Mean radiant temperature from global-scale numerical weather prediction models

In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun’s position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies

A System for 3D Shape Estimation and Texture Extraction via Structured Light

Shape estimation is a crucial problem in the fields of computer vision, robotics and engineering. This thesis explores a shape from structured light (SFSL) approach using a pyramidal laser projector, and the application of texture extraction. The specific SFSL system is chosen for its hardware simplicity, and efficient software. The shape estimation system is capable of estimating the 3D shape of both static and dynamic objects by relying on a fixed pattern. In order to eliminate the need for precision hardware alignment and to remove human error, novel calibration schemes were developed. In addition, selecting appropriate system geometry reduces the typical correspondence problem to that of a labeling problem. Simulations and experiments verify the effectiveness of the built system. Finally, we perform texture extraction by interpolating and resampling sparse range estimates, and subsequently flattening the 3D triangulated graph into a 2D triangulated graph via graph and manifold methods

Point-set manifold processing for computational mechanics: thin shells, reduced order modeling, cell motility and molecular conformations

In many applications, one would like to perform calculations on smooth manifolds of dimension d embedded in a high-dimensional space of dimension D. Often, a continuous description of such manifold is not known, and instead it is sampled by a set of scattered points in high dimensions. This poses a serious challenge. In this thesis, we approximate the point-set manifold as an overlapping set of smooth parametric descriptions, whose geometric structure is revealed by statistical learning methods, and then parametrized by meshfree methods. This approach avoids any global parameterization, and hence is applicable to manifolds of any genus and complex geometry. It combines four ingredients: (1) partitioning of the point set into subregions of trivial topology, (2) the automatic detection of the local geometric structure of the manifold by nonlinear dimensionality reduction techniques, (3) the local parameterization of the manifold using smooth meshfree (here local maximum-entropy) approximants, and (4) patching together the local representations by means of a partition of unity. In this thesis we show the generality, flexibility, and accuracy of the method in four different problems. First, we exercise it in the context of Kirchhoff-Love thin shells, (d=2, D=3). We test our methodology against classical linear and non linear benchmarks in thin-shell analysis, and highlight its ability to handle point-set surfaces of complex topology and geometry. We then tackle problems of much higher dimensionality. We perform reduced order modeling in the context of finite deformation elastodynamics, considering a nonlinear reduced configuration space, in contrast with classical linear approaches based on Principal Component Analysis (d=2, D=10000's). We further quantitatively unveil the geometric structure of the motility strategy of a family of micro-organisms called Euglenids from experimental videos (d=1, D~30000's). Finally, in the context of enhanced sampling in molecular dynamics, we automatically construct collective variables for the molecular conformational dynamics (d=1...6, D~30,1000's)

Cuckoo Search Algorithm with Lévy Flights for Global-Support Parametric Surface Approximation in Reverse Engineering

This paper concerns several important topics of the Symmetry journal, namely, computer-aided design, computational geometry, computer graphics, visualization, and pattern recognition. We also take advantage of the symmetric structure of the tensor-product surfaces, where the parametric variables u and v play a symmetric role in shape reconstruction. In this paper we address the general problem of global-support parametric surface approximation from clouds of data points for reverse engineering applications. Given a set of measured data points, the approximation is formulated as a nonlinear continuous least-squares optimization problem. Then, a recent metaheuristics called Cuckoo Search Algorithm (CSA) is applied to compute all relevant free variables of this minimization problem (namely, the data parameters and the surface poles). The method includes the iterative generation of new solutions by using the Lévy flights to promote the diversity of solutions and prevent stagnation. A critical advantage of this method is its simplicity: the CSA requires only two parameters, many fewer than any other metaheuristic approach, so the parameter tuning becomes a very easy task. The method is also simple to understand and easy to implement. Our approach has been applied to a benchmark of three illustrative sets of noisy data points corresponding to surfaces exhibiting several challenging features. Our experimental results show that the method performs very well even for the cases of noisy and unorganized data points. Therefore, the method can be directly used for real-world applications for reverse engineering without further pre/post-processing. Comparative work with the most classical mathematical techniques for this problem as well as a recent modification of the CSA called Improved CSA (ICSA) is also reported. Two nonparametric statistical tests show that our method outperforms the classical mathematical techniques and provides equivalent results to ICSA for all instances in our benchmark.This research work has received funding from the project PDE-GIR (Partial Differential Equations for Geometric modelling, Image processing, and shape Reconstruction) of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No. 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program) under Grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds FEDER (AEI/FEDER, UE), and the project #JU12, jointly supported by public body SODERCAN of the Regional Government of Cantabria and European Funds FEDER (SODERCAN/FEDER UE). We also thank Toho University, Nihon University, and the Symmetry 2018, 10, 58 23 of 25 University of Cantabria for their support to conduct this research wor

Evaluation of the Radiation Scheme of a Numerical Weather Prediction Model by Airborne Measurements of Spectral Irradiance above Clouds.

In this thesis a novel approach to compare airborne observations of spectral upward and downward irradiances with along-track radiative transfer simulations (RTS) are presented. The RTS are performed with the ecRad radiation scheme of the Integrated Forecast System (IFS) operated by the European Centre for Medium Range Weather Forecast (ECMWF) and the library for Radiative transfer (libRadtran) on basis of hourly 0.1° IFS analysis data (IFS AD). The comparison aims to investigate the general capability of the utilized models to reproduce the observed radiation field. Simultaneous utilization of ecRad and libRadtran, driven by the same IFS AD, and comparison with observations enables to separate for potential errors in the applied IFS AD and ecRad

Amélioration de la paramétrisation des propriétés optiques des nuages d'eau liquide dans le spectre solaire

Représenter correctement l'impact radiatif des nuages est un vrai défi pour les modèles atmosphériques, du fait que les interactions rayonnement-nuages sont contrôlées par les propriétés optiques des particules nuageuses. Ces propriétés dépendent de la taille des particules, et de la longueur d'onde du rayonnement, deux éléments qui ne sont pas bien résolus dans les modèles atmosphériques, si bien que les propriétés optiques doivent être paramétrisées. Dans ce manuscrit nous nous efforçons de quantifier les incertitudes sur l'impact radiatif des nuages dans le spectre solaire (SW) liées à la paramétrisation des propriétés optiques des nuages liquides. Les incertitudes proviennent en premier lieu de l'hypothèse faite sur la forme de la distribution de taille des gouttelettes (DSD), qui intervient dans: 1- l'estimation du rayon effectif des gouttelettes (reff) à partir du contenu en eau (LWC) et de la concentration en nombre des gouttelettes (N); 2- le calcul des propriétés de diffusion simple (SSPs) à partir de reff. Des incertitudes sont également liées au moyennage spectral nécessaire pour calculer les SSPs sur des bandes larges. Pour rendre compte de ces incertitudes, un nouveau jeu de paramétrisations des SSPs est développé et implémenté dans le code radiatif ecRad, couvrant un grand nombre de formes de DSD et de méthodes de moyennage spectral. Cette version améliorée d'ecRad est utilisée pour simuler les propriétés radiatives (transmittance, réflectance, aborbance) d'une grande variété de nuages définis en termes de LWC et N, comprenant un nuage homogène idéalisé, des cas d'étude plus réalistes, et des sorties d'un modèle de climat. Ces simulations montrent que la transmittance/réflectance d'un nuage peut varier de 20% en changeant simplement la forme de la DSD. Des différences de l'ordre de 20% sont également obtenues pour les taux de chauffage atmosphérique. L'impact de la forme de la DSD sur l'estimation de reff contribue pour 80% à l'incertitude totale, le reste étant lié à l'impact sur les SSPs. Le moyennage spectral a moins d'influence, si ce n'est sur l'absorption au sein du nuage. A l'échelle globale nous estimons que le forçage radiatif des nuages peut varier de 6~W~m$^{-2}$ selon la forme de DSD supposée, ce qui correspond à environ 13% du forçage radiatif SW des nuages. Afin de compléter ces simulations de transfert radiatif, et d'étudier comment des différences de forçage radiatif se répercutent sur l'évolution des nuages, la version améliorée d'ecRad a été implémentée dans le modèle atmosphérique Méso-NH. Par ailleurs, la forme de la DSD utilisée dans le code radiatif est rendue cohérente avec celle supposée dans le schéma microphysique à deux moments de Méso-NH, LIMA. Des simulations 1D de stratocumulus sont réalisées en supposant différentes formes de DSD, à la fois dans LIMA et pour l'estimation de reff et des SSPs. L'impact direct de la DSD sur le forçage radiatif est évalué, et les effets indirects qui résulte des rétroactions du rayonnement sur les autres caractéristiques physiques sont également abordées. Dans ces simulations interactives, l'estimation de reff reste la principale source des différences, et les effets directs obtenus sont en accord avec les simulations hors-ligne. Au cours de la simulation les différences de flux radiatifs et de taux de réchauffement modifient progressivement les profils verticaux de température, de LWC et de N, ce qui renforce les différences liées à $r_mathrm{eff}$, puisqu'il dépend de ces quantités. Cette étude de cas souligne la complexité des interactions nuage-rayonnement, dont les processus physiques sous-jacents mériteraient d'être étudiés plus en détail. Finalement, ces simulations Méso-NH mettent en évidence que la sensibilité aux propriétés optiques des nuages dans le LW, qui devraient à l'avenir être traitées avec autant d'attention que dans le SW.Simulating the radiative impact of clouds is challenging for atmospheric models, because cloud-radiation interactions are driven by the optical properties of individual cloud particles. These properties depend on the size of the particle and the frequency of light, two quantities not fully resolved in atmospheric models, implying that cloud optical properties need to be parameterized. In this thesis we focus on quantifying the uncertainties in shortwave (SW) cloud radiative impact due to the SW optical properties parameterization of liquid clouds. Uncertainties are first due to the Droplet Size Distribution (DSD) shape assumption required in two steps: 1- to estimate the cloud droplets effective radius (reff) from liquid water content (LWC) and droplet number concentration (N); 2- to compute the single scattering properties (SSPs) as a function of reff. Uncertainties also arise from averaging SSPs over wide spectral bands. To assess these uncertainties, a set of new parameterizations corresponding to various DSD shapes and spectral averaging methods are designed and implemented in the radiative code ecRad. Using this updated version of ecRad, we perform offline simulations to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the transmittance/reflectance of the cloud can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for atmospheric heating rates. The impact of the DSD shape assumption on reff (resp. SSPs) estimation contributes to around 80% (resp. 20%) of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6~W~m$^{-2}$ depending on the assumed DSD shape, which is about 13% of the best observational estimate. To complement these offline simulations and investigate how differences in radiative forcing feed back on cloud evolution, the updated version of ecRad is implemented in the atmospheric model Meso-NH. In addition, the DSD shape assumed in ecRad is made consistent with the DSD shape assumed in the 2-moment microphysical scheme of Meso-NH, LIMA. 1D simulations of a stratocumulus cloud are performed with various DSD shapes affecting simultaneously LIMA, the reff estimation and the SSPs parameterization. The direct impact of the DSD on the simulated radiative forcing is assessed, and the indirect effects that results from interactions of radiation with other components of the model are discussed as well. In these interactive simulations, the estimation of reff remains the main source of differences, and the obtained direct effects are in line with the offline simulations. Throughout the simulation, the differences in radiative fluxes and heating rates progressively impact the vertical profiles of temperature, LWC and N, enhancing the feedback since reff depends on these two quantities.This case study highlights the complexity of the cloud-radiation interactions, which deserve further investigation to fully understand the primary physical mechanisms at stake. Finally, these Meso-NH simulations point out the sensitivity to the LW cloud properties, that should in the future be treated as carefully as the SW

Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals