scholarly journals Regression Mixture Model Clustering of Multimodel Ensemble Forecasts of Hurricane Sandy: Partition Characteristics

2016 ◽  
Vol 144 (10) ◽  
pp. 3825-3846 ◽  
Author(s):  
Alex M. Kowaleski ◽  
Jenni L. Evans
Keyword(s):  
Mixture Model ◽  
Forecast Error ◽  
Potential Temperature ◽  
Lower Troposphere ◽  
Hurricane Sandy ◽  
Rand Index ◽  
Ensemble Prediction ◽  
Adjusted Rand Index ◽  
Cluster Assignment ◽  
Regression Mixture

Track and cyclone phase space (CPS) forecasts of Hurricane Sandy from four global ensemble prediction systems are clustered using regression mixture models. Bayesian information criterion, cluster assignment strength, and mean-squared forecast error are used to select optimal model specifications. Fourth-order (third order) polynomials for 168-h forecasts (60-h forecast segments) and 5 (6) clusters for track (CPS) forecasts are selected. Mean cluster paths from eight initialization times show that track and CPS clustering meaningfully partition potential tracks and structural evolutions, distilling a large number of ensemble members into several representative and distinct solutions. Rand index and adjusted Rand index calculations demonstrate a relationship between track and CPS cluster membership for both 168-h forecasts and 60-h forecast segments, indicating that certain tracks are preferentially associated with certain structural evolutions. These relationships are explained in greater detail using forecasts initialized at 0000 UTC 25 October. Storm-centered cluster composite maps of 500-hPa geopotential height and 850-hPa equivalent potential temperature for the 120-h forecast valid at 0000 UTC 30 October (initialized at 0000 UTC 25 October) indicate that both track and CPS clustering successfully capture variations in the Sandy–trough interaction and the strength of the lower-troposphere warm core of Sandy at the time of observed landfall. Together, these results illustrate the relationship between the track and structural evolution of Sandy and suggest the potential of multiensemble mixture-model path clustering for tropical cyclone forecasting.

scholarly journals Mixture-Based Path Clustering for Synthesis of ECMWF Ensemble Forecasts of Tropical Cyclone Evolution

2016 ◽  
Vol 144 (9) ◽  
pp. 3301-3320 ◽  
Author(s):  
Prabhani Kuruppumullage Don ◽  
Jenni L. Evans ◽  
Francesca Chiaromonte ◽  
Alex M. Kowaleski
Keyword(s):  
Tropical Cyclone ◽  
Mixture Model ◽  
Cross Validation ◽  
Information Criterion ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Cluster Assignment ◽  
Third Order ◽  
Number Of Clusters ◽  
Regression Mixture

In this article, three tropical cyclones and their 120-h, 50-member ECMWF Integrated Forecasting System (IFS) ensemble track forecasts at 10 initialization times are considered. The IFS forecast tracks are clustered with a regression mixture model, and two traditional diagnostics (the Bayesian information criterion and a measure of strength of cluster assignment) are used to determine the optimal polynomial order and number of clusters to use in the model. In addition, cross-validation versions of the two diagnostics are formulated and computed to further aid in model selection. Both traditional and cross-validation diagnostics suggest that third-order polynomials and five clusters are effective options—although the evidence is less conclusive for the number of clusters than for the polynomial order, and the cross-validation diagnostics favor a smaller number of clusters than the traditional ones. Path clustering of IFS tropical cyclone track forecasts with this third-order polynomial, five-cluster regression mixture model produces interpretable partitions by direction and speed of motion for each of the storms and initialization times considered. Thus, this approach effectively synthesizes the forecast spreads within the IFS into a small number of representative trajectories. Based on how forecasts distribute across clusters, this approach also provides information on the likelihood of each such representative trajectory. If used operationally, this information has the potential to aid forecasters in parsing and quantifying the uncertainty in tropical cyclone track forecasts.

scholarly journals Relationship between the Track and Structural Evolution of Hurricane Sandy (2012) Using a Regional Ensemble

2018 ◽  
Vol 146 (12) ◽  
pp. 4279-4302 ◽  
Author(s):  
Alex M. Kowaleski ◽  
Jenni L. Evans
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Structural Evolution ◽  
Hurricane Sandy ◽  
Weather Research And Forecasting ◽  
Extratropical Transition ◽  
Ensemble Forecasts ◽  
High Predictability ◽  
Regression Mixture ◽  
The Relationship

Abstract An ensemble of 72 Weather Research and Forecasting (WRF) Model simulations is evaluated to examine the relationship between the track of Hurricane Sandy (2012) and its structural evolution. Initial and boundary conditions are obtained from ECMWF and GEFS ensemble forecasts initialized at 0000 UTC 25 October. The 5-day WRF simulations are initialized at 0000 UTC 27 October, 48 h into the global model forecasts. Tracks and cyclone phase space (CPS) paths from the 72 simulations are partitioned into 6 clusters using regression mixture models; results from the 4 most populous track clusters are examined. The four analyzed clusters vary in mean landfall location from southern New Jersey to Maine. Extratropical transition timing is the clearest difference among clusters; more eastward clusters show later Sandy–midlatitude trough interaction, warm seclusion formation, and extratropical transition completion. However, the intercluster variability is much smaller when examined relative to the landfall time of each simulation. In each cluster, a short-lived warm seclusion forms and contracts through landfall while lower-tropospheric potential vorticity concentrates at small radii. Despite the large-scale similarity among the clusters, relevant intercluster differences in landfall-relative extratropical transition are observed. In the easternmost cluster the Sandy–trough interaction is least intense and the warm seclusion decays the most by landfall. In the second most eastward cluster Sandy retains the most intact warm seclusion at landfall because of a slightly later (relative to landfall) and weaker trough interaction compared to the two most westward clusters. Nevertheless, the remarkably similar large-scale evolution of Sandy among the four clusters indicates the high predictability of Sandy’s warm seclusion extratropical transition before landfall.

scholarly journals Application of Adjoint-Derived Forecast Sensitivities to the 24–25 January 2000 U.S. East Coast Snowstorm

2005 ◽  
Vol 133 (11) ◽  
pp. 3148-3175 ◽  
Author(s):  
Daryl T. Kleist ◽  
Michael C. Morgan
Keyword(s):  
Response Function ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Vertical Motion ◽  
Initial Condition ◽  
Response Functions ◽  
Eastern United States ◽  
Surface Cyclone

Abstract The 24–25 January 2000 eastern United States snowstorm was noteworthy as operational numerical weather prediction (NWP) guidance was poor for lead times as short as 36 h. Despite improvements in the forecast of the surface cyclone position and intensity at 1200 UTC 25 January 2000 with decreasing lead time, NWP guidance placed the westward extent of the midtropospheric, frontogenetically forced precipitation shield too far to the east. To assess the influence of initial condition uncertainties on the forecast of this event, an adjoint model is used to evaluate forecast sensitivities for 36- and 48-h forecasts valid at 1200 UTC 25 January 2000 using as response functions the energy-weighted forecast error, lower-tropospheric circulation about a box surrounding the surface cyclone, 750-hPa frontogenesis, and vertical motion. The sensitivities with respect to the initial conditions for these response functions are in general very similar: geographically isolated, maximized in the middle and lower troposphere, and possessing an upshear vertical tilt. The sensitivities are maximized in a region of enhanced low-level baroclinicity in the vicinity of the surface cyclone’s precursor upper trough. However, differences in the phase and structure of the gradients for the four response functions are evident, which suggests that perturbations could be constructed to alter one response function but not necessarily the others. Gradients of the forecast error response function with respect to the initial conditions are used in an iterative procedure to construct initial condition perturbations that reduce the forecast error. These initial condition perturbations were small in terms of both spatial scale and magnitude. Those initial condition perturbations that were confined primarily to the midtroposphere grew rapidly into much larger amplitude upper-and-lower tropospheric perturbations. The perturbed forecasts were not only characterized by reduced final time forecast error, but also had a synoptic evolution that more closely followed analyses and observations.

scholarly journals 2-Way k-Means as a Model for Microbiome Samples

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Weston J. Jackson ◽  
Ipsita Agarwal ◽  
Itsik Pe’er
Keyword(s):  
Unsupervised Learning ◽  
16S Rdna ◽  
Mixture Model ◽  
Vaginal Microbiome ◽  
16S Rdna Sequencing ◽  
Sequencing Data ◽  
Cluster Assignment ◽  
Rdna Sequencing

Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.

scholarly journals Identifying mesoscale high-wind features within extratropical cyclones

2021 ◽  
Author(s):  
Lea Eisenstein ◽  
Peter Knippertz ◽  
Joaquim G. Pinto
Keyword(s):  
Forecast Error ◽  
Grid Point ◽  
Potential Temperature ◽  
Conveyor Belt ◽  
Extratropical Cyclones ◽  
Forecast Errors ◽  
Wind Gust ◽  
High Wind ◽  
Exclusion Criteria ◽  
Surface Observations

<p>Extratropical cyclones cause strong winds and heavy precipitation events and are therefore one of the most dangerous natural hazards in Europe. The strongest winds within these cyclones are mostly connected to four mesoscale dynamical features: the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), cold-frontal convective features (CFC) and the sting jet (SJ). While all four have high wind gust speeds in common, the timing, location and some further characteristics typically differ and hence likely also the forecast errors occurring in association with them.</p><p>Here we present an objective identification approach for the four features named above based on their most important characteristics in wind, rainfall, pressure and temperature evolution. The main motivations for this are to generate a climatology for Central Europe, to analyse forecast error specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve, we ideally want to be able to identify the features in surface observations and in forecasts in a consistent way.</p><p>Based on a dataset of hourly observations over Europe and nine windstorm cases during the winter seasons 2017/18, 2018/19 and 2019/20, it became apparent that mean sea-level pressure tendency, potential temperature tendency, change in wind direction and precipitation (all one-hourly) are most important for the distinction between the WJ and CFC. Further adding the time (relative to storm evolution) and location (relative to the storm centre) of occurrence helps to identify the CJ. Ultimately, the identification of each feature is based on a score on a scale from 0 to 10 that reflects the various criteria for a station or grid point. Additionally, exclusion criteria for each feature are defined to rule out locations that meet some criteria (and thus have a positive score) but strongly violate others. Finally, smooth contours are drawn around each feature to define their spatial extent.</p><p>While the distinction between WJ and CFC seems to work reliably, the identification of CJ remains ambiguous and needs further parameters and exclusion criteria to avoid too large areas and overlap with other features. Furthermore, SJ and CJ are very difficult to distinguish based on surface observations alone and are therefore taken together for this preliminary analysis. Once the definition of criteria is finalised, a climatology will be compiled based on observations and the German COSMO model and forecast errors analysed for said model.</p>

scholarly journals Characterization of transport regimes and the polar dome during Arctic spring and summer using in situ aircraft measurements

2019 ◽  
Vol 19 (23) ◽  
pp. 15049-15071
Author(s):  
Heiko Bozem ◽  
Peter Hoor ◽  
Daniel Kunkel ◽  
Franziska Köllner ◽  
Johannes Schneider ◽  
...  
Keyword(s):  
Potential Temperature ◽  
Lower Troposphere ◽  
High Arctic ◽  
Atmospheric Composition ◽  
The Arctic ◽  
Trace Gas ◽  
Chemical Compositions ◽  
Second Phase ◽  
Air Masses ◽  
Mixing Ratios

Abstract. The springtime composition of the Arctic lower troposphere is to a large extent controlled by the transport of midlatitude air masses into the Arctic. In contrast, precipitation and natural sources play the most important role during summer. Within the Arctic region sloping isentropes create a barrier to horizontal transport, known as the polar dome. The polar dome varies in space and time and exhibits a strong influence on the transport of air masses from midlatitudes, enhancing transport during winter and inhibiting transport during summer. We analyzed aircraft-based trace gas measurements in the Arctic from two NETCARE airborne field campaigns (July 2014 and April 2015) with the Alfred Wegener Institute Polar 6 aircraft, covering an area from Spitsbergen to Alaska (134 to 17∘ W and 68 to 83∘ N). Using these data we characterized the transport regimes of midlatitude air masses traveling to the high Arctic based on CO and CO2 measurements as well as kinematic 10 d back trajectories. We found that dynamical isolation of the high Arctic lower troposphere leads to gradients of chemical tracers reflecting different local chemical lifetimes, sources, and sinks. In particular, gradients of CO and CO2 allowed for a trace-gas-based definition of the polar dome boundary for the two measurement periods, which showed pronounced seasonal differences. Rather than a sharp boundary, we derived a transition zone from both campaigns. In July 2014 the polar dome boundary was at 73.5∘ N latitude and 299–303.5 K potential temperature. During April 2015 the polar dome boundary was on average located at 66–68.5∘ N and 283.5–287.5 K. Tracer–tracer scatter plots confirm different air mass properties inside and outside the polar dome in both spring and summer. Further, we explored the processes controlling the recent transport history of air masses within and outside the polar dome. Air masses within the springtime polar dome mainly experienced diabatic cooling while traveling over cold surfaces. In contrast, air masses in the summertime polar dome were diabatically heated due to insolation. During both seasons air masses outside the polar dome slowly descended into the Arctic lower troposphere from above through radiative cooling. Ascent to the middle and upper troposphere mainly took place outside the Arctic, followed by a northward motion. Air masses inside and outside the polar dome were also distinguished by different chemical compositions of both trace gases and aerosol particles. We found that the fraction of amine-containing particles, originating from Arctic marine biogenic sources, is enhanced inside the polar dome. In contrast, concentrations of refractory black carbon are highest outside the polar dome, indicating remote pollution sources. Synoptic-scale weather systems frequently disturb the transport barrier formed by the polar dome and foster exchange between air masses from midlatitudes and polar regions. During the second phase of the NETCARE 2014 measurements a pronounced low-pressure system south of Resolute Bay brought inflow from southern latitudes, which pushed the polar dome northward and significantly affected trace gas mixing ratios in the measurement region. Mean CO mixing ratios increased from 77.9±2.5 to 84.9±4.7 ppbv between these two regimes. At the same time CO2 mixing ratios significantly decreased from 398.16 ± 1.01 to 393.81 ± 2.25 ppmv. Our results demonstrate the utility of applying a tracer-based diagnostic to determine the polar dome boundary for interpreting observations of atmospheric composition in the context of transport history.

scholarly journals Dry Intrusions: Lagrangian Climatology and Dynamical Impact on the Planetary Boundary Layer

2017 ◽  
Vol 30 (17) ◽  
pp. 6661-6682 ◽  
Author(s):  
Shira Raveh-Rubin
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Potential Temperature ◽  
Storm Track ◽  
Lower Troposphere ◽  
Western Coast ◽  
Moisture Fluxes ◽  
Quantitative Understanding ◽  
America South ◽  
Heat And Moisture

Dry-air intrusions (DIs) are dry, deeply descending airstreams from the upper troposphere toward the planetary boundary layer (PBL). The significance of DIs spans a variety of aspects, including the interaction with convection, extratropical cyclones and fronts, the PBL, and extreme surface weather. Here, a Lagrangian definition for DI trajectories is used and applied to ECMWF interim reanalysis (ERA-Interim) data. Based on the criterion of a minimum descent of 400 hPa during 48 h, a first global Lagrangian climatology of DI trajectories is compiled for the years 1979–2014, allowing quantitative understanding of the occurrence and variability of DIs, as well as the dynamical and thermodynamical interactions that determine their impact. DIs occur mainly in winter. While traveling equatorward from 40°–50° latitude, DIs typically reach the lower troposphere (with maximum frequencies of ~10% in winter) in the storm-track regions, as well as over the Mediterranean Sea, Arabian Sea, and eastern North Pacific, off the western coast of South America, South Africa, and Australia, and across the Antarctic coast. The DI descent is nearly adiabatic, with a mean potential temperature decrease of 3 K in two days. Relative humidity drops strongly during the first descent day and increases in the second day, because of mixing into the moist PBL. Significant destabilization of the lower levels occurs beneath DIs, accompanied by increased 10-m wind gusts, intense surface heat and moisture fluxes, and elevated PBL heights. Interestingly, only 1.2% of all DIs are found to originate from the stratosphere.

A computer program to calculate Hubert and Arabie's adjusted rand index

1996 ◽  
Vol 13 (1) ◽  
pp. 169-172 ◽  
Author(s):  
Robert Saltstone ◽  
Ken Stange
Keyword(s):  
Computer Program ◽  
Rand Index ◽  
Adjusted Rand Index
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