Machine-learned cloud classes from satellite data for process-oriented climate model evaluation

Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks towards more robust climate change projections. This study introduces a new machine-learning based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the MODIS instrument labelled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud\_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labelled satellite data for a more systematic evaluation of clouds in climate models.Comment: Main Paper 16 pages, 11 figures. Supporting material 7 Pages, 8 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Ensinar e aprender português: a digital resource for learning to read and write

In Education, the use of technological resources to support students' teaching and learning is becoming more and more urgent. Teaching and Learning Portuguese [Ensinar e Aprender Português – EAP] is a structured and innovative educational resource, supported by the use of Information and Communication Technologies and based on scientific evidence. It is aimed at students (and teachers) of the four years of primary school. It aims to: i) support the teaching/learning of reading and writing in primary school; ii) signalize, in a timely manner, students at-risk of presenting difficulties in learning to read and write; and iii) support the recovery of learning. This paper will present this digital resource developed for the Portuguese context, based on the Portuguese curriculum and the legislation on inclusive education. It is also anchored in the multilevel approach whose focus on digital transition, on screening tests and monitoring of learning has introduced new challenges in the education system. As it is a digital resource whose activities are self-executable and for which explanatory and corrective feedbacks are provided, it contributes to bridge a gap in terms of digital transition which was particularly visible in a pandemic context such as the one we are going through.In Education, the use of technological resources to support students' teaching and learning is becoming more and more urgent. Teaching and Learning Portuguese [Ensinar e Aprender Português – EAP] is a structured and innovative educational resource, supported by the use of Information and Communication Technologies and based on scientific evidence. It is aimed at students (and teachers) of the four years of primary school. It aims to: i) support the teaching/learning of reading and writing in primary school; ii) signalize, in a timely manner, students at-risk of presenting difficulties in learning to read and write; and iii) support the recovery of learning. This paper will present this digital resource developed for the Portuguese context, based on the Portuguese curriculum and the legislation on inclusive education. It is also anchored in the multilevel approach whose focus on digital transition, on screening tests and monitoring of learning has introduced new challenges in the education system. As it is a digital resource whose activities are self-executable and for which explanatory and corrective feedbacks are provided, it contributes to bridge a gap in terms of digital transition which was particularly visible in a pandemic context such as the one we are going through.This work was financially supported by Portuguese national funds through the FCT (Foundation for Science and Technology) within the framework of the CIEC (Research Center for Child Studies of the University of Minho) projects under the references UIDB/00317/2020 and UIDP/00317/2020

Design of an innovative learning experience for the final project of the building engineering degree

This essay presents the bases for a new teaching methodology for the Final Project of the Degree of Building Engineering. The aim of this methodology is to approach students to advanced forms of work in architectural and engineering offices by employing Building Information Modelling (BIM) technologies. This initiative has been funded within the Call 2011 for Teaching Research Incentives ofthe I Teaching Plan of the University of Seville. Following the guidelines of the European Higher Education Area, the learning experience designed has to enable the future Building Engineers to acquire specific and generic competences ascribed to the Final Project in the Verification Report of the Building Engineering Degree. The specific competence “E71. Presentation and defence before a university board of a final project, consisting of an integration exercise of the formative contents received and skills acquired through t he degree” is trained by the development of a building execution project with the use of BIM technologies. For a decade, architecture and engineering offices have increasingly been incorporating in their projects new tools for information processing in digital integrated systems, i.e. programs which allow the construction of building virtual models in three dimensions, and the identification of their constructive components, providing them with parametric dimensions. The operating capacity of BIM programs is stronger than that of 2D drawing programs, since they can manage and generate all the technical documentation in an integrated way. As far as the generic competences are concerned, the problem is that the Final Project has ascribed twenty four competences and their training and evaluation throughout a single term, which seems rather unattainable. In order to solve this matter, the four most important generic competences of Building Engineers have been identified according to their professional profile: “G01. Capacity for organization and planning”, “G06. Information management skills”, “G09. Ability to work in an interdisciplinary team” and “G13. Positive social attitude towards social and technological innovations”. The use of BIM technologies and collaborative work methodologies allow the training of these genericcompetences. Finally, assessment matrixes of the five competences involved have been established with the descriptors of the assessment indicators for each of their corresponding criteria at each level of student achievement. This study is limited to the design of the experience; its implementation could be carried out in the first term of the 2012/2013 academic year, provided the main pre-requisite are met by students, and command of BIM programs such as ALLPLAN, REVIT or ARCHICAD, is achieved. Aware of this challenge− since BIM programs are taught as optional subjects− a curricular line for students interested in participating in this experience is also proposed for the next academic yea

Bio-Ontologies: A Knowledge Representation Resource in Bioinformatics

Bioinformatics manages the information that has been gathered in databases since the advent of the molecular biology technological revolution. The successful research is based in interpretations of that information that can be accessed and managed computationally, which is a difficult task. An attempt to solve that problem is to use ontologies. Ontologies are computational formalisations of the knowledge about a given domain, allowing computers to manage the information in a semantic level. In medical informatics, ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. Bio-ontologies define the basic terms and relations in biological domains and are being used among others, as community reference, as the basis for interoperability between systems, and for search, integration and exchange of biological data. The most successful ontologies applied in Bioinformatics are the ones in the Open Biomedical Ontologies (OBO) project. At the same time, the Web Ontology Language (OWL) is a official proposal for ontologies implementation in the semantic web. In this article, we review the current position in bio-ontologies. We review this trend and what benefits it might bring to ontologies and their use within biomedicine

Analysis of the spatial and temporal variability of aerosol optical thickness and columnar water vapor from MERIS data

The SCAPE-M method has been developed for the retrieval of aerosol optical thickness (AOT), columnar water vapor (CWV) and surface reflectance from MERIS data. The retrieval of the three products is performed on a coupled manner taking profit of a common basis in which variations in surface elevation, topography and atmospheric state are considered pixel-by-pixel. More than 200 MERIS images have been processed by SCAPE-M. This results in a large data base of AOT, CWV and reflectance data to be validated and analysed. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed typical Root Mean Square Errors (RMSEs) of 0.05, an average correlation coefficient R2 of about 0.7-0.8. The R? values grow up to more than 0.9 in the case of CWV after comparison with the same stations. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from CHRIS-PROBA images and with ground-based measurements over water

Attrition in higher education: a new structural model

In this research, which is part of my PhD research into the influence of the use of social media in higher education, I have lessened the amount of variables, from Tinto’s integration theory. By including only the best-proven predictive variables, based on previous studies, I avoid the capitalization of chance and have built a more easy to use model for teachers and management. The latent variable ‘satisfaction’ is constructed by using just a fraction of the original manifest variables. The simplified model is tested using principal component analysis (PCA), to prove its fit. Furthermore, to better suit students’ contemporary society in the developed world, the model is enriched with the use of social media, in this case Facebook. The purpose of Facebook use (information, education, social and leisure) and the use of different pages amongst students were also measured with PCA. This provided a better insight in the integration/engagement components, which are also included in the new model. According by the measurements of Cronbach’s alpha and Guttman’s lambda-2, the new components showed internal consistency and reliability. In addition, SPSS AMOS was used for testing the fit of the model and showed reasonable values for the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA). This study will compare different background variables within the model to uncover the possible influences upon students’ attrition (and therefor also their success), engagement/satisfaction and social media use. Ultimately this paper will provide jet another piece to the puzzle for a better insight into the factors of students’ attrition and/or success

A method for the detection of solar-induced vegetation fluorescence frommeris FR data

A characteristic spectral emission is observed in vegetation chlorophyll under excitation by solar radiation. This emission, known as solar-induced chlorophyll fluorescence, occurs in the red and near infra-red spectral regions. In this paper a new methodology for the estimation of solar-induced chlorophyll fluorescence from spaceborne and airborne sensors is presented. The fluorescence signal is included in an atmospheric radiative transfer scheme so that chlorophyll fluorescence and surface reflectance are retrieved consistently from the measured at-sensor radiance. This methodology is tested on images acquired by the Medium Resolution Imaging Spectrometer (MERIS) on board the ENVIronmental SATellite (ENVISAT) taking advantage of its good characterization of the O2-A absorption band. Validation of MERIS-derived fluorescence is carried out by applying the method to data acquired by the Compact Airborne Spectrographic Imager (CASI-1500) sensor concurrently to MERIS acquisitions. CASI-derived fluorescence is in turn compared with ground-based fluorescence measurements. A correlation coefficient R2 of 0.85 is obtained

Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images.

Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, which is a typical example of a push-broom instrument exhibiting a relatively high noise component. The proposed correction method is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. A technique to exclude the contribution of the spatial high frequencies of the surface from the destriping process is introduced. First, the performance of the proposed algorithm is tested on a set of realistic synthetic images with added modeled noise in order to quantify the noise reduction and the noise estimation accuracy. Then, algorithm robustness is tested on more than 350 real CHRIS images from different sites, several acquisition modes (different spatial and spectral resolutions), and covering the full range of possible sensor temperatures. The proposed algorithm is benchmarked against the CHRIS reference algorithm. Results show excellent rejection of the noise pattern with respect to the original CHRIS images, especially improving the removal in those scenes with a natural high contrast. However, some low-frequency components still remain. In addition, the developed correction model captures and corrects the dependency of the noise patterns on sensor temperature, which confirms the robustness of the presented approach

Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images.

Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, which is a typical example of a push-broom instrument exhibiting a relatively high noise component. The proposed correction method is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. A technique to exclude the contribution of the spatial high frequencies of the surface from the destriping process is introduced. First, the performance of the proposed algorithm is tested on a set of realistic synthetic images with added modeled noise in order to quantify the noise reduction and the noise estimation accuracy. Then, algorithm robustness is tested on more than 350 real CHRIS images from different sites, several acquisition modes (different spatial and spectral resolutions), and covering the full range of possible sensor temperatures. The proposed algorithm is benchmarked against the CHRIS reference algorithm. Results show excellent rejection of the noise pattern with respect to the original CHRIS images, especially improving the removal in those scenes with a natural high contrast. However, some low-frequency components still remain. In addition, the developed correction model captures and corrects the dependency of the noise patterns on sensor temperature, which confirms the robustness of the presented approach