scholarly journals Aire Limitée Adaptation dynamique Développement InterNational – Limited Area Ensemble Forecasting (ALADIN-LAEF)

2019 ◽  
Vol 16 ◽  
pp. 63-68
Author(s):  
Martin Belluš ◽  
Florian Weidle ◽  
Christoph Wittmann ◽  
Yong Wang ◽  
Simona Taşku ◽  
...  
Keyword(s):  
Regional Cooperation ◽  
Daily Basis ◽  
Ensemble Forecasting ◽  
Energy Industry ◽  
Limited Area ◽  
Multi Scale ◽  
Probabilistic Information ◽  
Probabilistic Forecasts ◽  
Reliable Source ◽  
Meso Scale

Abstract. A meso-scale ensemble system Aire Limitée Adaptation dynamique Développement InterNational – Limited Area Ensemble Forecasting (ALADIN-LAEF) based on the limited area model ALADIN has been developed in the framework of Regional Cooperation for Limited Area modelling in Central Europe (RC LACE) consortium, focusing on short range probabilistic forecasts and profiting from advanced multi-scale ALARO physics. Its main purpose is to provide probabilistic forecast on daily basis for the national weather services of RC LACE partners. It also serves as a reliable source of probabilistic information applied to downstream hydrology and energy industry.

27 Years of Regional Cooperation for Limited Area Modelling in Central Europe

2018 ◽  
Vol 99 (7) ◽  
pp. 1415-1432 ◽  
Author(s):  
Yong Wang ◽  
Martin Belluš ◽  
Andrea Ehrlich ◽  
Máté Mile ◽  
Neva Pristov ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Central Europe ◽  
Regional Cooperation ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Member States ◽  
Limited Area Model ◽  
Area Model ◽  
Common System

AbstractThis paper describes 27 years of scientific and operational achievement of Regional Cooperation for Limited Area Modelling in Central Europe (RC LACE), which is supported by the national (hydro-) meteorological services of Austria, Croatia, the Czech Republic, Hungary, Romania, Slovakia, and Slovenia. The principal objectives of RC LACE are to 1) develop and operate the state-of-the-art limited-area model and data assimilation system in the member states and 2) conduct joint scientific and technical research to improve the quality of the forecasts.In the last 27 years, RC LACE has contributed to the limited-area Aire Limitée Adaptation Dynamique Développement International (ALADIN) system in the areas of preprocessing of observations, data assimilation, model dynamics, physical parameterizations, mesoscale and convection-permitting ensemble forecasting, and verification. It has developed strong collaborations with numerical weather prediction (NWP) consortia ALADIN, the High Resolution Limited Area Model (HIRLAM) group, and the European Centre for Medium-Range Weather Forecasts (ECMWF). RC LACE member states exchange their national observations in real time and operate a common system that provides member states with the preprocessed observations for data assimilation and verification. RC LACE runs operationally a common mesoscale ensemble system, ALADIN–Limited Area Ensemble Forecasting (ALADIN-LAEF), over all of Europe for early warning of severe weather.RC LACE has established an extensive regional scientific and technical collaboration in the field of operational NWP for weather research, forecasting, and applications. Its 27 years of experience have demonstrated the value of regional cooperation among small- and medium-sized countries for success in the development of a modern forecasting system, knowledge transfer, and capacity building.

scholarly journals Multi-scale atmospheric environment modelling for urban areas

2009 ◽  
Vol 3 (1) ◽  
pp. 53-57 ◽  
Author(s):  
A. A. Baklanov ◽  
R. B. Nuterman
Keyword(s):  
Urban Areas ◽  
Scale Effects ◽  
Urban Setting ◽  
Atmospheric Environment ◽  
Scale Model ◽  
Limited Area ◽  
Nested Models ◽  
Micro Scale ◽  
Multi Scale ◽  
Meso Scale

Abstract. Modern supercomputers allow realising multi-scale systems for assessment and forecasting of urban meteorology, air pollution and emergency preparedness and considering nesting with obstacle-resolved models. A multi-scale modelling system with downscaling from regional to city-scale with the Environment – HIgh Resolution Limited Area Model (Enviro-HIRLAM) and to micro-scale with the obstacle-resolved Micro-scale Model for Urban Environment (M2UE) is suggested and demonstrated. The M2UE validation results versus the Mock Urban Setting Trial (MUST) experiment indicate satisfactory quality of the model. Necessary conditions for the choice of nested models, building descriptions, areas and resolutions of nested models are analysed. Two-way nesting (up- and down-scaling), when scale effects both directions (from the meso-scale on the micro-scale and from the micro-scale on the meso-scale), is also discussed.

Evaluating the Validity of a Bayesian Program for Predicting Stage in Hodgkin’s Disease

1981 ◽  
Vol 20 (03) ◽  
pp. 174-178 ◽  
Author(s):  
A. I. Barnett ◽  
J. Cynthia ◽  
F. Jane ◽  
Nancy Gutensohn ◽  
B. Davies
Keyword(s):  
New England ◽  
Hodgkin's Disease ◽  
Bayesian Model ◽  
Medical Center ◽  
Hodgkin’S Disease ◽  
Test Procedure ◽  
Disease Patient ◽  
Probabilistic Information ◽  
Probabilistic Forecasts ◽  
England Medical

A Bayesian model that provides probabilistic information about the spread of malignancy in a Hodgkin’s disease patient has been developed at the Tufts New England Medical Center. In assessing the model’s reliability, it seemed important to use it to make predictions about patients other than those relevant to its construction. The accuracy of these predictions could then be tested statistically. This paper describes such a test, based on 243 Hodgkin’s disease patients of known pathologic stage. The results obtained were supportive of the model, and the test procedure might interest those wishing to determine whether the imperfections that attend any attempt to make probabilistic forecasts have gravely damaged their accuracy.

scholarly journals C‐LAEF : Convection‐permitting Limited‐Area Ensemble Forecasting system

2021 ◽  
Author(s):  
Clemens Wastl ◽  
Yong Wang ◽  
Aitor Atencia ◽  
Florian Weidle ◽  
Christoph Wittmann ◽  
...  
Keyword(s):  
Ensemble Forecasting ◽  
Limited Area ◽  
Forecasting System

A novel synergistic multi-scale modeling framework to predict micro- and meso-scale damage behaviors of 2D triaxially braided composite

2021 ◽  
pp. 105678952110339
Author(s):  
Hongyong Jiang ◽  
Yiru Ren ◽  
Qiduo Jin
Keyword(s):  
Compressive Load ◽  
Scale Model ◽  
Modeling Framework ◽  
Braided Composite ◽  
Multi Scale ◽  
Scale Modeling ◽  
Bridge Model ◽  
Multi Scale Modeling ◽  
Transverse Damage ◽  
Meso Scale

A novel synergistic multi-scale modeling framework with a coupling of micro- and meso-scale is proposed to predict damage behaviors of 2D-triaxially braided composite (2DTBC). Based on the Bridge model, the internal stress and micro damage of constituent materials are respectively coupled with the stress and damage of tow. The initial effective elastic properties of tow (IEEP) used as the predefined data are estimated by micro-mechanics models. Due to in-situ effects, stress concentration factor (SCF) is considered in the micro matrix, exhibiting progressive damage accumulation. Comparisons of IEEP and strengths between the Bridge and Chamis’ theory are conducted to validate the values of IEEP and SCF. Based on the representative volume element (RVE), the macro properties and damage modes of 2DTBC are predicted to be consistent with available experiments and meso-scale simulation. Both axial and transverse damage mechanisms of 2DTBC under tensile or compressive load are revealed. Micro fiber and matrix damage accumulations have significant effects on the meso-scale axial and transverse damage of tows due to multi-scale coupling effects. Different from existing meso-/multi-scale models, the proposed multi-scale model can capture a crucial phenomenon that the transverse damage of tow is vulnerable to micro fiber fracture. The proposed multi-scale framework provides a robust tool for future systematic studies on constituent materials level to larger-scale aeronautical materials.

Case Dependence of Multiscale Interactions between Multisource Perturbations for Convection-Permitting Ensemble Forecasting during SCMREX

2021 ◽  
Author(s):  
Xubin Zhang
Keyword(s):  
Perturbation Methods ◽  
Monsoon Rainfall ◽  
Southern China ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Synoptic Scale ◽  
Ensemble Prediction System ◽  
Control Member ◽  
Probabilistic Forecasts ◽  
Perturbation Energy

AbstractThis study examines the case dependence of the multiscale characteristics of initial condition (IC) and model physics (MO) perturbations and their interactions in a convection-permitting ensemble prediction system (CPEPS), focusing on the 12-h forecasts of precipitation perturbation energy. The case dependence of forecast performances of various ensemble configurations is also examined to gain guidance for CPEPS design. Heavy-rainfall cases over Southern China during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014 were discriminated between the strongly and weakly forced events in terms of synoptic-scale forcing, each of which included 10 cases. In the cases with weaker forcing, MO perturbations showed larger influences while the enhancements of convective activities relative to the control member due to IC perturbations were less evident, leading to smaller dispersion reduction due to adding MO perturbations to IC perturbations. Such dispersion reduction was more sensitive to IC and MO perturbation methods in the weakly and strongly forced cases, respectively. The dispersion reduction improved the probabilistic forecasts of precipitation, with more evident improvements in the cases with weaker forcing. To improve the benefits of dispersion reduction in forecasts, it is instructive to elaborately consider the case dependence of dispersion reduction, especially the various sensitivities of dispersion reduction to different-source perturbation methods in various cases, in CPEPS design.

scholarly journals Macro-meso scale simulations of 3D woven composite reinforcements during the forming process

2021 ◽  
Author(s):  
Jie Wang ◽  
Peng Wang ◽  
Nahiène Hamila ◽  
Philippe Boisse
Keyword(s):  
Computational Cost ◽  
Constitutive Law ◽  
Forming Process ◽  
Multi Scale ◽  
Rtm Process ◽  
Macroscopic Simulation ◽  
Deformations And Orientations ◽  
High Computational Cost ◽  
Meso Scale ◽  
Composite Reinforcements

During the forming stage in the RTM process, deformations and orientations of yarns at the mesoscopic scale are essential to evaluate mechanical behaviors of final composite products and calculate the permeability of the reinforcement. However, due to the high computational cost, it is very difficult to carry out a mesoscopic draping simulation for the entire reinforcement. In this paper, a macro-meso scale simulation of composite reinforcements is presented in order to predict mesoscopic deformations of the fabric in a reasonable calculation time. The proposed multi-scale method allows linking the macroscopic simulation of the reinforcement with the mesoscopic modelling of the RVE through a macromeso embedded analysis. On the base of macroscopic simulations using a hyperelastic constitutive law of the reinforcement, an embedded mesoscopic geometry is first deduced from the macroscopic simulation of the draping. To overcome the inconvenience of the macro-meso embedded solution which leads to unreal excessive yarn extensions, local mesoscopic simulations based on the embedded analysis are carried out on a single RVE by defining specific boundary conditions. Finally, the multi-scale forming simulations are investigated in comparison with the experimental results, illustrating the efficiency of the proposed approach, in terms of accuracy and CPU time.

scholarly journals A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

2007 ◽  
Vol 135 (4) ◽  
pp. 1424-1438 ◽  
Author(s):  
Andrew R. Lawrence ◽  
James A. Hansen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Ensemble Size ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Chaotic Model ◽  
The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

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