a measure of species turnover with the purpose of explaining changes in species composition under changing environments or along environmental gradients. Beta diversity only captures turnover across pairs of sites and, therefore, disproportionately represents turnover in rare species across communities. By contrast, zeta diversity, the average number of shared species across multiple sites, captures the full spectrum of rare, intermediate and widespread species as they contribute differently to compositional turnover. 2. We show how integrating zeta diversity into GDMs (which we term multi-site generalised dissimilarity modelling, MS-GDM), provides a more information rich approach to modelling how communities respond to environmental variation and change. We demonstrate the value of including zeta diversity in biodiversity assessment and modelling using BirdLife Australia Atlas data. Zeta diversity values for different numbers of sites (the order of zeta) are regressed against environmental differences and distance using two kinds of regressions: shape constrained additive models and a combination of I-splines and generalised linear models. 3. Applying MS-GDM to different orders of zeta revealed shifts in the importance of environmental variables in explaining species turnover, varying with the order of zeta and thus with the level of co-occurrence of the species and, by extension, their commonness and rarity. In particular, precipitation gradients emerged as drivers in the turnover of rare species, whereas temperature gradients were more important drivers of turnover in widespread species. 4. Appreciation of the factors that drive compositional turnover across multiple sites is necessary for accommodating the full spectrum of compositional turnover across rare to common species. This extends beyond understanding drivers for pairwise beta diversity only. MS-GDM provides a valuable addition to the toolkit of GDM, with further potential for survey gap analysis and prediction of species composition in unsampled sites

The minimum energy expenditure shortest path method

This article discusses the addition of an energy parameter to the shortest path execution process; namely, the energy expenditure by a character during execution of the path. Given a simple environment in which a character has the ability to perform actions related to locomotion, such as walking and stair stepping, current techniques execute the shortest path based on the length of the extracted root trajectory. However, actual humans acting in constrained environments do not plan only according to shortest path criterion, they conceptually measure the path that minimizes the amount of energy expenditure. On this basis, it seems that virtual characters should also execute their paths according to the minimization of actual energy expenditure as well. In this article, a simple method that uses a formula for computing vanadium dioxide ($VO_2$) levels, which is a proxy for the energy expenditure by humans during various activities, is presented. The presented solution could be beneficial in any situation requiring a sophisticated perspective of the path-execution process. Moreover, it can be implemented in almost every path-planning method that has the ability to measure stepping actions or other actions of a virtual character