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https://academiccommons.columbia.edu/catalog?action=index&controller=catalog&f%5Bauthor_facet%5D%5B%5D=Ghil%2C+Michael&f%5Bpub_date_facet%5D%5B%5D=2007&format=rss&fq%5B%5D=has_model_ssim%3A%22info%3Afedora%2Fldpd%3AContentAggregator%22&q=&rows=500&sort=record_creation_date+desc
Academic Commons Search Resultsen-usGraphical Models for Statistical Inference and Data Assimilation
https://academiccommons.columbia.edu/catalog/ac:150473
Ihler, Alexander T.; Kirshner, Sergey; Ghil, Michael; Robertson, Andrew W.; Smyth, Padhraic10.7916/D85T3W4JMon, 23 Jul 2012 16:08:58 +0000In data assimilation for a system which evolves in time, one combines past and current observations with a model of the dynamics of the system, in order to improve the simulation of the system as well as any future predictions about it. From a statistical point of view, this process can be regarded as estimating many random variables, which are related both spatially and temporally: given observations of some of these variables, typically corresponding to times past, we require estimates of several others, typically corresponding to future times. Graphical models have emerged as an effective formalism for assisting in these types of inference tasks, particularly for large numbers of random variables. Graphical models provide a means of representing dependency structure among the variables, and can provide both intuition and efficiency in estimation and other inference computations. We provide an overview and introduction to graphical models, and describe how they can be used to represent statistical dependency and how the resulting structure can be used to organize computation. The relation between statistical inference using graphical models and optimal sequential estimation algorithms such as Kalman filtering is discussed. We then give several additional examples of how graphical models can be applied to climate dynamics, specifically estimation using multi-resolution models of large-scale data sets such as satellite imagery, and learning hidden Markov models to capture rainfall patterns in space and time.Graphical modeling (Statistics), Mathematical statistics, Meteorologyawr2001International Research Institute for Climate and SocietyArticlesCluster Analysis of Typhoon Tracks. Part II: Large-Scale Circulation and ENSO
https://academiccommons.columbia.edu/catalog/ac:150459
Camargo, Suzana J.; Robertson, Andrew W.; Gaffney, Scott J.; Smyth, Padhraic; Ghil, Michael10.7916/D89K4MW9Mon, 23 Jul 2012 15:35:31 +0000A new probabilistic clustering method, based on a regression mixture model, is used to describe tropical cyclone (TC) propagation in the western North Pacific (WNP). Seven clusters were obtained and described in Part I of this two-part study. In Part II, the present paper, the large-scale patterns of atmospheric circulation and sea surface temperature associated with each of the clusters are investigated, as well as associations with the phase of the El Niño–Southern Oscillation (ENSO). Composite wind field maps over the WNP provide a physically consistent picture of each TC type, and of its seasonality. Anomalous vorticity and outgoing longwave radiation indicate changes in the monsoon trough associated with different types of TC genesis and trajectory. The steering winds at 500 hPa are more zonal in the straight-moving clusters, with larger meridional components in the recurving ones. Higher values of vertical wind shear in the midlatitudes also accompany the straight-moving tracks, compared to the recurving ones. The influence of ENSO on TC activity over the WNP is clearly discerned in specific clusters. Two of the seven clusters are typical of El Niño events; their genesis locations are shifted southeastward and they are more intense. The largest cluster is recurving, located northwestward, and occurs more often during La Niña events. Two types of recurving and one of straight-moving tracks occur preferentially when the Madden–Julian oscillation is active over the WNP region.Southern oscillation, Atmospheric circulation, Typhoons, Meteorologysjc71, awr2001International Research Institute for Climate and SocietyArticlesCluster Analysis of Typhoon Tracks. Part I: General Properties
https://academiccommons.columbia.edu/catalog/ac:150456
Camargo, Suzana J.; Robertson, Andrew W.; Gaffney, Scott J.; Smyth, Padhraic; Ghil, Michael10.7916/D8K07DZGMon, 23 Jul 2012 15:15:24 +0000A new probabilistic clustering technique, based on a regression mixture model, is used to describe tropical cyclone trajectories in the western North Pacific. Each component of the mixture model consists of a quadratic regression curve of cyclone position against time. The best-track 1950–2002 dataset is described by seven distinct clusters. These clusters are then analyzed in terms of genesis location, trajectory, landfall, intensity, and seasonality. Both genesis location and trajectory play important roles in defining the clusters. Several distinct types of straight-moving, as well as recurving, trajectories are identified, thus enriching this main distinction found in previous studies. Intensity and seasonality of cyclones, though not used by the clustering algorithm, are both highly stratified from cluster to cluster. Three straight-moving trajectory types have very small within-cluster spread, while the recurving types are more diffuse. Tropical cyclone landfalls over East and Southeast Asia are found to be strongly cluster dependent, both in terms of frequency and region of impact. The relationships of each cluster type with the large-scale circulation, sea surface temperatures, and the phase of the El Niño–Southern Oscillation are studied in a companion paper.Typhoons--Tracks, Meteorologysjc71, awr2001International Research Institute for Climate and SocietyArticlesProbabilistic Clustering of Extratropical Cyclones Using Regression Mixture Models
https://academiccommons.columbia.edu/catalog/ac:150240
Gaffney, Scott J.; Robertson, Andrew W.; Smyth, Padhraic; Camargo, Suzana J.; Ghil, Michael10.7916/D8N01H5RWed, 18 Jul 2012 20:36:28 +0000A probabilistic clustering technique is developed for classification of wintertime extratropical cyclone (ETC) tracks over the North Atlantic. We use a regression mixture model to describe the longitude-time and latitude–time propagation of the ETCs. A simple tracking algorithm is applied to 6-hourly mean sea-level pressure fields to obtain the tracks from either a general circulation model (GCM) or a reanalysis data set. Quadratic curves are found to provide the best description of the data. We select a three-cluster classification for both data sets, based on a mix of objective and subjective criteria. The track orientations in each of the clusters are broadly similar for the GCM and reanalyzed data; they are characterized by predominantly south-to-north (S–N), west-to-east (W–E), and southwest-to-northeast (SW–NE) tracking cyclones, respectively. The reanalysis cyclone tracks, however, are found to be much more tightly clustered geographically than those of the GCM. For the reanalysis data, a link is found between the occurrence of cyclones belonging to different clusters of trajectory-shape, and the phase of the North Atlantic Oscillation (NAO). The positive phase of the NAO is associated with the SW–NE oriented cluster, whose tracks are relatively straight and smooth (with cyclones that are typically faster, more intense, and of longer duration). The negative NAO phase is associated with more-erratic W–E tracks, with typically weaker and slower-moving cyclones. The S–N cluster is accompanied by a more transient geopotential trough over the western North Atlantic. No clear associations are found in the case of the GCM composites. The GCM is able to capture cyclone tracks of quite realistic orientation, as well as subtle associated features of cyclone intensity, speed and lifetimes. The clustering clearly highlights, though, the presence of serious systematic errors in the GCM’s simulation of ETC behavior.Cyclone tracks, North Atlantic oscillation, Meteorologyawr2001, sjc71International Research Institute for Climate and SocietyArticles