8 research outputs found

    General Comparison of FY-4A/AGRI With Other GEO/LEO Instruments and Its Potential and Challenges in Non-meteorological Applications

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    Meteorological satellites have become an indispensable tool for weather and land observation. Traditionally, geostationary (GEO) satellites have been used in operational meteorological services due to their high temporal resolution, while polar-orbiting satellites, with their high spatial resolution, are applied more to monitor environmental change and natural disasters. The development of China’s next-generation geostationary meteorological satellites (the FY-4 series) represents an exciting expansion of Chinese non-meteorological remote sensing capabilities. The first satellite (FY-4A) of the FY-4 series was launched on 11 December 2016. The Advanced Geosynchronous Radiation Imager (AGRI) on board FY-4A has 14 spectral bands (increased from the 5 bands of FY-2) that are quantized with 12 bits per pixel (up from 10 bits for FY-2) and sampled at 1 km at nadir in the visible (VIS), 2 km in the near-infrared (NIR), and 4 km in the remaining IR spectral bands (compared with 1.25 km for VIS, no NIR, and 5 km for IR of FY-2). In later satellites in the FY-4A series, the AGRI channel number will be gradually increased from 14 to 18 with IR spatial resolution of 2 km, and the full-disk temporal resolution will be enhanced from 15 to 5 min. With their improved spectral, spatial, and temporal resolution properties, the FY-4 series will gradually approach low earth orbiting (LEO) sensors in spatial and spectral resolution, which will offer greater opportunity and capability for observing small objects and rapid changes in land, ocean, and atmosphere. This review paper provides an introduction to the Chinese FY-4 observation capabilities, a comparison of FY-4 with other new-generation GEO and LEO weather satellites, and associated non-meteorological applications. A series of typical examples based on recent and on-going operational work in National Satellite Meteorological Center of China Meteorological Administration (NSMC/CMA) that use FY-4A data for non-meteorological applications are demonstrated and discussed, including (i) aerosol monitoring, (ii) dust monitoring, (iii) volcanic ash detection and aviation applications, (iv) fire detection and dynamical evaluation, (v) water body detection, and (vi) floating algae monitoring. The paper concludes with a synthesis of these application areas and the challenges that CMA has to address for future research, technological innovation, and in-depth applications

    Assessing the social impacts of extreme weather events using social media

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    The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC

    Groß-skalige 2D-hydraulische Modellierung: Verbesserung der Analyse der Flutdynamik mit remote sensing und freien geographischen Informationen

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    This work investigates the integration of hydro-geomorphic models, traditional data (static stage gages) and novel data sources, such as remotely sensed images and Crowdsourced data (Volunteering Geographic Information or VGI), for observation-driven improvements of hydro-modelling tools. The Tiber river basin, was selected as case study with a focus domain on the approximately 120 km channel upstream of Rome for its strategic importance in the protection of the historical city centre and the coastal urbanized zone. A parsimonious hydrological modelling algorithm was implemented, calibrated and validated for calculating the flow hydrographs of the ungauged small basins contributing to the study area. Furthermore, to delineate the boundaries computational domain of the hydraulic model for the Data Assimilation application, a DEM-based hydro-geomorphic floodplain delineation algorithm adapted from literature was tested with different DEMs and considering also its parametrization varying the stream orders. The adopted DA methodology is the Ensemble Kalman Filter (EnKF) that requires multiple simulations for representing the uncertainties related to the model and the observations errors. New approaches were proposed for integrating, as observations in the DA method, traditional static sensors, and simultaneously remotely sensed images and VGI data. Despite the static sensor have already been adopted in literature as observations in a DA framework, some new technical measures were necessary for integrating them in Quasi-2D hydraulic model. The assimilation of satellite images resulted to be effective, since the whole computational domain is interested by the water levels correction, although the improvement of the model performance persisted for only some hours of simulation. The usefulness of VGI have been investigated considering the uncertainties related to their reliability mostly in terms of accuracy and time allocation. Results show the potential of new data for improving the performance of the flood model, partially overcoming the limitations and the potential scarce availability of the traditional sensors. Finally, the simultaneous integration of all the three types of observations gave promising results, improving the performance of the model compared to the ones obtained assimilating only Satellite images or VGI observations.Diese Arbeit untersucht die Integration von hydro-geomorphen Modellen, traditionellen Daten (statische Stufenpegeln) und neuartigen Datenquellen wie Remote-Sensing-Bildern und Crowdsourced-Daten (volunteering Geographic Information oder VGI), um beobachtungsorientierte Verbesserungen von Hydromodellierungswerkzeugen zu erreichen. Das Tiber-Flusseinzugsgebiet wurde als Fallstudie mit einem Schwerpunkt auf dem etwa 120 km stromaufwärts von Rom gelegenen Kanal ausgewählt und zwar wegen seiner strategischen Bedeutung für den Schutz des historischen Stadtzentrums und der urbanisierten Küstenregion. Ein sparsamer hydrologischer Modellierungsalgorithmus wurde implementiert, kalibriert und validiert, um die Fluss-Hydrographen der durch Pegel nicht erfassten kleinen Becken zu berechnen, die zum Untersuchungsgebiet beitragen. Um die Grenzen des rechnerischen Bereichs des Hydraulikmodells für die Data-Assimilation-Anwendung abzugrenzen, wurde außerdem ein DEM-basierter, aus der Literatur angepasster Algorithmus zur Abgrenzung von hydrogeomorphen Flutebenen mit verschiedenen DEMs getestet, wobei auch die Parametrisierung der Stream-Reihenfolge berücksichtigt wurde. Die angenommene DA-Methode ist der Ensemble Kalman Filter (EnKF), der mehrere Simulationen zur Darstellung der mit dem Modell verbundenen Unsicherheiten und Beobachtungsfehler erfordert. Es wurden neue Ansätze für die Integration herkömmlicher statischer Sensoren, von Fernerkundungs-Bildern und von VGI-Daten als Beobachtungen für das DA-Verfahren vorgeschlagen. Obwohl die statischen Sensoren bereits in der Literatur als Beobachtungen in einem DA-Rahmen verwendet wurden, waren einige technische Maßnahmen erforderlich, um sie in das Quasi-2D-Hydraulikmodell zu integrieren. Die Assimilation von Satellitenbildern erwies sich als effektiv, da der gesamte rechnerische Bereich an der Korrektur des Wasserstandes interessiert ist; die Verbesserung der Modellleistung dauerte allerdings nur einige Stunden in der Simulation an. Die Nützlichkeit von VGI wurde unter Berücksichtigung der mit ihrer Zuverlässigkeit verbundenen Unsicherheiten hauptsächlich hinsichtlich Genauigkeit und Zeitzuordnung untersucht. Die Ergebnisse zeigen das Potenzial neuer Daten zur Verbesserung der Leistung des Flutmodells, wobei teilweise die Einschränkungen und die oftmals knappe Verfügbarkeit herkömmlicher Sensoren überwunden werden. Schließlich ergab die gleichzeitige Integration aller drei Arten von Beobachtungen vielversprechende Ergebnisse und verbesserte die Leistung des Modells im Vergleich zur Nutzung nur von Satellitenbilder oder VGI-Beobachtungen

    Development and Testing of Numerical Hydrodynamic Tools for Large-scale Flood Hazard and Risk Assessment

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    The increasing availability of high-resolution topographic data and the ever-growing computational potential of workstations enable us to simulate inundation events with higher accuracy across geographically larger areas. Recent studies suggest using fully two-dimensional (2D) models with high resolution in order to avoid uncertainties and limitations coming from the incorrect interpretation of flood dynamics and unrealistic reproductions of the terrain topography. Additionally, low-frequency high-magnitude events bring additional challenges as conventional structural flood protection systems (e.g. levees), which are omnipresent in floodplain landscapes, might collapse due to hydraulic conditions such as high water loads, durations and velocities, or geotechnical factors that weaken structures (e.g. burrowing animal activities). Therefore, it is important to jointly consider the distribution of the inundated zones, potential levee breaching and holistic river-system behaviour when assessing flood hazard. In order to address the abovementioned challenges the present research focuses on the high-resolution flood simulations performed on geographically large areas using 2D inundation models with a specific focus on complex topography (e.g. main and minor levee systems, embankments, artificial canals, etc.). Our study evaluates and compares numerical models of different complexity by testing them on a floodplain inundation event that occurred in the basin of the Secchia River, Northern Italy, on 19th January, 2014. Then, we test fully 2D raster-based model to simulate the event on the 350 km long stretch on the mid-lower portion of the Po River and provide insight on the input terrain resolutions, accuracy and computation time. Moreover, this Thesis aims at developing and testing a new tool, which allows for an efficient levee breach modelling and river dynamic tracking in fully 2D mode

    Towards an Extensible Expert-Sourcing Platform

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    University of Minnesota Ph.D. dissertation.May 2019. Major: Computer Science. Advisor: Mohamed Mokbel. 1 computer file (PDF); viii, 106 pages.In recent years, general purpose crowdsourcing platforms, e.g., Amazon Mechanical Turk, Figure Eight, and ChinaCrowds, have been gaining a lot of popularity due to their capability in solving tasks that are still difficult for machines or computers to solve, e.g., labeling data, sorting images, computing skyline over noisy data, and sentiment analysis. Unfortunately, current crowdsourcing platforms are lacking a very important feature that is desired by many of the recent crowdsourcing applications, namely, recruiting workers that are expert at a given task. Being able to recruit expert workers will allow those applications to not only achieve a more accurate results but also higher quality results than recruiting general crowd for the applications. We call such crowdsourcing process as expert-sourcing, i.e., outsourcing tasks to experts. Without having any platforms to support them, developers of each expert-sourcing application needs to build the whole crowdsourcing system stack from scratch while, in fact, those systems share many common components with each other. This thesis proposes Luna; the first extensible expert-sourcing platform. To instantiate a new expert-sourcing application out of Luna, one only needs to provide a few simple plug-ins that will be integrated with the core components of Luna to provide the expert-sourcing platform for the new application. This is possible due to the fact that Luna is able to identify the components that can be shared among many expert-sourcing applications and the components that need to be tailored for a specific application. In this thesis, we show the extensibility of Luna by instantiating six different expert-sourcing applications that are currently not well supported by the general purpose crowdsourcing platforms. Experimental evaluation with real crowdsourcing deployment as well as by using real dataset shows that Luna is able to achieve not only more accurate but also better quality results than existing general purpose crowdsourcing platforms in supporting expert-sourcing applications. Lastly, we also provide a more specialized expert-sourcing platform for image geotagging application that is initially deemed unfit to be solved by crowdsourcing
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