Identification of dust outbreaks on infrared msg-seviri data by using a Robust Satellite Technique (RST)

Dust storms are meteorological phenomena of great interest for scientific community because of their potential impact on climate changes, for the risk that may pose to human health and due to other issues as desertification processes and reduction of the agricultural production. Satellite remote sensing, thanks to global coverage, high frequency of observation and low cost data, may highly contribute in monitoring these phenomena, provided that proper detection methods are used. In this work, the known Robust Satellite Techniques (RST) multitemporal approach, used for studying and monitoring several natural/environmental hazards, is tested on some important dust events affecting Mediterranean region in May 2004 and Arabian Peninsula in February 2008. To perform this study, data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) have been processed, comparing the generated dust maps to some independent satellite-based aerosol products. Outcomes of this work show that the RST technique can be profitably used for detecting dust outbreaks from space, providing information also about areas characterized by a different probability of dust presence. They encourage further improvements of this technique in view of its possible implementation in the framework of operational warning systems

Data integration and FAIR data management in Solid Earth Science

Integrated use of multidisciplinary data is nowadays a recognized trend in scientific research, in particular in the domain of solid Earth science where the understanding of a physical process is improved and made complete by different types of measurements – for instance, ground acceleration, SAR imaging, crustal deformation – describing a physical phenomenon. FAIR principles are recognized as a means to foster data integration by providing a common set of criteria for building data stewardship systems for Open Science. However, the implementation of FAIR principles raises issues along dimensions like governance and legal beyond, of course, the technical one. In the latter, in particular, the development of FAIR data provision systems is often delegated to Research Infrastructures or data providers, with support in terms of metrics and best practices offered by cluster projects or dedicated initiatives. In the current work, we describe the approach to FAIR data management in the European Plate Observing System (EPOS), a distributed research infrastructure in the solid Earth science domain that includes more than 250 individual research infrastructures across 25 countries in Europe. We focus in particular on the technical aspects, but including also governance, policies and organizational elements, by describing the architecture of the EPOS delivery framework both from the organizational and technical point of view and by outlining the key principles used in the technical design. We describe how a combination of approaches, namely rich metadata and service-based systems design, are required to achieve data integration. We show the system architecture and the basic features of the EPOS data portal, that integrates data from more than 220 services in a FAIR way. The construction of such a portal was driven by the EPOS FAIR data management approach, that by defining a clear roadmap for compliance with the FAIR principles, produced a number of best practices and technical approaches for complying with the FAIR principles. Such a work, that spans over a decade but concentrates the key efforts in the last 5 years with the EPOS Implementation Phase project and the establishment of EPOS-ERIC, was carried out in synergy with other EU initiatives dealing with FAIR data. On the basis of the EPOS experience, future directions are outlined, emphasizing the need to provide i) FAIR reference architectures that can ease data practitioners and engineers from the domain communities to adopt FAIR principles and build FAIR data systems; ii) a FAIR data management framework addressing FAIR through the entire data lifecycle, including reproducibility and provenance; and iii) the extension of the FAIR principles to policies and governance dimensions.publishedVersio

RST analysis of MSG-SEVIRI TIR radiances at the time of the Abruzzo 6 April 2009 earthquake

Space-time fluctuations of Earth's emitted Thermal Infrared (TIR) radiation have been observed from satellite months to weeks before earthquakes occurrence. The general RST approach has been proposed in order to discriminate normal (i.e. related to the change of natural factor and/or observation conditions) TIR signal fluctuations from anomalous signal transient possibly associated to earthquake occurrence. In this work RST approach is applied to the Abruzzo 6 April 2009 event (M(L)=5.8) by using for the first time MSG-SEVIRI (Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imager) thermal infrared observations. A validation/confutation analysis has been performed in order to verify the presence/absence of anomalous space-time TIR transients in the presence/absence of significant seismic activity. March-April 2009 has been analyzed for validation purposes. Relatively unperturbed periods (no earthquakes with M(L)>= 5) have been taken for confutation. A specific TIR anomalies space-time persistence analysis as well as a cloud coverage distribution test have been introduced in order to eliminate artifacts and outliers both in the validation and confutation phases. Preliminary results show clear differences in TIR anomalies occurrence during the periods used for validation and confutation purposes. Quite clear TIR anomalies appear also to mark main tectonic lines related to the preparatory phases of others, low magnitude (M(L)similar to 4) earthquakes, occurred in the area

A rst-based cloud mask for fire-related applications

Satellite-based algorithms for fire detection and monitoring are generally applied after a preliminary phase of cloud-affected pixel identification in order to process only clear sky pixels. Performances of cloud masks usually available for satellite data are generally not suitable in fire-related applications because such products have been formerly developed for meteorological and/or climatological purposes. A not suitable cloud mask may be so responsible for omission errors, excluding cloudy contaminated pixels from further analysis, not only in case of opaque clouds, but also in the presence of semi-transparent clouds which, indeed, could permit a signal affected by fires to reach a satellite sensor. Conversely, if a cloud mask let reflective clouds out, false positives may be detected by a fire detection algorithm, due to their effect in the medium infrared (MIR) band. Since the “2nd Workshop on Geostationary Fire Monitoring and Applications”, the importance of a cloud mask tailored to fire-related applications has been clearly highlighted and our experience gained during several real time validation campaigns of the RST-FIRES algorithm (Robust Satellite Technique for Fire detection) confirmed that. In particular, in the first implementation of RST-FIRES on MSGSEVIRI data, the algorithm was applied only to pixels not declared as “cloudy” by the EUMETSAT CLM product. Unfortunately, CLM product showed to be not suitable for fire applications mainly because slipped off reflective clouds. In order to increase the reliability of the cloud detection phase, CLM product was combined with the RST-based OCA (One-channel Cloud-detection Approach) algorithm, only applied to two channels (one in the visible and the other one in the thermal infrared) so that it was indicated as OCA VIS-TIR. The higher reliability of this combined cloud detection scheme, as compared with the exclusive use of CLM product, showed to minimize false positives, while increasing omission errors because additional smoky pixels were flagged as “cloudy” and events under transparent clouds were undetected. This led us to develop a multispectral RST-based cloud detection scheme specifically tailored for fire-related applications. It was developed for discriminating spectral characteristics of different types of clouds, smoke, and clear-sky pixels following the heritage of the RST-based OCA VIS-TIR algorithm. The new cloud mask, named OCA MULTI-SPECTRAL, was preliminarily tested in the case of fire-affected pixels which, despite a strong MIR signal, were not detected because declared “cloudy” by the present scheme of cloud detection within the RST-FIRES system, based, as before mentioned, on the combination of EUMETSAT CLM product and OCA VISTIR. Performances of OCA MULTI-SPECTRAL have been also evaluated in comparison with the ones of the present cloud detection scheme. Some examples will be shown and discussed in this paper

Establishing Core Concepts for Information-Powered Collaborations

Science benefits tremendously from mutual exchanges of information and pooling of effort and resources. The combination of different skills and diverse knowledge is a powerful capacity, source of new intuitions and creative insights. Therefore multidisciplinary approaches can be a great opportunity to explore novel scientific horizons. Collaboration is not only an opportunity, it is essential when tackling today’s global challenges by exploiting our fast growing wealth of data. In this paper we introduce the concept of Information-Powered Collaborations (IPC) — an abstraction that captures those requirements and opportunities. We propose a conceptual framework that partitions the inherent complexity of such dynamic environments and offers concrete tools and methods to thrive in the data revolution era. Such a framework promotes and enables information sharing from multiple heterogeneous sources that are independently managed. We present the results of assessing our approach as an IPC for solid-Earth sciences: the European Plate Observing System (EPOS)

Reducing atmospheric noise in RST analysis of TIR satellite radiances for earthquakes prone areas satellite monitoring

Space-time fluctuations of the Earth’s emitted Thermal Infrared (TIR) radiation observed from satellite from months to weeks before an earthquake are reported in several studies. Among the others, a Robust Satellite data analysis Technique (RST) was proposed (and applied to different satellite sensors in various geo-tectonic contexts) to discriminate anomalous signal transients possibly associated with earthquake occurrence from normal TIR signal fluctuations due to other possible causes (e.g. solar diurnal-annual cycle, meteorological conditions, changes in observational conditions, etc.). Variations in satellite view angle depending on satellite’s passages (for polar satellites) and atmospheric water vapour fluctuations were recognized in the past as the main factors affecting the residual signal variability reducing the overall Signal-to-Noise (S/N) ratio and the potential of the RST-based approach in identifying seismically related thermal anomalies. In this paper we focus on both factors for the first time, applying the RST approach to geostationary satellites (which guarantees stable view angles) and using Land Surface Temperature (LST) data products (which are less affected by atmospheric water vapour variability) instead of just TIR radiances at the sensor. The first results, obtained in the case of the Abruzzo earthquake (6 April 2009, MW∼6.3) by analyzing 6 years of SEVIRI (Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat Second Generation satellite) LST products provided by EUMETSAT, seem to confirm the major sensitivity of the proposed approach in detecting perturbations of the Earth’s thermal emission a few days before the main shock. The results achieved in terms of increased S/N ratio (in validation) and reduced “false alarms” rate (in confutation) are discussed comparing results obtained by applying RST to LST products with those achieved by applying an identical RST analysis (using the same MSG-SEVIRI 2005-2010 data-set) to the simple TIR radiances at the sensor