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Agency: European Commission | Branch: H2020 | Program: ERA-NET-Cofund | Phase: SC5-02-2015 | Award Amount: 78.28M | Year: 2016

Within the European Research Area (ERA), the ERA4CS Consortium is aiming to boost, research for Climate Services (CS), including climate adaptation, mitigation and disaster risk management, allowing regions, cities and key economic sectors to develop opportunities and strengthen Europes leadership. CS are seen by this consortium as driven by user demands to provide knowledge to face impacts of climate variability and change, as well as guidance both to researchers and decisionmakers in policy and business. ERA4CS will focus on the development of a climate information translation layer bridging user communities and climate system sciences. It implies the development of tools, methods, standards and quality control for reliable, qualified and tailored information required by the various field actors for smart decisions. ERA4CS will boost the JPI Climate initiative by mobilizing more countries, within EU Member States and Associated Countries, by involving both the research performing organizations (RPOs) and the research funding organizations (RFOs), the distinct national climate services and the various disciplines of academia, including Social Sciences and Humanities. ERA4CS will launch a joint transnational co-funded call, with over 16 countries and up to 75M, with two complementary topics: (i) a cash topic, supported by 12 RFOs, on co-development for user needs and action-oriented projects; (ii) an in-kind topic, supported by 28 RPOs, on institutional integration of the research components of national CS. Finally, ERA4CS additional activities will initiate a strong partnership between JPI Climate and others key European and international initiatives (as Copernicus, KIC-Climate, JPIs, WMO/GFCS, Future Earth, Belmont Forum) in order to work towards a common vision and a multiyear implementation strategy, including better co-alignment of national programs and activities up to 2020 and beyond.

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: MG-3.1-2016 | Award Amount: 7.51M | Year: 2016

Aviation is one of the most critical infrastructures of the 21st century. Even comparably short interruptions can cause economic damage summing up to the Billion-Euro range. As evident from the past, aviation shows certain vulnerability with regard to natural hazards. The proposal EUNADICS-AV addresses airborne hazards (environmental emergency scenarios), including volcano eruptions, nuclear accidents and emergencies and other scenarios where aerosols and certain trace gases are injected into the atmosphere. Such events are considered rare, but may have an extremely high impact, as demonstrated during the European Volcanic Ash Crisis in 2010. Before the 1990s, insufficient monitoring as well as limited data analysis capabilities made it difficult to react to and to prepare for certain rare, high-impact events. Meanwhile, there are many data available during crisis situations, and the data analysis technology has improved significantly. However, there is still a significant gap in the Europe-wide availability of real time hazard measurement and monitoring information for airborne hazards describing what, where, how much in 3 dimensions, combined with a near-real-time European data analysis and assimilation system. The main objective of EUNADICS-AV is to close this gap in data and information availability, enabling all stakeholders in the aviation system to obtain fast, coherent and consistent information. This would allow a seamless response on a European scale, including ATM, ATC, airline flight dispatching and individual flight planning. In the SESAR 2020 Programme Execution Framework, EUNADICS-AV is a SESAR Enabling project (project delivering SESAR Technological Solutions). The project aims at passing a SESAR maturity level V2, which includes respective service validation activities, including validation exercises. Work will be also done to prepare a full V3 validation.

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: FETHPC-1-2014 | Award Amount: 3.98M | Year: 2015

ESCAPE will develop world-class, extreme-scale computing capabilities for European operational numerical weather prediction (NWP) and future climate models. The biggest challenge for state-of-the-art NWP arises from the need to simulate complex physical phenomena within tight production schedules. Existing extreme-scale application software of weather and climate services is ill-equipped to adapt to the rapidly evolving hardware. This is exacerbated by other drivers for hardware development, with processor arrangements not necessarily optimal for weather and climate simulations. ESCAPE will redress this imbalance through innovation actions that fundamentally reform Earth-system modelling. ESCAPE addresses the ETP4HPC SRA Energy and resiliency priority topic, developing a holistic understanding of energy-efficiency for extreme-scale applications using heterogeneous architectures, accelerators and special compute units. The three key reasons why this proposal will provide the necessary means to take a huge step forward in weather and climate modelling as well as interdisciplinary research on energy-efficient high-performance computing are: 1) Defining and encapsulating the fundamental algorithmic building blocks (Weather & Climate Dwarfs) underlying weather and climate services. This is the pre-requisite for any subsequent co-design, optimization, and adaptation efforts. 2) Combining ground-breaking frontier research on algorithm development for use in extreme-scale, high-performance computing applications, minimizing time- and cost-to-solution. 3) Synthesizing the complementary skills of all project partners. This includes ECMWF, the world leader in global NWP together with leading European regional forecasting consortia, teaming up with excellent university research and experienced high-performance computing centres, two world-leading hardware companies, and one European start-up SME, providing entirely new knowledge and technology to the field.

Vannitsem S.,Institute Royal Meteorologique Of Belgique
Climate Dynamics | Year: 2014

The dynamics of a low-order coupled wind-driven ocean-atmosphere system is investigated with emphasis on its predictability properties. The low-order coupled deterministic system is composed of a baroclinic atmosphere for which 12 dominant dynamical modes are only retained (Charney and Straus in J Atmos Sci 37:1157-1176, 1980) and a wind-driven, quasi-geostrophic and reduced-gravity shallow ocean whose field is truncated to four dominant modes able to reproduce the large scale oceanic gyres (Pierini in J Phys Oceanogr 41:1585-1604, 2011). The two models are coupled through mechanical forcings only. The analysis of its dynamics reveals first that under aperiodic atmospheric forcings only dominant single gyres (clockwise or counterclockwise) appear, while for periodic atmospheric solutions the double gyres emerge. In the present model domain setting context, this feature is related to the level of truncation of the atmospheric fields, as indicated by a preliminary analysis of the impact of higher wavenumber ("synoptic" scale) modes on the development of oceanic gyres. In the latter case, double gyres appear in the presence of a chaotic atmosphere. Second the dynamical quantities characterizing the short-term predictability (Lyapunov exponents, Lyapunov dimension, Kolmogorov-Sinaï (KS) entropy) displays a complex dependence as a function of the key parameters of the system, namely the coupling strength and the external thermal forcing. In particular, the KS-entropy is increasing as a function of the coupling in most of the experiments, implying an increase of the rate of loss of information about the localization of the system on its attractor. Finally the dynamics of the error is explored and indicates, in particular, a rich variety of short term behaviors of the error in the atmosphere depending on the (relative) amplitude of the initial error affecting the ocean, from polynomial (at2 + bt3 + ct4) up to exponential-like evolutions. These features are explained and analyzed in the light of the recent findings on error growth (Nicolis et al. in J Atmos Sci 66:766-778, 2009). © 2013 Springer-Verlag Berlin Heidelberg.

Nicolis C.,Institute Royal Meteorologique Of Belgique
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

The classical setting of stochastic resonance is extended to account for the presence of an arbitrary number of simultaneously stable steady states. General expressions for the linear response are derived for systems involving one variable. The existence of an optimal value of noise strength and of an optimal number of stable states for which the response is maximized is established. © 2010 The American Physical Society.

Carrassi A.,Institute Royal Meteorologique Of Belgique | Vannitsem S.P.,Institute Royal Meteorologique Of Belgique
Monthly Weather Review | Year: 2010

In data assimilation, observations are combined with the dynamics to get an estimate of the actual state of a natural system. The knowledge of the dynamics, under the form of a model, is unavoidably incomplete and model error affects the prediction accuracy together with the error in the initial condition. The variational assimilation theory provides a framework to deal with model error along with the uncertainties coming from other sources entering the state estimation. Nevertheless, even if the problem is formulated as Gaussian, accounting for model error requires the estimation of its covariances and correlations, which are difficult to estimate in practice, in particular because of the large system dimension and the lack of enough observations. Model error has been therefore either neglected or assumed to be an uncorrelated noise. In the present work, an approach to account for a deterministic model error in the variational assimilation is presented. Equations for its correlations are first derived along with an approximation suitable for practical applications. Based on these considerations, a new four-dimensional variational data assimilation (4DVar) weak-constraint algorithm is formulated and tested in the context of a linear unstable system and of the three-component Lorenz model, which has chaotic dynamics. The results demonstrate that this approach is superior in skill to both the strong-constraint and a weak-constraint variational assimilation that employs the uncorrelated noise model error assumption. © 2010 American Meteorological Society.

Nicolis C.,Institute Royal Meteorologique Of Belgique | Nicolis G.,The Interdisciplinary Center
Quarterly Journal of the Royal Meteorological Society | Year: 2010

The formalism of irreversible thermodynamics is extended to include the effect of random perturbations and applied to representative systems giving rise to instabilities and to complex nonlinear behaviours. The extent to which dissipation as measured by the entropy production exhibits variational properties that can be linked to key indicators of the dynamical behaviour is explored with emphasis on the conjecture of the climate system as a system of maximum dissipation. © 2010 Royal Meteorological Society.

Carrassi A.,Institute Royal Meteorologique Of Belgique | Vannitsem S.,Institute Royal Meteorologique Of Belgique
Quarterly Journal of the Royal Meteorological Society | Year: 2011

An alternative formulation of the extended Kalman filter for state and parameter estimation is presented, referred to as Short-Time Augmented Extended Kalman Filter (ST-AEKF). In this algorithm, the evolution of the model error generated by the uncertain parameters is described using a truncated short-time Taylor expansion within the assimilation interval. This allows for a simplification of the forward propagation of the augmented error covariance matrix with respect to the classical state augmented approach. The algorithm is illustrated in the case of a scalar unstable dynamics and is then more extensively analyzed in the context of the Lorenz 36-variable model. The results demonstrate the ability of the ST-AEKF to provide accurate estimate of both the system's state and parameters with a skill comparable to that of the full state augmented approach and in some cases close to the EKF in a perfect model scenario. The performance of the filter is analyzed for different initial parametric errors and assimilation intervals, and for the estimates of one or more model parameters. The filter accuracy is sensitive to the nature of the estimated parameter but more importantly to the assimilation interval, a feature connected to the short-time approximation on which the filter formulation relies. The conditions and the context of applications of the present approach are also discussed. © 2011 Royal Meteorological Society.

Vannitsem S.,Institute Royal Meteorologique Of Belgique
Geophysical Research Letters | Year: 2015

The development of the low-frequency variability (LFV) in the atmosphere at multidecadal timescales is investigated in the context of a low-order coupled ocean-atmosphere model designed to emulate the interaction between the ocean mixed layer (OML) and the atmosphere at midlatitudes, both subject to seasonal variations of the Sun's radiative input. When no seasonal dependences are present, a LFV is emerging from the chaotic background for sufficiently large wind stress forcing (WSF). The period of this LFV is strongly controlled by the depth of the OML, with a shorter period for a deeper layer. In the seasonally dependent case, a similar LFV is developing that persists throughout the year. Remarkably, the emergence of this LFV occurs for smaller values of the WSF coefficient and is strongly related to the small thickness of the OML in summer, i.e., large impact of the WSF. Potential implications for real-world dynamics are discussed. Key Points The low-frequency variability at decadal timescales is robust in the presence of a seasonal cycle The summer ocean mixed layer depth plays a key role in the emergence of the LFV The mean depth of the ocean mixed layer controls the period of the low-frequency variability © 2015. American Geophysical Union. All Rights Reserved.

Roulin E.,Institute Royal Meteorologique Of Belgique | Vannitsem S.,Institute Royal Meteorologique Of Belgique
Monthly Weather Review | Year: 2012

Extended logistic regression is used to calibrate areal precipitation forecasts over two small catchments in Belgium computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) between 2006 and 2010. The parameters of the postprocessing are estimated from the hindcast database, characterized by a much lower number of members (5) than the EPS (51). Therefore, the parameters have to be corrected for predictor uncertainties. They have been fitted on the 51-member EPS ensembles, on 5-member subensembles drawn from the same EPS, and on the 5-member hindcasts. For small ensembles, a simple "regression calibration" method by which the uncertain predictors are corrected has been applied. The different parameter sets have been compared, and the corresponding extended logistic regressions have been applied to the 51-member EPS. The forecast probabilities have then been validated using rain gauge data and compared with the raw EPS. In addition, the calibrated distributions are also used to modify the ensembles of precipitation traces. The postprocessing with the extended logistic regression is shown to improve the continuous ranked probability skill score relative to the raw ensemble, and the regression calibration to remove a large portion of the bias in parameter estimation with small ensembles. With a training phase limited to a 5-week moving window, the benefit lasts for the first 2 forecast days in winter and the first 5 or 6 days in summer. In general, substantial improvements of the mean error and of the continuous ranked probability score have been shown. © 2012 American Meteorological Society.

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