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Journal articles on the topic 'Ensemble'
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1
Lawrence, Andrew R., and James A. Hansen. "A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size." Monthly Weather Review 135, no. 4 (April 1, 2007): 1424–38. http://dx.doi.org/10.1175/mwr3357.1.
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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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Rodríguez Molina, Javier. "Variantes léxicas y gramaticales del adverbio ensemble en la documentación medieval." Cuadernos del Instituto Historia de la Lengua, no. 7 (January 16, 2023): 405–24. http://dx.doi.org/10.58576/cilengua.vi7.129.
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En este artículo se estudia la evolución histórica del adverbiomedieval ensemble, que apenas si ha recibido atención en labibliografía. A partir de un exhaustivo análisis de documentos notariales,se defiende que ensemble no es un préstamo del francés antiguo,como sugieren varios estudios previos, sino un desarrollovernáculo del latín peninsular ĬN SĬMŬL. Esta hipótesis se basa tantoen la distribución geográfica de los datos medievales como en la variaciónmorfológica que presenta el adverbio (ensemble, ensembra,ensembla). En la Edad Media ensemble presenta un patrón de distribucióndialectal claro, ya que solo se documenta en León y en Aragón,pero no en Castilla y, a tenor de los datos presentados, dejó deusarse mucho más tarde de lo que se pensaba, pues no desaparece enel siglo XIII, sino en el XVI.
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Fiskvik, Anne Margrete. "La Famille Dansant. Investigating the Family Structure and Repertory of the Johannesénske Balletselskab." Nordic Theatre Studies 27, no. 2 (August 30, 2015): 104. http://dx.doi.org/10.7146/nts.v27i2.24254.
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The performance history of the Johannesénske Balletselskab spans a long period. In different shapes, sizes and names the ensemble was on the road for 30 years. This article analyses the activities of the Johannesénske enterprise through the lenses of itinerant performance traditions. Two features are discussed in this article: the reliance on family members as performers and the ensemble’s diverse repertory. The ensemble featured a repertory popular in its own time, consisting chiefly of national and character dances as well as pantomimes. Arguably, an investigation of the ensemble’s performance history offers information on little explored perspectives of nineteenth century Nordic ballet. In particular, the Johannesénske Balletselskab offers insights into family structures and the repertoire typical of itinerant ensembles. This information is also useful on a more general level, given that there were several ensembles similar to the Johannesénske travelling in the Nordic countries that functioned similarly to La famille dansant. Through analysing the use of the repertoire, it can be shown that the Johannesénske enterprise was, in their programming, up to date as a “ballet ensemble”. The family kept up itinerant traditions through their use of children as performers and reliance on pantomimes. The Johannesénske ensemble is, therefore, especially valuable for revealing insights into dance practices and dance styles, which have, so far, been little researched.
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Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.
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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
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Liu, Li Min, and Xiao Ping Fan. "A Survey: Clustering Ensemble Selection." Advanced Materials Research 403-408 (November 2011): 2760–63. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2760.
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Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area,it has been known that selective classifier ensembles can always achieve better solutions.Following the selective classifier ensembles,the question of clustering ensemble is defined as clustering ensemble selection.The paper introduces the concept of clustering ensemble selection and gives the survey of clustering ensemble selection algorithms.
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Kolczynski, Walter C., David R. Stauffer, Sue Ellen Haupt, Naomi S. Altman, and Aijun Deng. "Investigation of Ensemble Variance as a Measure of True Forecast Variance." Monthly Weather Review 139, no. 12 (December 1, 2011): 3954–63. http://dx.doi.org/10.1175/mwr-d-10-05081.1.
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Abstract The uncertainty in meteorological predictions is of interest for applications ranging from economic to recreational to public safety. One common method to estimate uncertainty is by using meteorological ensembles. These ensembles provide an easily quantifiable measure of the uncertainty in the forecast in the form of the ensemble variance. However, ensemble variance may not accurately reflect the actual uncertainty, so any measure of uncertainty derived from the ensemble should be calibrated to provide a more reliable estimate of the actual uncertainty in the forecast. A previous study introduced the linear variance calibration (LVC) as a simple method to determine the ensemble variance to error variance relationship and demonstrated this technique on real ensemble data. The LVC parameters, the slopes, and y intercepts, however, are generally different from the ideal values. This current study uses a stochastic model to examine the LVC in a controlled setting. The stochastic model is capable of simulating underdispersive and overdispersive ensembles as well as perfectly reliable ensembles. Because the underlying relationship is specified, LVC results can be compared to theoretical values of the slope and y intercept. Results indicate that all types of ensembles produce calibration slopes that are smaller than their theoretical values for ensemble sizes less than several hundred members, with corresponding y intercepts greater than their theoretical values. This indicates that all ensembles, even otherwise perfect ensembles, should be calibrated if the ensemble size is less than several hundred. In addition, it is shown that an adjustment factor can be computed for inadequate ensemble size. This adjustment factor is independent of the stochastic model and is applicable to any linear regression of error variance on ensemble variance. When applied to experiments using the stochastic model, the adjustment produces LVC parameters near their theoretical values for all ensemble sizes. Although the adjustment is unnecessary when applying LVC, it allows for a more accurate assessment of the reliability of ensembles, and a fair comparison of the reliability for differently sized ensembles.
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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics 14, no. 21 (November 11, 2014): 11791–815. http://dx.doi.org/10.5194/acp-14-11791-2014.
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Abstract. Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. Theoretical aspects like the bias–variance–covariance decomposition and the accuracy–diversity decomposition are linked together and support the importance of creating ensemble that incorporates both these elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi-model ensembles. The sophisticated ensemble averaging techniques, following proper training, were shown to have higher skill across all distribution bins compared to solely ensemble averaging forecasts.
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Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (April 30, 2022): 4577. http://dx.doi.org/10.3390/app12094577.
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Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.
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Louk, Maya Hilda Lestari, and Bayu Adhi Tama. "Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks." Big Data and Cognitive Computing 5, no. 4 (December 6, 2021): 72. http://dx.doi.org/10.3390/bdcc5040072.
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Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefits of ensemble-based models for identifying threats and attacks in a cyber-physical power grid. We provide a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, including cost-sensitive C4.5, roughly balanced bagging, random oversampling bagging, random undersampling bagging, synthetic minority oversampling bagging, random undersampling boosting, synthetic minority oversampling boosting, AdaC2, and EasyEnsemble. Each ensemble’s performance is tested against a range of benchmarked power system datasets utilizing balanced accuracy, Kappa statistics, and AUC metrics. Our findings demonstrate that EasyEnsemble outperformed significantly in comparison to its rivals across the board. Furthermore, undersampling and oversampling strategies were effective in a boosting-based ensemble but not in a bagging-based ensemble.
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LaRow, T. E., S. D. Cocke, and D. W. Shin. "Multiconvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies." Journal of Climate 18, no. 15 (August 1, 2005): 2963–78. http://dx.doi.org/10.1175/jcli3448.1.
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Abstract A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.
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Dey, Seonaid R. A., Giovanni Leoncini, Nigel M. Roberts, Robert S. Plant, and Stefano Migliorini. "A Spatial View of Ensemble Spread in Convection Permitting Ensembles." Monthly Weather Review 142, no. 11 (October 24, 2014): 4091–107. http://dx.doi.org/10.1175/mwr-d-14-00172.1.
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Abstract With movement toward kilometer-scale ensembles, new techniques are needed for their characterization. A new methodology is presented for detailed spatial ensemble characterization using the fractions skill score (FSS). To evaluate spatial forecast differences, the average and standard deviation are taken of the FSS calculated over all ensemble member–member pairs at different scales and lead times. These methods were found to give important information about the ensemble behavior allowing the identification of useful spatial scales, spinup times for the model, and upscale growth of errors and forecast differences. The ensemble spread was found to be highly dependent on the spatial scales considered and the threshold applied to the field. High thresholds picked out localized and intense values that gave large temporal variability in ensemble spread: local processes and undersampling dominate for these thresholds. For lower thresholds the ensemble spread increases with time as differences between the ensemble members upscale. Two convective cases were investigated based on the Met Office United Model run at 2.2-km resolution. Different ensemble types were considered: ensembles produced using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and an ensemble produced using different model physics configurations. Comparison of the MOGREPS and multiphysics ensembles demonstrated the utility of spatial ensemble evaluation techniques for assessing the impact of different perturbation strategies and the need for assessing spread at different, believable, spatial scales.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.
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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.
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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Siegert, Stefan, Jochen Bröcker, and Holger Kantz. "Rank Histograms of Stratified Monte Carlo Ensembles." Monthly Weather Review 140, no. 5 (May 1, 2012): 1558–71. http://dx.doi.org/10.1175/mwr-d-11-00302.1.
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Abstract The application of forecast ensembles to probabilistic weather prediction has spurred considerable interest in their evaluation. Such ensembles are commonly interpreted as Monte Carlo ensembles meaning that the ensemble members are perceived as random draws from a distribution. Under this interpretation, a reasonable property to ask for is statistical consistency, which demands that the ensemble members and the verification behave like draws from the same distribution. A widely used technique to assess statistical consistency of a historical dataset is the rank histogram, which uses as a criterion the number of times that the verification falls between pairs of members of the ordered ensemble. Ensemble evaluation is rendered more specific by stratification, which means that ensembles that satisfy a certain condition (e.g., a certain meteorological regime) are evaluated separately. Fundamental relationships between Monte Carlo ensembles, their rank histograms, and random sampling from the probability simplex according to the Dirichlet distribution are pointed out. Furthermore, the possible benefits and complications of ensemble stratification are discussed. The main conclusion is that a stratified Monte Carlo ensemble might appear inconsistent with the verification even though the original (unstratified) ensemble is consistent. The apparent inconsistency is merely a result of stratification. Stratified rank histograms are thus not necessarily flat. This result is demonstrated by perfect ensemble simulations and supplemented by mathematical arguments. Possible methods to avoid or remove artifacts that stratification induces in the rank histogram are suggested.
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Fraley, Chris, Adrian E. Raftery, and Tilmann Gneiting. "Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging." Monthly Weather Review 138, no. 1 (January 1, 2010): 190–202. http://dx.doi.org/10.1175/2009mwr3046.1.
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Abstract Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a weighted average of PDFs centered on the bias-corrected ensemble members, where the weights reflect the relative skill of the individual members over a training period. This work adapts the BMA approach to situations that arise frequently in practice; namely, when one or more of the member forecasts are exchangeable, and when there are missing ensemble members. Exchangeable members differ in random perturbations only, such as the members of bred ensembles, singular vector ensembles, or ensemble Kalman filter systems. Accounting for exchangeability simplifies the BMA approach, in that the BMA weights and the parameters of the component PDFs can be assumed to be equal within each exchangeable group. With these adaptations, BMA can be applied to postprocess multimodel ensembles of any composition. In experiments with surface temperature and quantitative precipitation forecasts from the University of Washington mesoscale ensemble and ensemble Kalman filter systems over the Pacific Northwest, the proposed extensions yield good results. The BMA method is robust to exchangeability assumptions, and the BMA postprocessed combined ensemble shows better verification results than any of the individual, raw, or BMA postprocessed ensemble systems. These results suggest that statistically postprocessed multimodel ensembles can outperform individual ensemble systems, even in cases in which one of the constituent systems is superior to the others.
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Melhauser, Christopher, Fuqing Zhang, Yonghui Weng, Yi Jin, Hao Jin, and Qingyun Zhao. "A Multiple-Model Convection-Permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014)." Weather and Forecasting 32, no. 2 (March 21, 2017): 665–88. http://dx.doi.org/10.1175/waf-d-16-0082.1.
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Abstract This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.
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WINDEATT, T., and G. ARDESHIR. "DECISION TREE SIMPLIFICATION FOR CLASSIFIER ENSEMBLES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (August 2004): 749–76. http://dx.doi.org/10.1142/s021800140400340x.
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The goal of designing an ensemble of simple classifiers is to improve the accuracy of a recognition system. However, the performance of ensemble methods is problem-dependent and the classifier learning algorithm has an important influence on ensemble performance. In particular, base classifiers that are too complex may result in overfitting. In this paper, the performance of Bagging, Boosting and Error-Correcting Output Code (ECOC) is compared for five decision tree pruning methods. A description is given for each of the pruning methods and the ensemble techniques. AdaBoost.OC which is a combination of Boosting and ECOC is compared with the pseudo-loss based version of Boosting, AdaBoost.M2 and the influence of pruning on the performance of the ensembles is studied. Motivated by the result that both pruned and unpruned ensembles made by AdaBoost.OC give similar accuracy, pruned ensembles are compared with ensembles of Decision Stumps. This leads to the hypothesis that ensembles of simple classifiers may give better performance for some problems. Using the application of face recognition, it is shown that an AdaBoost.OC ensemble of Decision Stumps outperforms an ensemble of pruned C4.5 trees for face identification, but is inferior for face verification. The implication is that in some real-world tasks to achieve best accuracy of an ensemble, it may be necessary to select base classifier complexity.
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Imran, Sheik, and Pradeep N. "A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images." International Journal of Health Sciences and Research 14, no. 1 (January 19, 2024): 201–13. http://dx.doi.org/10.52403/ijhsr.20240124.
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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, classification, and disease diagnosis. The ensemble models can be divided into those based on decision fusion strategies, bagging, boosting, stacking, negative correlation, explicit/implicit ensembles, homogeneous/heterogeneous ensembles, and explicit/implicit ensembles. In comparison to shallow or traditional, machine learning models and deep learning architectures are currently performing better. Also, a brief discussion of the various ensemble models used in CT images is provided. We wrap up this work by outlining a few possible avenues for further investigation. Key words: Computed Tomography, Ensemble, Deep learning, Machine Learning.
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Hart, Emma, and Kevin Sim. "On Constructing Ensembles for Combinatorial Optimisation." Evolutionary Computation 26, no. 1 (March 2018): 67–87. http://dx.doi.org/10.1162/evco_a_00203.
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Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any metaheuristic ensemble, and lead to better understanding of how to undertake principled ensemble design.
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Yokohata, Tokuta, Mark J. Webb, Matthew Collins, Keith D. Williams, Masakazu Yoshimori, Julia C. Hargreaves, and James D. Annan. "Structural Similarities and Differences in Climate Responses to CO2 Increase between Two Perturbed Physics Ensembles." Journal of Climate 23, no. 6 (March 15, 2010): 1392–410. http://dx.doi.org/10.1175/2009jcli2917.1.
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Abstract The equilibrium climate sensitivity (ECS) of the two perturbed physics ensembles (PPE) generated using structurally different GCMs, Model for Interdisciplinary Research on Climate (MIROC3.2) and the Third Hadley Centre Atmospheric Model with slab ocean (HadSM3), is investigated. A method to quantify the shortwave (SW) cloud feedback by clouds with different cloud-top pressure is developed. It is found that the difference in the ensemble means of the ECS between the two ensembles is mainly caused by differences in the SW low-level cloud feedback. The ensemble mean SW cloud feedback and ECS of the MIROC3.2 ensemble is larger than that of the HadSM3 ensemble. This is likely related to the 1XCO2 low-level cloud albedo of the former being larger than that of the latter. It is also found that the largest contribution to the within-ensemble variation of ECS comes from the SW low-level cloud feedback in both ensembles. The mechanism that causes the within-ensemble variation is different between the two ensembles. In the HadSM3 ensemble, members with large 1XCO2 low-level cloud albedo have large SW cloud feedback and large ECS; ensemble members with large 1XCO2 cloud cover have large negative SW cloud feedback and relatively low ECS. In the MIROC3.2 ensemble, the 1XCO2 low-level cloud albedo is much more tightly constrained, and no relationship is found between it and the cloud feedback. These results indicate that both the parametric uncertainties sampled in PPEs and the structural uncertainties of GCMs are important and worth further investigation.
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Sanderson, Benjamin M. "A Multimodel Study of Parametric Uncertainty in Predictions of Climate Response to Rising Greenhouse Gas Concentrations." Journal of Climate 24, no. 5 (March 1, 2011): 1362–77. http://dx.doi.org/10.1175/2010jcli3498.1.
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Abstract One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations from two such ensembles: a subset of the climateprediction.net ensemble using the Met Office Hadley Centre Atmosphere Model, version 3.0 and the new “CAMcube” ensemble using the Community Atmosphere Model, version 3.5. The study determines that the distribution of climate sensitivity in the two ensembles is very different: the climateprediction.net ensemble subset range is 1.7–9.9 K, while the CAMcube ensemble range is 2.2–3.2 K. On a regional level, however, both ensembles show a similarly diverse range in their mean climatology. Model radiative flux changes suggest that the major difference between the ranges of climate sensitivity in the two ensembles lies in their clear-sky longwave responses. Large clear-sky feedbacks present only in the climateprediction.net ensemble are found to be proportional to significant biases in upper-tropospheric water vapor concentrations, which are not observed in the CAMcube ensemble. Both ensembles have a similar range of shortwave cloud feedback, making it unlikely that they are causing the larger climate sensitivities in climateprediction.net. In both cases, increased negative shortwave cloud feedbacks at high latitudes are generally compensated by increased positive feedbacks at lower latitudes.
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Roberts, Brett, Burkely T. Gallo, Israel L. Jirak, Adam J. Clark, David C. Dowell, Xuguang Wang, and Yongming Wang. "What Does a Convection-Allowing Ensemble of Opportunity Buy Us in Forecasting Thunderstorms?" Weather and Forecasting 35, no. 6 (December 2020): 2293–316. http://dx.doi.org/10.1175/waf-d-20-0069.1.
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AbstractThe High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data Assimilation and Predictability (MAP) ensemble, two experimental CAM ensembles that ran during the 5-week Spring Forecasting Experiment (SFE) in spring 2018. The HRRRE and MAP are formally designed ensembles with spread achieved primarily through perturbed ICs. Verification in this study focuses on composite radar reflectivity and updraft helicity to assess ensemble performance in forecasting convective storms. The HREFv2.1 shows the highest overall skill for these forecasts, matching subjective real-time impressions from SFE participants. Analysis of the skill and variance of ensemble member forecasts suggests that the HREFv2.1 exhibits greater spread and more effectively samples model uncertainty than the HRRRE or MAP. These results imply that to optimize skill in forecasting convective storms at 1–2-day lead times, future CAM ensembles should employ either diverse membership designs or sophisticated perturbation schemes capable of representing model uncertainty with comparable efficacy.
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Kim, Kue Bum, Hyun-Han Kwon, and Dawei Han. "Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme." Hydrology and Earth System Sciences 20, no. 5 (May 17, 2016): 2019–34. http://dx.doi.org/10.5194/hess-20-2019-2016.
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Abstract. This study presents a novel bias correction scheme for regional climate model (RCM) precipitation ensembles. A primary advantage of using model ensembles for climate change impact studies is that the uncertainties associated with the systematic error can be quantified through the ensemble spread. Currently, however, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. Since the observation is only one case of many possible realizations due to the climate natural variability, a successful bias correction scheme should preserve the ensemble spread within the bounds of its natural variability (i.e. sampling uncertainty). To demonstrate a new bias correction scheme conforming to RCM precipitation ensembles, an application to the Thorverton catchment in the south-west of England is presented. For the ensemble, 11 members from the Hadley Centre Regional Climate Model (HadRM3-PPE) data are used and monthly bias correction has been done for the baseline time period from 1961 to 1990. In the typical conventional method, monthly mean precipitation of each of the ensemble members is nearly identical to the observation, i.e. the ensemble spread is removed. In contrast, the proposed method corrects the bias while maintaining the ensemble spread within the natural variability of the observations.
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Radanovics, Sabine, Jean-Philippe Vidal, and Eric Sauquet. "Spatial Verification of Ensemble Precipitation: An Ensemble Version of SAL." Weather and Forecasting 33, no. 4 (July 16, 2018): 1001–20. http://dx.doi.org/10.1175/waf-d-17-0162.1.
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Abstract Spatial verification methods able to handle high-resolution ensemble forecasts and analysis ensembles are increasingly required because of the increasing development of such ensembles. An ensemble extension of the structure–amplitude–location (SAL) spatial verification method is proposed here. The ensemble SAL (eSAL) allows for verifying ensemble forecasts against a deterministic or ensemble analysis. The eSAL components are equal to those of SAL in the deterministic case, thus allowing the comparison of deterministic and ensemble forecasts. The Mesoscale Verification Intercomparison over Complex Terrain (MesoVICT) project provides a dataset containing deterministic and ensemble precipitation forecasts as well as a deterministic and ensemble analysis for case studies in summer 2007 over the greater Alpine region. These datasets allow for testing of the sensitivity of SAL and eSAL to analysis uncertainty and their suitability for the verification of ensemble forecasts. Their sensitivity with respect to the main parameter of this feature-based method—the threshold for defining precipitation features—is furthermore tested for both the deterministic and ensemble forecasts. Our results stress the importance of using meaningful thresholds in order to limit any unstable behavior of the threshold-dependent SAL components. The eSAL components are typically close to the median of the distribution of deterministic SAL components calculated for all combinations of ensemble members of the forecast and the analysis, with considerably less computational time. The eSAL ensemble extension of SAL can be considered as a relevant summary measure that leads to more easily interpretable SAL diagrams.
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Allen, Douglas R., Karl W. Hoppel, and David D. Kuhl. "Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model." Atmospheric Chemistry and Physics 16, no. 13 (July 7, 2016): 8193–204. http://dx.doi.org/10.5194/acp-16-8193-2016.
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Abstract. Wind extraction from stratospheric ozone (O3) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2–0.5 for small ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ∼ 35 % with 25 and 50 members to ∼ 50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.
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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics Discussions 14, no. 11 (June 17, 2014): 15803–65. http://dx.doi.org/10.5194/acpd-14-15803-2014.
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Abstract. Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. There exists a trade-off between diversity and accuracy for which one cannot be gained without expenses of the other. Theoretical aspects like the bias-variance-covariance decomposition and the accuracy-diversity decomposition are linked together and support the importance of creating ensemble that incorporates both the elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi model ensembles. To this end we have devised statistical tools that can be used for diagnostic evaluation of ensemble modelling products, complementing existing operational methods.
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KO, ALBERT HUNG-REN, ROBERT SABOURIN, and ALCEU DE SOUZA BRITTO. "COMPOUND DIVERSITY FUNCTIONS FOR ENSEMBLE SELECTION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (June 2009): 659–86. http://dx.doi.org/10.1142/s021800140900734x.
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An effective way to improve a classification method's performance is to create ensembles of classifiers. Two elements are believed to be important in constructing an ensemble: (a) the performance of each individual classifier; and (b) diversity among the classifiers. Nevertheless, most works based on diversity suggest that there exists only weak correlation between classifier performance and ensemble accuracy. We propose compound diversity functions which combine the diversities with the performance of each individual classifier, and show that there is a strong correlation between the proposed functions and ensemble accuracy. Calculation of the correlations with different ensemble creation methods, different problems and different classification algorithms on 0.624 million ensembles suggests that most compound diversity functions are better than traditional diversity measures. The population-based Genetic Algorithm was used to search for the best ensembles on a handwritten numerals recognition problem and to evaluate 42.24 million ensembles. The statistical results indicate that compound diversity functions perform better than traditional diversity measures, and are helpful in selecting the best ensembles.
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Čyplytė, Raminta. "The Interaction Among Lithuanian Folk Dance Ensembles in the Context of Cultural Education: Directors’ Attitude." Pedagogika 114, no. 2 (June 10, 2014): 200–208. http://dx.doi.org/10.15823/p.2014.017.
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The article aims to reveal features and expression of the interaction between the state song and dance ensemble “Lietuva” and folk dance ensembles of higher education institutions in the process of youth cultural education. Since this aspect has not been analyzed in detail, the research was held among directors of folk dance ensembles of higher education institutions and the state song and dance ensemble „Lietuva“ and attempted to reveal two perspectives.The questioning of the directors showed that the interaction between ensemble “Lietuva” and folk dance ensembles of high schools in the context of youth cultural education exists and appears through folk dance ensembles connecting factors such as: genre of folk dance, common cultural activities and repertoire as well as common content of education which includes teaching methods, dance technique and its evaluation, other problems and relevant topics which forces to attract attention to the peculiarity of folk dance and its promotion in the contemporary cultural context.Directors of the ensemble “Lietuva” and high school ensembles stated that the ensemble “Lietuva” is still relevant today and actively participate in the cultural education of young people through folk dance, song and music hereby preserving national traditions and customs.
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Berrocal, Veronica J., Adrian E. Raftery, and Tilmann Gneiting. "Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts." Monthly Weather Review 135, no. 4 (April 1, 2007): 1386–402. http://dx.doi.org/10.1175/mwr3341.1.
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Abstract Forecast ensembles typically show a spread–skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the spatial BMA technique, which combines BMA and the geostatistical output perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather fields simultaneously, rather than just weather events at individual locations. At any site individually, spatial BMA reduces to the original BMA technique. The spatial BMA method provides statistical ensembles of weather field forecasts that take the spatial structure of observed fields into account and honor the flow-dependent information contained in the dynamical ensemble. The members of the spatial BMA ensemble are obtained by dressing the weather field forecasts from the dynamical ensemble with simulated spatially correlated error fields, in proportions that correspond to the BMA weights for the member models in the dynamical ensemble. Statistical ensembles of any size can be generated at minimal computational cost. The spatial BMA technique was applied to 48-h forecasts of surface temperature over the Pacific Northwest in 2004, using the University of Washington mesoscale ensemble. The spatial BMA ensemble generally outperformed the BMA and GOP ensembles and showed much better verification results than the raw ensemble, both at individual sites, for weather field forecasts, and for forecasts of composite quantities, such as average temperature in National Weather Service forecast zones and minimum temperature along the Interstate 90 Mountains to Sound Greenway.
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Mitchell, Herschel L., and P. L. Houtekamer. "Ensemble Kalman Filter Configurations and Their Performance with the Logistic Map." Monthly Weather Review 137, no. 12 (December 1, 2009): 4325–43. http://dx.doi.org/10.1175/2009mwr2823.1.
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Abstract This paper examines ensemble Kalman filter (EnKF) performance for a number of different EnKF configurations. The study is performed in a perfect-model context using the logistic map as forecast model. The focus is on EnKF performance when the ensemble is small. In accordance with theory, it is found that those configurations that maintain an appropriate ensemble spread are indeed those with the smallest ensemble mean error in a data assimilation cycle. Thus, the deficient ensemble spread produced by the single-ensemble EnKF results in increased ensemble mean error for this configuration. This problem with the conceptually simplest EnKF motivates an examination of a variety of other configurations. These include the configuration with a pair of ensembles and several configurations with overlapping ensembles, such as the four-subensemble configuration (used operationally at the Canadian Meteorological Centre) and the configuration in which observations are assimilated into each member using a gain computed from all of the other members. Also examined is a configuration that uses the jackknife estimator to obtain an estimate of the gain and an estimate of its uncertainty. Using these estimates, a different perturbed gain is then produced for each ensemble member. In general, it is found that these latter configurations outperform both the single-ensemble EnKF and the configuration with a pair of ensembles. In addition to these “stochastic” filters, the performance of a “deterministic” filter (which does not use perturbed observations) is also examined.
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Đurasević, Marko, and Domagoj Jakobović. "Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment." Axioms 13, no. 1 (January 5, 2024): 37. http://dx.doi.org/10.3390/axioms13010037.
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Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning is the selection of DRs to form the ensemble. To this end, various ensemble construction methods have been proposed over the years. However, these methods are quite computationally intensive and require a lot of computation time to obtain good ensembles. Therefore, in this study, we propose several simple heuristic ensemble construction methods that can be used to construct ensembles quite efficiently and without the need to evaluate their performance. The proposed methods construct the ensembles solely based on certain properties of the individual DRs used for their construction. The experimental study shows that some of the proposed heuristic construction methods perform better than more complex state-of-the-art approaches for constructing ensembles.
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Wilks, D. S. "On the Reliability of the Rank Histogram." Monthly Weather Review 139, no. 1 (January 1, 2011): 311–16. http://dx.doi.org/10.1175/2010mwr3446.1.
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Abstract Ensemble consistency is a name for the condition that an observation being forecast by a dynamical ensemble is statistically indistinguishable from the ensemble members. This statistical indistinguishability condition is meaningful only in a multivariate sense. That is, it pertains to the joint distribution of the ensemble members and the observation. The rank histogram has been designed to assess overall ensemble consistency, but mistakenly employing it to assess only restricted aspects of this joint distribution (e.g., the climatological distribution) leads to the incorrect conclusion that the verification rank histogram is not a useful diagnostic for good behavior of ensemble forecasts. The potential confusion is analyzed in the context of an idealized multivariate Gaussian model of forecast ensembles and their corresponding observations, and it is shown that the rank histogram does correctly assess the consistency of forecast ensembles.
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Siegert, S., J. Bröcker, and H. Kantz. "On the predictability of outliers in ensemble forecasts." Advances in Science and Research 8, no. 1 (March 28, 2012): 53–57. http://dx.doi.org/10.5194/asr-8-53-2012.
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Abstract. In numerical weather prediction, ensembles are used to retrieve probabilistic forecasts of future weather conditions. We consider events where the verification is smaller than the smallest, or larger than the largest ensemble member of a scalar ensemble forecast. These events are called outliers. In a statistically consistent K-member ensemble, outliers should occur with a base rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We study the predictability of outlier events in terms of the Brier Skill Score and find that forecast probabilities can be calculated which are more skillful than the unconditional base rate. This is shown analytically for statistically consistent ensembles. Using logistic regression, forecast probabilities for outlier events in an operational ensemble are calculated. These probabilities exhibit positive skill which is quantitatively similar to the analytical results. Possible causes of these results as well as their consequences for ensemble interpretation are discussed.
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Buizza, Roberto. "Comparison of a 51-Member Low-Resolution (TL399L62) Ensemble with a 6-Member High-Resolution (TL799L91) Lagged-Forecast Ensemble." Monthly Weather Review 136, no. 9 (September 1, 2008): 3343–62. http://dx.doi.org/10.1175/2008mwr2430.1.
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Abstract The 51-member TL399L62 ECMWF ensemble prediction system (EPS51) is compared with a lagged ensemble system based on the six most recent ECMWF TL799L91 forecasts (LAG6). The EPS51 and LAG6 systems are compared to two 6-member ensembles with a “weighted” ensemble-mean: EPS6wEM and LAG6wEM. EPS6wEM includes six members of EPS51 and has the ensemble mean constructed giving optimal weights to its members, while LAG6wEM includes the LAG6 six members and has the ensemble mean constructed giving optimal weights to its members. In these weighted ensembles, the optimal weights are based on 50-day forecast error statistics of each individual member (in EPS51 and LAG6 the ensemble mean is constructed giving the same weight to each individual member). The EPS51, LAG6, EPS6wEM, and LAG6wEM ensembles are compared for a 7-month period (from 1 April to 30 October 2006—213 cases) and for two of the most severe storms that hit the Scandinavian countries since 1969. The study shows that EPS51 has the best-tuned ensemble spread, and provides the best probabilistic forecasts, with differences in predictability between EPS51 and LAG6 or LAG6wEM probabilistic forecasts of geopotential height anomalies of up to 24 h. In terms of ensemble mean, EPS51 gives the best forecast from forecast day 4, but before forecast day 4 LAG6wEM provides a slightly better forecast, with differences in predictability smaller than 2 h up to forecast day 6, and of about 6 h afterward. The comparison also shows that a larger ensemble size is more important in the medium range rather than in the short range. Overall, these results indicate that if the aim of ensemble prediction is to generate not only a single (most likely) scenario but also a probabilistic forecast, than EPS51 has a higher skill than the lagged ensemble system based on LAG6 or LAG6wEM.
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Lee, Jared A., Walter C. Kolczynski, Tyler C. McCandless, and Sue Ellen Haupt. "An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Prediction." Monthly Weather Review 140, no. 7 (July 1, 2012): 2270–86. http://dx.doi.org/10.1175/mwr-d-11-00065.1.
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Abstract Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology. The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). This 24-member ensemble varies physics parameterizations for 18 randomly selected 48-h forecast periods in boreal summer 2009. Verification focuses on 2-m temperature and 10-m wind components at forecast lead times from 12 to 48 h. Various statistical guidance methods are employed for down-selection, calibration, and verification of the ensemble forecasts. The ensemble down-selection is accomplished with principal component analysis. The ensemble PDF is then statistically dressed, or calibrated, using Bayesian model averaging. The postprocessing techniques presented here result in a recommended down-selected ensemble that is about half the size of the original ensemble yet produces similar forecast performance, and still includes critical diversity in several types of physics schemes.
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Broomhead, Paul. "Individual Expressive Performance: Its Relationship to Ensemble Achievement, Technical Achievement, and Musical Background." Journal of Research in Music Education 49, no. 1 (April 2001): 71–84. http://dx.doi.org/10.2307/3345811.
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Participation in an expressive ensemble may be inappropriately presumed to produce expressive independence in individual ensemble members. This study is an examination of relationships between individual expressive achievement and (a) the expressive achievement of choral ensembles, (b) technical performance, and (c) musical background. Subjects included 11 high school choral ensembles and 82 individual ensemble members. A multivariate analysis of variance (MANOVA) revealed no significant relationships between individual and ensemble expressive achievement. Cor-relations showed technical and expressive performance to be strongly related. Significantly related musical background factors from a MANOVA included: (a) involvement in outside performing groups, (b) semesters of high school choir, (c) private vocal lessons, and (d) age of first private lessons. The study provided grounds for questioning the assumption that expressive ensembles yield expressive individuals.
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Buehner, Mark. "Local Ensemble Transform Kalman Filter with Cross Validation." Monthly Weather Review 148, no. 6 (May 6, 2020): 2265–82. http://dx.doi.org/10.1175/mwr-d-19-0402.1.
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Abstract Many ensemble data assimilation (DA) approaches suffer from the so-called inbreeding problem. As a consequence, there is an excessive reduction in ensemble spread by the DA procedure, causing the analysis ensemble spread to systematically underestimate the uncertainty of the ensemble mean analysis. The stochastic EnKF used for operational NWP in Canada largely avoids this problem by applying cross validation, that is, using an independent subset of ensemble members for updating each member. The goal of the present study is to evaluate two new variations of the local ensemble transform Kalman filter (LETKF) that also incorporate cross validation. In idealized numerical experiments with Gaussian-distributed background ensembles, the two new LETKF approaches are shown to produce reliable analysis ensembles such that the ensemble spread closely matches the uncertainty of the ensemble mean, without any ensemble inflation. In ensemble DA experiments with highly nonlinear idealized forecast models, the deterministic version of the LETKF with cross validation quickly diverges, but the stochastic version produces better results, nearly identical to the stochastic EnKF with cross validation. In the context of a regional NWP system, ensemble DA experiments are performed with the two new LETKF-based approaches with cross validation, the standard LETKF, and the stochastic EnKF. All approaches with cross validation produce similar ensemble spread at the first analysis time, though the amplitude of the changes to the individual members is larger with the stochastic approaches. Over the 10-day period of the experiments, the fit of the ensemble mean background state to radiosonde observations is statistically indistinguishable for all approaches evaluated.
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Baker, Casey M., and Yiyang Gong. "Identifying properties of pattern completion neurons in a computational model of the visual cortex." PLOS Computational Biology 19, no. 6 (June 6, 2023): e1011167. http://dx.doi.org/10.1371/journal.pcbi.1011167.
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Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles’ role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair’s power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.
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Yamaguchi, Munehiko, Frédéric Vitart, Simon T. K. Lang, Linus Magnusson, Russell L. Elsberry, Grant Elliott, Masayuki Kyouda, and Tetsuo Nakazawa. "Global Distribution of the Skill of Tropical Cyclone Activity Forecasts on Short- to Medium-Range Time Scales." Weather and Forecasting 30, no. 6 (November 25, 2015): 1695–709. http://dx.doi.org/10.1175/waf-d-14-00136.1.
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Abstract Operational global medium-range ensemble forecasts of tropical cyclone (TC) activity (genesis plus the subsequent track) are systematically evaluated to understand the skill of the state-of-the-art ensembles in forecasting TC activity as well as the relative benefits of a multicenter grand ensemble with respect to a single-model ensemble. The global ECMWF, JMA, NCEP, and UKMO ensembles are evaluated from 2010 to 2013 in seven TC basins around the world. The verification metric is the Brier skill score (BSS), which is calculated within a 3-day time window over a forecast length of 2 weeks to examine the skill from short- to medium-range time scales (0–14 days). These operational global medium-range ensembles are capable of providing guidance on TC activity forecasts that extends into week 2. Multicenter grand ensembles (MCGEs) tend to have better forecast skill (larger BSSs) than does the best single-model ensemble, which is the ECMWF ensemble in most verification time windows and most TC basins. The relative benefit of the MCGEs is relatively large in the north Indian Ocean and TC basins in the Southern Hemisphere where the BSS of the single-model ensemble is relatively small. The BSS metric and the reliability are found to be sensitive to the choice of threshold wind values that are used to define the model TCs.
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Taillardat, Maxime, Olivier Mestre, Michaël Zamo, and Philippe Naveau. "Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics." Monthly Weather Review 144, no. 6 (June 1, 2016): 2375–93. http://dx.doi.org/10.1175/mwr-d-15-0260.1.
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Abstract Ensembles used for probabilistic weather forecasting tend to be biased and underdispersive. This paper proposes a statistical method for postprocessing ensembles based on quantile regression forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function (PDF) like in ensemble model output statistics (EMOS) but provides an estimation of desired quantiles. This is a nonparametric approach that eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the ensemble but any predictor available including statistics on other variables. The method is applied to the Météo-France 35-member ensemble forecast (PEARP) for surface temperature and wind speed for available lead times from 3 up to 54 h and compared to EMOS. All postprocessed ensembles are much better calibrated than the PEARP raw ensemble and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for human forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.
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Alipova, Kseniya A., Vasiliy G. Mizyak, Mikhail A. Tolstykh, and Gordey S. Goyman. "Stochastic perturbations in the semi-Lagrangian advection algorithm of the SL-AV global atmosphere model." Russian Journal of Numerical Analysis and Mathematical Modelling 39, no. 1 (February 1, 2024): 1–11. http://dx.doi.org/10.1515/rnam-2024-0001.
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Abstract An algorithm for stochastic perturbation of the semi-Lagrangian trajectories is implemented in the ensemble weather prediction system based on the global atmosphere model SL-AV20 with a horizontal resolution of approximately 20 km, 51 vertical levels, and Local Ensemble Transform Kalman Filter (LETKF). The combined use of methods for stochastic perturbation of trajectories and the parameters and tendencies of subgrid-scale processes parameterizations allows to generate ensembles with a larger spread compared to ensembles without stochastic perturbations of trajectories. An improvement in probabilistic estimates of the ensemble forecasts for various variables is shown. The comparison of two versions of ensemble prediction system is presented.
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Yussouf, Nusrat, Jidong Gao, David J. Stensrud, and Guoqing Ge. "The Impact of Mesoscale Environmental Uncertainty on the Prediction of a Tornadic Supercell Storm Using Ensemble Data Assimilation Approach." Advances in Meteorology 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/731647.
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Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative.
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Sun, Xiao Wei, and Hong Bo Zhou. "Research on Applied Technology in Experiments with Three Boosting Algorithms." Advanced Materials Research 908 (March 2014): 513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.908.513.
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Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. In this paper we use applied technology to built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN and Boosting-MultiTAN ensembles with 25 sub-classifiers on standard benchmark datasets and the proposed technique was the most accurate. These results argue that boosting algorithms deserve more attention in machine learning and data mining communities.
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Jiang, Xiangkui, Chang-an Wu, and Huaping Guo. "Forest Pruning Based on Branch Importance." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3162571.
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A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.
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Zubarev, V. Yu, B. V. Ponomarenko, E. G. Shanin, and A. G. Vostretsov. "Formation of Minimax Ensembles of Aperiodic Gold Codes." Journal of the Russian Universities. Radioelectronics 23, no. 2 (April 28, 2020): 26–37. http://dx.doi.org/10.32603/1993-8985-2020-23-2-26-37.
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Introduction. Signals constructed on the basis of ensembles of code sequences are widely used in digital communication systems. During development of such systems, the most attention is paid to analysis, synthesis and implementation of periodic signal ensembles. Theoretic methods for synthesis of periodic signal ensembles are developed and are in use. Considerably fewer results are received regarding construction of aperiodic signal ensembles with given properties. Theoretical methods for synthesis of such ensembles are practically nonexistent.Aim. To construct aperiodic Gold code ensembles with the best ratios of code length to ensemble volume among the most known binary codes.Materials and methods. Methods of directed search and discrete choice of the best ensemble based on unconditional preference criteria are used.Results. Full and truncated aperiodic Gold code ensembles with given length and ensemble volume were constructed. Parameters and shape of auto- and mutual correlation functions were shown for a number of constructed ensembles. Comparison of the paper results with known results for periodic Gold code ensembles has been conducted regarding growth of minimax correlation function values depending on code length and ensemble volume.Conclusion. The developed algorithms, unlike the known ones, make it possible to form both complete ensembles and ensembles taking into account the limitation of their volume. In addition, the algorithms can be extended to the tasks of forming ensembles from other families, for example, assembled from code sequences belonging to different families.
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Ahmad, Amir, Hamza Abujabal, and C. Aswani Kumar. "Random Subclasses Ensembles by Using 1-Nearest Neighbor Framework." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (February 24, 2017): 1750031. http://dx.doi.org/10.1142/s0218001417500318.
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A classifier ensemble is a combination of diverse and accurate classifiers. Generally, a classifier ensemble performs better than any single classifier in the ensemble. Naive Bayes classifiers are simple but popular classifiers for many applications. As it is difficult to create diverse naive Bayes classifiers, naive Bayes ensembles are not very successful. In this paper, we propose Random Subclasses (RS) ensembles for Naive Bayes classifiers. In the proposed method, new subclasses for each class are created by using 1-Nearest Neighbor (1-NN) framework that uses randomly selected points from the training data. A classifier considers each subclass as a class of its own. As the method to create subclasses is random, diverse datasets are generated. Each classifier in an ensemble learns on one dataset from the pool of diverse datasets. Diverse training datasets ensure diverse classifiers in the ensemble. New subclasses create easy to learn decision boundaries that in turn create accurate naive Bayes classifiers. We developed two variants of RS, in the first variant RS(2), two subclasses per class were created whereas in the second variant RS(4), four subclasses per class were created. We studied the performance of these methods against other popular ensemble methods by using naive Bayes as the base classifier. RS(4) outperformed other popular ensemble methods. A detailed study was carried out to understand the behavior of RS ensembles.
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Fujita, Tadashi, David J. Stensrud, and David C. Dowell. "Surface Data Assimilation Using an Ensemble Kalman Filter Approach with Initial Condition and Model Physics Uncertainties." Monthly Weather Review 135, no. 5 (May 1, 2007): 1846–68. http://dx.doi.org/10.1175/mwr3391.1.
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Abstract The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach is evaluated for the potential to improve short-range forecasting. Two severe weather cases are examined, in which the assimilation is performed over a 6-h period using hourly surface observations followed by an 18-h simulation period. Ensembles are created in three different ways—by using different initial and boundary conditions, by using different model physical process schemes, and by using both different initial and boundary conditions and different model physical process schemes. The ensembles are compared in order to investigate the role of uncertainties in the initial and boundary conditions and physical process schemes in EnKF data assimilation. In the initial condition ensemble, spread is associated largely with the displacement of atmospheric baroclinic systems. In the physics ensemble, spread comes from the differences in model physics, which results in larger spread in temperature and dewpoint temperature than the initial condition ensemble, and smaller spread in the wind fields. The combined initial condition and physics ensemble has properties from both of the previous two ensembles. It provides the largest spread and produces the best simulation for most of the variables, in terms of the rms difference between the ensemble mean and observations. Perhaps most importantly, this combined ensemble provides very good guidance on the mesoscale features important to the severe weather events of the day.
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Schwartz, Craig S. "Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model." Monthly Weather Review 147, no. 8 (July 31, 2019): 2997–3023. http://dx.doi.org/10.1175/mwr-d-18-0452.1.
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Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.
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Kieburg, Mario. "Additive matrix convolutions of Pólya ensembles and polynomial ensembles." Random Matrices: Theory and Applications 09, no. 04 (November 8, 2019): 2150002. http://dx.doi.org/10.1142/s2010326321500027.
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Recently, subclasses of polynomial ensembles for additive and multiplicative matrix convolutions were identified which were called Pólya ensembles (or polynomial ensembles of derivative type). Those ensembles are closed under the respective convolutions and, thus, build a semi-group when adding by hand a unit element. They even have a semi-group action on the polynomial ensembles. Moreover, in several works transformations of the bi-orthogonal functions and kernels of a given polynomial ensemble were derived when performing an additive or multiplicative matrix convolution with particular Pólya ensembles. For the multiplicative matrix convolution on the complex square matrices the transformations were even done for general Pólya ensembles. In the present work, we generalize these results to the additive convolution on Hermitian matrices, on Hermitian anti-symmetric matrices, on Hermitian anti-self-dual matrices and on rectangular complex matrices. For this purpose, we derive the bi-orthogonal functions and the corresponding kernel for a general Pólya ensemble which was not done before. With the help of these results, we find transformation formulas for the convolution with a fixed matrix or a random matrix drawn from a general polynomial ensemble. As an example, we consider Pólya ensembles with an associated weight which is a Pólya frequency function of infinite order. But we also explicitly evaluate the Gaussian unitary ensemble as well as the complex Laguerre (aka Wishart, Ginibre or chiral Gaussian unitary) ensemble. All results hold for finite matrix dimension. Furthermore, we derive a recursive relation between Toeplitz determinants which appears as a by-product of our results.
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Cui, Bo, Zoltan Toth, Yuejian Zhu, and Dingchen Hou. "Bias Correction for Global Ensemble Forecast." Weather and Forecasting 27, no. 2 (April 1, 2012): 396–410. http://dx.doi.org/10.1175/waf-d-11-00011.1.
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Abstract The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts.