scholarly journals Tropospheric Planetary Wave Dynamics and Mixture Modeling: Two Preferred Regimes and a Regime Shift

2007 ◽  
Vol 64 (10) ◽  
pp. 3521-3541 ◽  
Author(s):  
A. Hannachi
Keyword(s):  
General Circulation ◽  
Regime Shift ◽  
Planetary Wave ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Gaussian Mixture Models ◽  
Circulation Model ◽  
Gaussian Mixture ◽  
Stochastic Dynamical Systems ◽  
Wave Dynamics

Abstract Investigation of preferred structures of planetary wave dynamics is addressed using multivariate Gaussian mixture models. The number of components in the mixture is obtained using order statistics of the mixing proportions, hence avoiding previous difficulties related to sample sizes and independence issues. The method is first applied to a few low-order stochastic dynamical systems and data from a general circulation model. The method is next applied to winter daily 500-hPa heights from 1949 to 2003 over the Northern Hemisphere. A spatial clustering algorithm is first applied to the leading two principal components (PCs) and shows significant clustering. The clustering is particularly robust for the first half of the record and less for the second half. The mixture model is then used to identify the clusters. Two highly significant extratropical planetary-scale preferred structures are obtained within the first two to four EOF state space. The first pattern shows a Pacific–North American (PNA) pattern and a negative North Atlantic Oscillation (NAO), and the second pattern is nearly opposite to the first one. It is also observed that some subspaces show multivariate Gaussianity, compatible with linearity, whereas others show multivariate non-Gaussianity. The same analysis is also applied to two subperiods, before and after 1978, and shows a similar regime behavior, with a slight stronger support for the first subperiod. In addition a significant regime shift is also observed between the two periods as well as a change in the shape of the distribution. The patterns associated with the regime shifts reflect essentially a PNA pattern and an NAO pattern consistent with the observed global warming effect on climate and the observed shift in sea surface temperature around the mid-1970s.

scholarly journals The Antarctic Oscillation Structure in an AOGCM with Interactive Stratospheric Ozone

2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
S. Brand ◽  
K. Dethloff ◽  
D. Handorf
Keyword(s):  
General Circulation ◽  
Planetary Wave ◽  
Middle Atmosphere ◽  
Stratospheric Ozone ◽  
Circulation Model ◽  
Wave Activity ◽  
Antarctic Oscillation ◽  
Wave Dynamics ◽  
Hemisphere Winter ◽  
The Antarctic

Based on 150-year equilibrium simulations using the atmosphere-ocean-sea ice general circulation model (AOGCM) ECHO-GiSP, the southern hemisphere winter circulation is examined focusing on tropo-stratosphere coupling and wave dynamics. The model covers the troposphere and strato-mesosphere up to 80 km height and includes an interactive stratospheric chemistry. Compared to the reference simulation without interactive chemistry, the interactive simulation shows a weaker polar vortex in the middle atmosphere and is shifted towards the negative phase of the Antarctic Oscillation (AAO) in the troposphere. Differing from the northern hemisphere winter situation, the tropospheric planetary wave activity is weakened. A detailed analysis shows, that the modelled AAO zonal mean signal behaves antisymmetrically between troposphere and strato-mesosphere. This conclusion is supported by reanalysis data and a discussion of planetary wave dynamics in terms of Eliassen-Palm fluxes. Thereby, the tropospheric planetary wave activity appears to be controlled from the middle atmosphere.

Linear and Nonlinear Signatures in the Planetary Wave Dynamics of an AGCM: Probability Density Functions

2007 ◽  
Vol 64 (1) ◽  
pp. 117-136 ◽  
Author(s):  
Judith Berner ◽  
Grant Branstator
Keyword(s):  
Phase Space ◽  
Probability Density ◽  
General Circulation ◽  
Planetary Wave ◽  
Circulation Model ◽  
Gaussian Mixture ◽  
Empirical Orthogonal Functions ◽  
Planetary Waves ◽  
Linear And Nonlinear ◽  
Non Gaussian

Abstract To identify and quantify indications of linear and nonlinear planetary wave behavior and their impact on the distribution of atmospheric states, characteristics of a very long integration of an atmospheric general circulation model (GCM) in a four-dimensional phase space are examined. The phase space is defined by the leading four empirical orthogonal functions of 500-hPa geopotential heights. First it is established that nonlinear tendencies similar to those reported in an earlier study of the phase space behavior in this GCM have the potential to lead to non-Gaussian features in the probability density function (PDF) of planetary waves. Then using objective measures it is demonstrated that the model’s distribution of states has distinctive non-Gaussian features. These features are characterized in various subspaces of dimension as high as four. A key feature is the presence of three radial ridges of enhanced probability emanating from the mode, which is shifted away from the climatological mean. There is no evidence of multiple maxima in the full PDF, but the radial ridges lead to three distinct modes in the distribution of circulation patterns. It is demonstrated that these key aspects of non-Gaussianity are captured by a two-Gaussian mixture model fitted in four dimensions. The two circulation states at the centroids of the component Gaussians are very similar to those associated with two nonlinear features identified by Branstator and Berner in their analysis of the trajectories of the GCM. These two dynamical features are locally linear, so it is concluded that the behavior of planetary waves can be conceptualized as being approximately piecewise-linear, leading to a two-Gaussian mixture with three preferred patterns.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

scholarly journals A method for assessment of the general circulation model quality using K-means clustering algorithm

2021 ◽  
Author(s):  
Urmas Raudsepp ◽  
Ilja Maljutenko
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Clustering Algorithm ◽  
Measurement Data ◽  
Circulation Model ◽  
Optimal Number ◽  
Model Errors ◽  
Model Quality ◽  
Dimensional Error ◽  
The Baltic

Abstract. The model's ability to reproduce the state of the simulated object or particular feature or phenomenon is always a subject of discussion. Multidimensional model quality assessment is usually customized to the specific focus of the study and often to a limited number of locations. In this paper, we propose a method that provides information on the accuracy of the model in general, while all dimensional information for posterior analysis of the specific tasks is retained. The main goal of the method is to perform clustering of the multivariate model errors. The clustering is done using the K-means algorithm of unsupervised machine learning. In addition, the potential application of the K-means clustering of model errors for learning and predicting is shown. The method is tested on the 40-year simulation results of the general circulation model of the Baltic Sea. The model results are evaluated with the measurement data of temperature and salinity from more than one million casts by forming a two-dimensional error space and performing a clustering procedure in it. The optimal number of clusters that consist of four clusters was determined using the Elbow cluster selection criteria and based on the analysis of the different number of error clusters. In this particular model, the error cluster of good quality of the model with a bias of 0.4 °C (std = 0.8 °C) for temperature and 0.6 g kg−1 (std = 0.7 g kg−1) for salinity made up 57 % of all comparison data pairs. The prediction of centroids from a limited number of randomly selected data showed that the obtained centroids gained a stability of at least 100 000 error pairs in the learning dataset.

scholarly journals BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3035 ◽  
Author(s):  
Elaina D. Graham ◽  
John F. Heidelberg ◽  
Benjamin J. Tully
Keyword(s):  
Neural Networks ◽  
Microbial Community ◽  
Mixture Models ◽  
Clustering Algorithm ◽  
Gaussian Mixture Models ◽  
Gc Content ◽  
Gaussian Mixture ◽  
Affinity Propagation ◽  
Adjusted Rand Index ◽  
Low Coverage

Metagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environmental microorganisms is important for interpreting microbial community functions and the impacts these communities have on geochemical cycles. However, with metagenomic studies there is the computational hurdle of ‘binning’ contigs into phylogenetically related units or putative genomes. Binning methods have been implemented with varying approaches such as k-means clustering, Gaussian mixture models, hierarchical clustering, neural networks, and two-way clustering; however, many of these suffer from biases against low coverage/abundance organisms and closely related taxa/strains. We are introducing a new binning method, BinSanity, that utilizes the clustering algorithm affinity propagation (AP), to cluster assemblies using coverage with compositional based refinement (tetranucleotide frequency and percent GC content) to optimize bins containing multiple source organisms. This separation of composition and coverage based clustering reduces bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that BinSanity has a higher precision, recall, and Adjusted Rand Index compared to five commonly implemented methods. When tested on a previously published environmental metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.

Linear and Nonlinear Signatures in the Planetary Wave Dynamics of an AGCM: Phase Space Tendencies

2005 ◽  
Vol 62 (6) ◽  
pp. 1792-1811 ◽  
Author(s):  
Grant Branstator ◽  
Judith Berner
Keyword(s):  
Phase Space ◽  
General Circulation ◽  
Planetary Wave ◽  
Gaussian White Noise ◽  
Circulation Model ◽  
Lag Time ◽  
Empirical Orthogonal Functions ◽  
Linear Component ◽  
Least Squares Fit ◽  
Linear And Nonlinear

Abstract To identify and quantify indications of linear and nonlinear planetary wave behavior, characteristics of a very long integration of an atmospheric general circulation model in a four-dimensional phase space are examined. The phase space is defined by the leading four empirical orthogonal functions of 500-hPa geopotential heights, and the primary investigated characteristic is the state dependence of mean phase space tendencies. Defining the linear component of planetary wave tendencies as that part which can be captured by a least squares fit linear operator driven by additive Gaussian white noise, the study finds that there are distinct linear and nonlinear signatures. These signatures are especially easy to see in plots of mean tendencies projected onto phase space planes. For some planes the mean tendencies are highly linear, while for others there are strong departures from linearity. The results of the analysis are found to depend strongly on the lag time used to estimate tendencies with the linear component monotonically increasing with lag time. This is shown to result from the ergodicity of the system. Using the theory of Markov models it is possible to remove the lag-dependent component of the tendencies from the results. When this is done the projected mean dynamics in some planes is found to be almost exclusively nonlinear, while in others it is nearly linear. In the four-dimensional space the linear component of the dynamics is largely a reflection of a westward propagating Northern Hemisphere pattern concentrated over the Pacific and North America. The nonlinear signature can be approximated by two linear functions, each operating in a different region of phase space. One region is centered around a Pacific blocking pattern while the other is centered on a state with enhanced zonal symmetry. It is concluded that reduced models of the planetary waves should strive to include these state-dependent dynamics.

Spectral Clustering Algorithm of Uncertain Objects

2013 ◽  
Vol 300-301 ◽  
pp. 1058-1061
Author(s):  
Tong He
Keyword(s):  
Mixture Models ◽  
Experimental Evaluation ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Bayesian Probability ◽  
Leibler Divergence ◽  
Effectiveness And Efficiency ◽  
Spectral Clustering Algorithm

By extending classical spectral clustering algorithm, a new clustering algorithm of uncertain objects is proposed in this paper. In the algorithm, each uncertain object is represented as a Gaussian mixture model, and Kullback-Leibler divergence and Bayesian probability are respectively used as similarity measure between Gaussian mixture models. In an extensive experimental evaluation, we not only show the effectiveness and efficiency of the new algorithm and compare it with CLARANS algorithm of uncertain objects.

Probability Estimation of Direct Hydrocarbon Indicators Using Gaussian Mixture Models

2021 ◽  
Author(s):  
John B. Lemos ◽  
Matheus R. S. Barbosa ◽  
Edric B. Troccoli ◽  
Alexsandro G. Cerqueira
Keyword(s):  
Cluster Analysis ◽  
Mixture Models ◽  
Clustering Algorithm ◽  
Gaussian Mixture Models ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Original Dataset ◽  
Pca Algorithm ◽  
Optimal Number Of Clusters

This work aims to delimit the Direct Hydrocarbon Indicators (DHI) zones using the Gaussian Mixture Models (GMM) algorithm, an unsupervised machine learning method, over the FS8 seismic horizon in the seismic data of the Dutch F3 Field. The dataset used to perform the cluster analysis was extracted from the 3D seismic dataset. It comprises the following seismic attributes: Sweetness, Spectral Decomposition, Acoustic Impedance, Coherence, and Instantaneous Amplitude. The Principal Component Analysis (PCA) algorithm was applied in the original dataset for dimensionality reduction and noise filtering, and we choose the first three principal components to be the input of the clustering algorithm. The cluster analysis using the Gaussian Mixture Models was performed by varying the number of groups from 2 to 20. The Elbow Method suggested a smaller number of groups than needed to isolate the DHI zones. Therefore, we observed that four is the optimal number of clusters to highlight this seismic feature. Furthermore, it was possible to interpret other clusters related to the lithology through geophysical well log data.

scholarly journals The Origin of Nonlinear Signatures of Planetary Wave Dynamics: Mean Phase Space Tendencies and Contributions from Non-Gaussianity

2007 ◽  
Vol 64 (11) ◽  
pp. 3987-4003 ◽  
Author(s):  
Christian Franzke ◽  
Andrew J. Majda ◽  
Grant Branstator
Keyword(s):  
Phase Space ◽  
General Circulation ◽  
General Framework ◽  
Planetary Wave ◽  
Low Frequency ◽  
Planetary Waves ◽  
Small Deviations ◽  
Wave Dynamics ◽  
Gaussian Case ◽  
The Impact

Abstract Mean phase space tendencies are investigated to systematically identify the origin of nonlinear signatures and the dynamical significance of small deviations from Gaussianity of planetary low-frequency waves. A general framework for the systematic investigation of mean phase space tendencies in complex geophysical systems is derived. In the special case of purely Gaussian statistics, this theory predicts that the interactions among the planetary waves themselves are the source of the nonlinear signatures in phase space, whereas the unresolved waves contribute only an amplitude-independent forcing, and cannot contribute to any nonlinear signature. The predictions of the general framework are studied for a simple stochastic climate model. This toy model has statistics that are very close to being Gaussian and a strong nonlinear signature in the form of a double swirl in the mean phase space tendencies of its low-frequency variables, much like recently identified signatures of nonlinear planetary wave dynamics in prototype and comprehensive atmospheric general circulation models (GCMs). As predicted by the general framework for the Gaussian case, the double swirl results from nonlinear interactions of the low-frequency variables. Mean phase space tendencies in a reduced space of a prototype atmospheric GCM are also investigated. Analysis of the dynamics producing nonlinear signatures in these mean tendencies shows a complex interplay between waves resolved in the subspace and unresolved waves. The interactions among the resolved planetary waves themselves do not produce the nonlinear signature. It is the interaction with the unresolved waves that is responsible for the nonlinear dynamics. Comparing this result with the predictions of the general framework for the Gaussian case shows that the impact of the unresolved waves is due to their small deviations from Gaussianity. This suggests that the observed deviations from Gaussianity, even though small, are dynamically relevant.

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