Improved Tropical-Cyclone Flight-Level Wind Estimates Using Routine Infrared Satellite Reconnaissance

2015 ◽  
Vol 54 (2) ◽  
pp. 463-478 ◽  
Author(s):  
John A. Knaff ◽  
Scott P. Longmore ◽  
Robert T. DeMaria ◽  
Debra A. Molenar
Keyword(s):  
Tropical Cyclone ◽  
Principal Components ◽  
Wind Field ◽  
Motion Vector ◽  
Principal Component ◽  
Principal Component Method ◽  
Tangential Wind ◽  
Flight Level ◽  
Polar Grid ◽  
Single Field

AbstractA new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.

scholarly journals Influence of Cloud–Radiative Forcing on Tropical Cyclone Structure

2014 ◽  
Vol 71 (5) ◽  
pp. 1644-1662 ◽  
Author(s):  
Yizhe Peggy Bu ◽  
Robert G. Fovell ◽  
Kristen L. Corbosiero
Keyword(s):  
Tropical Cyclone ◽  
Wind Field ◽  
Radiative Forcing ◽  
Outer Core ◽  
Shortwave Radiation ◽  
Convective Heating ◽  
Cloud Radiative Forcing ◽  
Radiative Processes ◽  
Tangential Wind ◽  
Radial Outflow

Abstract The authors demonstrate how and why cloud–radiative forcing (CRF), the interaction of hydrometeors with longwave and shortwave radiation, can influence tropical cyclone structure through “semi idealized” integrations of the Hurricane Weather Research and Forecasting model (HWRF) and an axisymmetric cloud model. Averaged through a diurnal cycle, CRF consists of pronounced cooling along the anvil top and weak warming through the cloudy air, which locally reverses the large net cooling that occurs in the troposphere under clear-sky conditions. CRF itself depends on the microphysics parameterization and represents one of the major reasons why simulations can be sensitive to microphysical assumptions. By itself, CRF enhances convective activity in the tropical cyclone’s outer core, leading to a wider eye, a broader tangential wind field, and a stronger secondary circulation. This forcing also functions as a positive feedback, assisting in the development of a thicker and more radially extensive anvil than would otherwise have formed. These simulations clearly show that the weak (primarily longwave) warming within the cloud anvil is the major component of CRF, directly forcing stronger upper-tropospheric radial outflow as well as slow, yet sustained, ascent throughout the outer core. In particular, this ascent leads to enhanced convective heating, which in turn broadens the wind field, as demonstrated with dry simulations using realistic heat sources. As a consequence, improved tropical cyclone forecasting in operational models may depend on proper representation of cloud–radiative processes, as they can strongly modulate the size and strength of the outer wind field that can potentially influence cyclone track as well as the magnitude of the storm surge.

scholarly journals Using the Method of Principal Components and Discriminant Analysis with Aim of Simplifying the Selection of Analogue Object for New Area

2021 ◽  
Vol 133 (2) ◽  
pp. 42-46
Author(s):  
R. I. Bulatov ◽  
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Principal Component ◽  
Physical Parameters ◽  
Principal Component Method ◽  
Component Method ◽  
Method Of Principal Components ◽  
New Development ◽  
Selection Of

The article discusses the main geo-physical parameters from 66 development objects. This data is analyzed using component and discriminant analysis methods. Using the principal component method, relatively homogeneous analog groups are distinguished, and discriminant analysis is used to derive the discriminant function and its constants. Based on the values of this function and constants, the new development object is assigned to the analog group.

The Roles of an Expanding Wind Field and Inertial Stability in Tropical Cyclone Secondary Eyewall Formation

2012 ◽  
Vol 69 (9) ◽  
pp. 2621-2643 ◽  
Author(s):  
Christopher M. Rozoff ◽  
David S. Nolan ◽  
James P. Kossin ◽  
Fuqing Zhang ◽  
Juan Fang
Keyword(s):  
Energy Efficiency ◽  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Wind Field ◽  
Temperature Structure ◽  
Latent Heating ◽  
Vortex Model ◽  
Secondary Eyewall Formation ◽  
Tangential Wind ◽  
Transverse Circulation

Abstract The Weather and Research and Forecasting Model (WRF) is used to simulate secondary eyewall formation (SEF) in a tropical cyclone (TC) on the β plane. The simulated SEF process is accompanied by an outward expansion of kinetic energy and the TC warm core. An absolute angular momentum budget demonstrates that this outward expansion is predominantly a symmetric response to the azimuthal-mean and wavenumber-1 components of the transverse circulation. As the kinetic energy expands outward, the kinetic energy efficiency in which latent heating can be retained as local kinetic energy increases near the developing outer eyewall. The kinetic energy efficiency associated with SEF is examined further using a symmetric linearized, nonhydrostatic vortex model that is configured as a balanced vortex model. Given the symmetric tangential wind and temperature structure from WRF, which is close to a state of thermal wind balance above the boundary layer, the idealized model provides the transverse circulation associated with the symmetric latent heating and friction prescribed from WRF. In a number of ways, this vortex response matches the azimuthal-mean secondary circulation in WRF. These calculations suggest that sustained azimuthal-mean latent heating outside of the primary eyewall will eventually lead to SEF. Sensitivity experiments with the balanced vortex model show that, for a fixed amount of heating, SEF is facilitated by a broadening TC wind field.

Contribution to Creation of Complex System Macrosituations

2002 ◽  
Vol 6 (2) ◽  
pp. 79-83
Author(s):  
Eva Ocelíková ◽  
◽  
Ladislav Madarász
Keyword(s):  
Complex System ◽  
Decision Making ◽  
Linear Combination ◽  
Principal Components ◽  
Dimensional Space ◽  
Principal Component ◽  
Multidimensional Data ◽  
Main Subject ◽  
Principal Component Method ◽  
Technical Realization

This paper deals with the creation of multidimensional data classes – macrosituations – by decreasing their dimension. A large number of monitored attributes in examined situations in complex systems often complicates technical realization of classification and extends the time needed for providing a decision. It is possible to decrease the dimension of situations and, simultaneously, to not decrease decision-making quality. The main subject relates to a possible approach – the Principal Component Method. The basis of this method lies in finding a linear transformation of original p-dimensional space of attributes into a new p’-dimensional space of attributes where p’≤p. New attributes, called principal components, arise in a suitable linear combination of original attributes and are sorted in descending order based on their variance.

A Machine Learning Approach to Modeling Tropical Cyclone Wind Field Uncertainty

2017 ◽  
Vol 145 (8) ◽  
pp. 3203-3221 ◽  
Author(s):  
Thomas Loridan ◽  
Ryan P. Crompton ◽  
Eugene Dubossarsky
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Tropical Cyclone ◽  
Wind Field ◽  
Full Range ◽  
Wrf Model ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Probabilistic Forecasting ◽  
Wind Fields

Tropical cyclone (TC) risk assessment models and probabilistic forecasting systems rely on large ensembles to simulate the track trajectories, intensities, and spatial distributions of damaging winds from severe events. Given computational constraints associated with the generation of such ensembles, the representation of TC winds is typically based on very simple parametric formulations. Such models strongly underestimate the full range of TC wind field variability and thus do not allow for accurate representation of the risk profile. With this in mind, this study explores the potential of machine learning algorithms as an alternative to current parametric methods. First, a catalog of high-resolution TC wind simulations is assembled for the western North Pacific using the Weather Research and Forecasting (WRF) Model. The simulated wind fields are then decomposed via principal component analysis (PCA) and a quantile regression forest model is trained to predict the conditional distributions of the first three principal component (PC) weights. With this model, predictions can be made for any quantiles in the distributions of the PC weights thereby providing a way to account for uncertainty in the modeled wind fields. By repeatedly sampling the quantile values, probabilistic maps for the likelihood of attaining given wind speed thresholds can be easily generated. Similarly the inclusion of such a model as part of a TC risk assessment framework can greatly increase the range of wind field patterns sampled, providing a broader view of the threat posed by TC winds.

scholarly journals Polarization selection of radar target using the principal component method

2019 ◽  
Vol 30 ◽  
pp. 12003
Author(s):  
A.B. Borzov ◽  
L.V. Labunetc ◽  
K.P. Likhoedenko ◽  
I.V. Muratov ◽  
G.L. Pavlov ◽  
...  
Keyword(s):  
Target Detection ◽  
Principal Components ◽  
Field Experiments ◽  
Principal Component ◽  
Radar Target ◽  
Principal Component Method ◽  
Component Method ◽  
Detection Algorithms ◽  
Method Of Principal Components ◽  
Selection Of

The results of polarization selection of radar targets using the method of principal components according to the results of modeling and field experiments are presented. Target Detection Algorithms Received

scholarly journals Examining Tropical Cyclone Structure and Intensification with the FLIGHT+ Dataset from 1999 to 2012

2017 ◽  
Vol 145 (11) ◽  
pp. 4401-4421 ◽  
Author(s):  
Jonathan Martinez ◽  
Michael M. Bell ◽  
Jonathan L. Vigh ◽  
Robert F. Rogers
Keyword(s):  
Steady State ◽  
Tropical Cyclone ◽  
Composite Structures ◽  
Outer Core ◽  
Intensity Change ◽  
Intensity Increase ◽  
In Situ Data ◽  
Tangential Wind ◽  
Structural Differences ◽  
Flight Level

A comprehensive examination of tropical cyclone (TC) kinematic and thermodynamic structure in the Atlantic basin is created from the Extended Flight Level Dataset for Tropical Cyclones (FLIGHT+, version 1.1). In situ data collected at the 700-hPa flight level by NOAA WP-3D and USAF WC-130 aircraft from 1999 to 2012 are analyzed. A total of 233 azimuthal mean profiles comprising 1498 radial legs are stratified by TC intensity and 12-h intensity change. A matrix of composite structures is created for minor (category 1 and 2) and major (category 3 and above) hurricanes that are intensifying [intensity increase ≥10 kt (12 h)−1], steady state [intensity change between ±5 kt (12 h)−1], and weakening [intensity decrease [Formula: see text] kt (12 h)−1]. Additional considerations to the impacts of age on TC structure are given as well. Axisymmetric radial composites reveal that intensifying TCs have statistically significant structural differences from TCs that are steady state or weakening, but that these differences also depend on the intensity of the TC. Intensifying TCs (both minor and major hurricanes) are characterized by steep tangential wind gradients radially inward of the radius of maximum tangential wind (RMW) that contribute to a ringlike structure of vorticity and inertial stability. Tangential wind structural differences are more pronounced in the eye of minor hurricanes compared to major hurricanes. Intensifying TCs are found to have higher inner- and outer-core moisture compared to steady-state and weakening TCs. Furthermore, intensifying major hurricanes possess drier eyes compared to steady-state and weakening major hurricanes.

scholarly journals Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data

2006 ◽  
Vol 21 (6) ◽  
pp. 990-1005 ◽  
Author(s):  
Kimberly J. Mueller ◽  
Mark DeMaria ◽  
John Knaff ◽  
James P. Kossin ◽  
Thomas H. Vonder Haar
Keyword(s):  
Satellite Data ◽  
Wind Field ◽  
Motion Vector ◽  
Wind Fields ◽  
Wind Structure ◽  
Tangential Wind ◽  
Wind Retrievals ◽  
The One ◽  
Dependent Samples ◽  
Aircraft Data

Abstract Geostationary infrared (IR) satellite data are used to provide estimates of the symmetric and total low-level wind fields in tropical cyclones, constructed from estimations of an azimuthally averaged radius of maximum wind (RMAX), a symmetric tangential wind speed at a radius of 182 km (V182), a storm motion vector, and the maximum intensity (VMAX). The algorithm is derived using geostationary IR data from 405 cases from 87 tropical systems in the Atlantic and east Pacific Ocean basins during the 1995–2003 hurricane seasons that had corresponding aircraft data available. The algorithm is tested on 50 cases from seven tropical storms and hurricanes during the 2004 season. Aircraft-reconnaissance-measured RMAX and V182 are used as dependent variables in a multiple linear regression technique, and VMAX and the storm motion vector are estimated using conventional methods. Estimates of RMAX and V182 exhibit mean absolute errors (MAEs) of 27.3 km and 6.5 kt, respectively, for the dependent samples. A modified combined Rankine vortex model is used to estimate the one-dimensional symmetric tangential wind field from VMAX, RMAX, and V182. Next, the storm motion vector is added to the symmetric wind to produce estimates of the total wind field. The MAE of the IR total wind retrievals is 10.4 kt, and the variance explained is 53%, when compared with the two-dimensional wind fields from the aircraft data for the independent cases.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

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