4,377 research outputs found

    Evaluation of a probabilistic hydrometeorological forecast system

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    Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread

    The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

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    The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

    Operational mesoscale atmospheric dispersion prediction using a parallel computing cluster

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    An operational atmospheric dispersion prediction system is implemented on a cluster supercomputer for Online Emergency Response at the Kalpakkam nuclear site. This numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48-hour forecast of the local weather and radioactive plume dispersion due to hypothetical airborne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. A 16-node dual Xeon distributed memory gigabit ethernet cluster has been found sufficient for operational applications. The runtime of a triple nested domain MM5 is about 4h for a 24h forecast. The system had been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Improvement is noticed in rainfall forecasts that used NCEP data, probably because of its high spatial and temporal resolution

    TOWARDS A GENERIC ONTOLOGY FOR SOLAR IRRADIANCE FORECASTING

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    The growth of solar energy resources in recent years has led to increased calls for accurate forecasts of solar irradiance for the reliable and sustainable integration of solar into the national grid. A growing body of academic research has developed models for forecasting solar irradiance, identified metrics for comparing solar forecasts, and described applications and end users of solar forecasts. In recent years, many disciplines are developing ontologies to facilitate better communication, improve inter-operabiity and refine knowledge reuse by experts and users of the domain. Ontologies are explicit and formal vocabulary of terms and their relationships. This report describes a step towards using ontologies to describe the knowledge, concepts and relationships in the domain of solar irradiance forecasting to develop a shared understanding for diverse stakeholders that interact with the domain. A preliminary ontology on solar irradiance forecasting was created and validated on three use cases

    From Sensor to Observation Web with Environmental Enablers in the Future Internet

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    This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)

    Aeronautical Engineering. A continuing bibliography, supplement 115

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    This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

    Machine Learning Tool for Transmission Capacity Forecasting of Overhead Lines based on Distributed Weather Data

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    Die Erhöhung des Anteils intermittierender erneuerbarer Energiequellen im elektrischen Energiesystem ist eine Herausforderung fĂŒr die Netzbetreiber. Ein Beispiel ist die Zunahme der Nord-SĂŒd Übertragung von Windenergie in Deutschland, die zu einer Erhöhung der EngpĂ€sse in den Freileitungen fĂŒhrt und sich direkt in den Stromkosten der Endverbraucher niederschlĂ€gt. Neben dem Ausbau neuer Freileitungen ist ein witterungsabhĂ€ngiger Freileitungsbetrieb eine Lösung, um die aktuelle Auslastung des Systems zu verbessern. Aus der Analyse in einer Probeleitung in Deutschland wurde gezeigt, dass einen Zuwachs von ca. 28% der StromtragfĂ€higkeit eine Reduzierung der Kosten fĂŒr Engpassmaßnahmen um ca. 55% bedeuten kann. Dieser Vorteil kann nur vom Netzbetreiber wahrgenommen werden, wenn eine Belastbarkeitsprognose fĂŒr die Stromerzeugunsgplanung der konventionellen Kraftwerke zur VerfĂŒgung steht. Das in dieser Dissertation vorgestellte System prognostiziert die Belastbarkeit von Freileitungen fĂŒr 48 Stunden, mit einer Verbesserung der Prognosegenauigkeit im Vergleich zum Stand-der-Technik von 6,13% in Durchschnitt. Der Ansatz passt die meteorologischen Vorhersagen an die lokale Wettersituation entlang der Leitung an. Diese Anpassungen sind aufgrund von VerĂ€nderungen der Topographie entlang der Leitungstrasse und Windschatten der umliegenden BĂ€ume notwendig, da durch die meteorologischen Modelle diese nicht beschrieben werden können. Außerdem ist das in dieser Dissertation entwickelte Modell in der Lage die Genauigkeitsabweichungen der Wettervorhersage zwischen Tag und Nacht abzugleichen, was vorteilhaft fĂŒr die Strombelastbarkeitsprognose ist. Die ZuverlĂ€ssigkeit und deswegen auch die Effizienz des Stromerzeugungsplans fĂŒr den nĂ€chsten 48 Stunden wurde um 10% gegenĂŒber dem Stand der Technik erhöht. Außerdem wurde in Rahmen dieser Arbeit ein Verfahren fĂŒr die Positionierung der Wetterstationen entwickelt, um die wichtigsten Stellen entlang der Leitung abzudecken und gleichzeitig die Anzahl der Wetterstationen zu minimieren. Wird ein verteiltes Sensornetzwerk in ganz Deutschland umgesetzt, wird die Einsparung von Redispatchingkosten eine Kapitalrendite von ungefĂ€hr drei Jahren bedeuten. Die DurchfĂŒhrung einer transienten Analyse ist im entwickelten System ebenfalls möglich, um EngpassfĂ€lle fĂŒr einige Minuten zu lösen, ohne die maximale Leitertemperatur zu erreichen. Dieses Dokument versucht, die Vorteile der Freileitungsmonitoringssysteme zu verdeutlichen und stellt eine Lösung zur UnterstĂŒtzung eines flexiblen elektrischen Netzes vor, die fĂŒr eine erfolgreiche Energiewende erforderlich ist

    Development of the MEGAN3 BVOC Emission Model for Use with the SILAM Chemical Transport Model

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    This project has aimed to investigate and propose improvements to the methods used in the System for Integrated ModeLing of Atmospheric coMposition (SILAM) model for simulating biogenic volatile organic compound (BVOC) emissions. The goal is to study an option in SILAM to use the Model for Emission of Gases and Aerosols in Nature, Version 3 (MEGAN3) as an alternative to SILAM’s existing BVOC calculation algorithm, which is a more simplified approach. SILAM is an atmospheric chemical transport, dispersion, and deposition modelling system owned and continuously developed by the Finnish Meteorological Institute (FMI). The model’s most well-known use is in forecasting air quality in Europe and southeast Asia. Although traffic and other urban emissions are important when modelling air quality, accurate modelling of biogenic emissions is also very important when developing a comprehensive, high-quality regional and sub-regional scale model. One of the motivations of this project is that if BVOC emission simulation in SILAM were improved, the improvements would be passed into subsequent atmospheric chemistry algorithms which form the molecules responsible to produce secondary organic aerosols (SOA). SOA have significant impacts on local and regional weather, climate, and air quality. The development in this project will therefore offer the potential for future improvement of air quality forecasting in the SILAM model. Because SILAM requires meteorological forecast as input boundary conditions, this study used output generated by the Environment-High Resolution Limited Area Model (Enviro-HIRLAM), developed by the HIRLAM Consortium in collaboration with universities in Denmark, Finland, the Baltic States, Ukraine, Russia, Turkey, Kazakhstan, and Spain. Enviro-HIRLAM includes multiple aerosol modes, which account for the effects of aerosols in the meteorological forecast. Running SILAM with and without the aerosol effects included in the Enviro-HIRLAM meteorological output showed that aerosols likely caused a minor decrease in BVOC emission rate. This project has focused on the boreal forest of HyytiĂ€lĂ€, southern Finland, the site of the Station for Measuring Ecosystem-Atmosphere Relations - II (SMEAR-II, 61.847°N, 24.294°E) during a one day trial on July 14, 2010. After performing a test run over the HyytiĂ€lĂ€ region in July 2010 for analysis, it was found that SILAM significantly underestimates BVOC emission rates of both isoprene and monoterpene, likely because of an oversimplified approach used in the model. The current approach in SILAM, called ‘Guenther Modified’, uses only a few equations from MEGAN and can be classified as a strongly simplified MEGAN version, with selected assumptions. It references a land cover classification map and lookup table, taking into account only three parameters (air temperature, month, and solar radiation) when performing the calculations. It does not take into account several other important parameters, which affect the BVOC emission rates. Based on qualitative analysis, this appears to be a simplified but limited approach. Therefore, based on these findings, the next step to improve SILAM simulations is to propose a full implementation of MEGAN as a replacement to the current logic in SILAM, which is to use land classification and a lookup table for BVOC emission estimates. MEGAN, which is a much more comprehensive model for simulating BVOC emissions from terrestrial ecosystems. MEGAN includes additional input parameters, such as Leaf Area Index (LAI), relative humidity, CO2 concentration, land cover, soil moisture, soil type, and canopy height. Furthermore, this study found that in the future, simulations involving BVOCs could also potentially be improved in SILAM by adding modern schemes for chemical reactions and SOA formation in future development of SILAM. After gaining in-depth understanding of the strengths and limitations of BVOC in the SILAM model, as practical result, some recommendations for improvements to the model are proposed
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