Intensity Statistics Research Articles

Interdecadal changes in Southern Hemisphere winter explosive storms and Southern Australian rainfall

Interdecadal changes, over the last seventy years, in winter synoptic weather systems that grow and decay in the extratropical storm tracks of the Southern Hemisphere are investigated. The statistics of the intensity of growing and decaying eddies, and their preferred structures and growth rates, are determined from 6-hourly lower-tropospheric reanalysis data for high-pass filtered (period less than 4 days) and band-pass filtered (period between 4 and 8 days) disturbances. A new data-driven methodology is used in which the perturbations are spectrally analysed in both space and time, their growth rates are calculated and statistics are accumulated for growing and decaying instabilities. Rainfall variability in Southern Australia is found to be closely related to the changing statistics of weather system intensity and to interannual and decadal variability in indices or predictors that characterize local, hemispheric or global aspects of the atmospheric circulation. In investigations into the mid-1970s drying of southwestern Western Australia, the Australian Millennium Drought and Southern Australian rainfall anomalies of the early twenty-first century it is found that decadal changes in rainfall are most related to the intensity of the fast growing weather systems. Two new indices, the Subtropical Atmospheric Jet (SAJ) index and the Southern Ocean Regional Dipole (SORD) index, are introduced and shown to have some skill in characterizing aspects of Southern Australian winter rainfall variability particularly on decadal timescales.

Projected changes of typhoon intensity in a regional climate model: Development of a machine learning bias correction scheme

AbstractA machine learning‐based bias correction scheme was developed to adjust the simulated Western North Pacific typhoon intensity in a 25‐km regional climate model (RCM). The bias correction scheme, MLERA, consists of a hybrid neural network, which takes modelled atmospheric and oceanic conditions near the storm centre as input. We use air temperature, specific humidity, and relative vorticity at 300 hPa, geopotential height at 700 hPa, wind speed at 850 hPa, sea‐level pressure, 10‐m wind speed, and total air‐sea fluxes. Those predictors are selected using least absolute shrinkage and selection operator (Lasso) algorithm and principal component analysis (PCA). Because there is no ‘ground truth’ for RCM simulated storms, we train and test MLERA using ERA‐Interim reanalysis and best‐track data. The intensity statistics estimated from MLERA match well with those from observations. MLERA, when applied to RCM history base period, increases the likelihood of simulated typhoons (with wind speed >33 m/s) from 40.8 to 73.9% in the direct simulation. Meanwhile, the RCM itself produces no C3+ storms (wind speed >50 m/s) in both RCP4.5 and RCP8.5 scenarios, while MLERA significantly increases those storms. MLERA suggests an increasing trend of the frequency of violent typhoons from near‐term (2031–2060) to long‐term (2069–2098). Such an increase is consistent with our understanding that a warming climate will increase the storm intensity. The present study shows the potential of the machine learning technique for bias correcting cyclone intensity in RCMs. Furthermore, the proposed algorithm could also be applied to the next generation of high‐resolution global climate models, which may have a spatial grid spacing close to today's RCMs.

Intensity and damage statistics of the September 19, 2017 Mexico earthquake: Influence of soft story and corner asymmetry on the damage reported during the earthquake

A destructive intraslab earthquake occurred in Mexico City on September 19, 2017 (Mw 7.1), causing significant damage and hundreds of human losses not only in the epicentral area, but also in the States of Morelos, Puebla, Mexico and in Mexico City. Only in Mexico City itself, around 230 people died, and more than 40 buildings collapsed. The intensities recorded in some lakebed areas of the city, especially in zones with soil periods around 1.5 s, were relatively high, even surpassing spectral values of 1.0 g; the vertical component, due to the proximity of the earthquake, was unusually high for Mexico City. The 2017 earthquake raised questions critical to understanding the city’s seismic vulnerability and resilience, and they are partly answered in this article. Using 77 accelerometric stations, the amplification pattern of the seismic intensities is characterized as well as the correlations of buildings structural characteristics with the site effects. A comprehensive statistical analysis of the damages is shown to analyze and understand the structural behavior of damaged buildings. It is including not only the structural types and the year of construction, but also the main structural problems identified (structural pathologies), such as irregularities, both in elevation and plan, soft story, and corner effect. The building damage database was constructed with 2125 reports of buildings carried out by universities and engineering associations after the earthquake, of which 543 had severe damage. It is also included the information of all buildings with no damage in the city thanks to the cadastral information provided by the Mexico City government, and post-earthquake inspections and visual inspections using Google Street View. A full study of selected neighborhoods, which compares similar buildings with and without damage, is included, yielding relevant statistical information on which pathologies cause more damage and even collapses.

Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.

Bayesian Edge Detector Using Deformable Directivity-Aware Sampling Window.

Conventional image entropy merely involves the overall pixel intensity statistics which cannot respond to intensity patterns over spatial domain. However, spatial distribution of pixel intensity is definitely crucial to any biological or computer vision system, and that is why gestalt grouping rules involve using features of both aspects. Recently, the increasing integration of knowledge from gestalt research into visualization-related techniques has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. This paper presents a Bayesian edge detector called GestEdge, which is effective in detecting gestalt edges, especially useful for forming object boundaries as perceived by human eyes. GestEdge is characterized by employing a directivity-aware sampling window or mask that iteratively deforms to probe or explore the existence of principal direction of sampling pixels; when convergence is reached, the window covers pixels best representing the directivity in compliance with the similarity and proximity laws in gestalt theory. During the iterative process based on the unsupervised Expectation-Minimization (EM) algorithm, the shape of the sampling window is optimally adjusted. Such a deformable window allows us to exploit the similarity and proximity among the sampled pixels. Comparisons between GestEdge and other edge detectors are shown to justify the effectiveness of GestEdge in extracting the gestalt edges.

Characteristics and thermodynamics of Sahelian heatwaves analysed using various thermal indices

Prolonged periods of extreme heat also known as heatwaves are a growing concern in a changing climate. Over the Sahel, a hot and semi-arid region in West Africa, they are still relatively poorly understood and managed. In this research, five multivariate thermal indices derived from the ERA5 database were used to characterize Sahelian heatwaves for statistical analysis and as a sampling basis to investigate their underlying thermodynamic causes. Results show that on average most locations in the Sahel suffer from one or two heatwaves a year lasting 3–5 days but with severe magnitude. The eastern Sahel is more at risk than the west, experiencing more frequent and longer lasting events. Despite similar statistics of intensity, duration and frequency across the heatwave indices, for a given diurnal phase, there is surprisingly low agreement in the timing of events. Furthermore daytime and nighttime heatwaves have little synchronicity. In terms of associated thermodynamic processes, heat advection and the greenhouse effect of moisture are identified as the main causes of Sahelian heatwaves. The processes are nevertheless sensitive to the indices, consequence of the distinctness of their respective samples. Therefore attention should be given to the choice of either index in operational monitoring and forecasting of heatwaves. This will allow to effectively target different exposed socio-economic groups and resultantly enhance the efficiency of early warning systems.

Nonstationary Intensity Statistics in Diffusive Waves.

It is a long-standing belief that, in the diffusion regime, the intensity statistics is always stationary and its probability distribution follows a negative exponential decay. Here, we demonstrate that, in fact, in reflection from strong disordered media, the intensity statistics changes through different stages of the diffusion. We present a statistical model that describes this nonstationary property and takes into account the evolving balance between recurrent scattering and near field coupling. The predictions are further verified by systematic experiments in the optical regime. This statistical nonstationary is akin to the nonequilibrium but steady-state diffusion of particulate systems.

Effects of source spatial partial coherence on intensity statistics of optical beams in mono-static turbulent channels.

We investigate, via both experimental measurements and wave-optics computer simulations, the statistical characteristics of the fluctuating intensity, such as the average intensity, the beam wander and the scintillation index, of the Gaussian Schell-model (GSM) beams on passing through double-pass, monostatic turbulence channels with either a retro-reflector (RR) or a flat mirror (FM). Our experimental results reveal that the enhanced backscatter (EBS) gradually weakens as the spatial coherence of the GSM source decreases, and eventually disappears for the sufficiently low source spatial coherence states. The r.m.s beam wander remains practically invariant with the variation of the source coherence width in the range from 0.2 to 6.0 mm both in the case of the RR and the FM, which formed the RR case being much smaller. In addition, it is found that the long-term scintillation index of the untracked beam with the RR is smaller than that with the FM, while the situation is reversed for the short-term scintillation index of the tracked beam. In both cases, the scintillation index decreases as the spatial coherence of the GSM source decreases. The obtained computer simulation results agree reasonably well with the experimental results. In addition, the effects of spatial coherence on statistical characteristics of the GSM beams along a 1 km propagation distance through the double-pass monostatic turbulence are also investigated using wave-optics simulation. We also carry out evaluation and comparison of the intensity probability density functions for the RR and FM cases and for various source coherence states that are of utmost importance for free-space optical communications in retro-reflection modulation regime. In addition, our findings will be beneficial for the development of remote sensing and directed energy laser applications in the presence of air turbulence.

Machine learning analysis of rogue solitons in supercontinuum generation

Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed “rogue wave”-like statistics of the spectral intensity on the long-wavelength edge of the supercontinuum, linked to the generation of a small number of “rogue solitons” with extreme red-shifts. Whilst the statistical properties of rogue solitons can be conveniently measured in the spectral domain using the real-time dispersive Fourier transform technique, we cannot use this technique to determine any corresponding temporal properties since it only records the spectral intensity and one loses information about the spectral phase. And direct temporal characterization using methods such as the time-lens has resolution of typically 100’s of fs, precluding the measurement of solitons which possess typically much shorter durations. Here, we solve this problem by using machine learning. Specifically, we show how supervised learning can train a neural network to predict the peak power, duration, and temporal walk-off with respect to the pump pulse position of solitons at the edge of a supercontinuum spectrum from only the supercontinuum spectral intensity without phase information. Remarkably, the network accurately predicts soliton characteristics for a wide range of scenarios, from the onset of spectral broadening dominated by pure modulation instability to near octave-spanning supercontinuum with distinct rogue solitons.

Experimental study of speckle patterns generated by low-coherence semiconductor laser light.

Speckle is a wave interference phenomenon that has been studied in various fields, including optics, hydrodynamics, and acoustics. Speckle patterns contain spectral information of the interfering waves and of the scattering medium that generates the pattern. Here, we study experimentally the speckle patterns generated by the light emitted by two types of semiconductor lasers: conventional laser diodes, where we induce low-coherence emission by optical feedback or by pump current modulation, and coupled nanolasers. In both cases, we analyze the intensity statistics of the respective speckle patterns to inspect the degree of coherence of the light. We show that the speckle analysis provides a non-spectral way to assess the coherence of semiconductor laser light.

PolInSAR Complex Coherence Nonlocal Estimation Using Shape-Adaptive Patches Matching and Trace-Moment-Based NLRB Estimator

The traditional nonlocal estimations have been demonstrated to be effective and widely used in polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, there still exist some problems about two key steps: 1) in the homogeneous pixels selection step, the regular square (RS) patches matching strategy shows the limited performance in textured area and 2) in the central pixel value estimation from the selected pixels, the well-known Lee estimator, which only uses the intensity statistic, tends to be unstable. To overcome these restrictions, we put forward two robust strategies and then propose an improved PolInSAR complex coherence nonlocal estimation: 1) the shape-adaptive (SA) patch is utilized for flexibly matching the similar pixels in a large search window, which is constructed by combining the likelihood ratio test (LRT) and the region growing (RG) algorithm and 2) the trace-moment-based nonlocal reduced bias (TMB-NLRB) estimator is employed, which considers the interchannel correlations and evaluates more accurately the homogeneity level between the selected pixels. The denoising effect of both strategies is quantitatively analyzed on the simulated data set, and the proposed algorithm is compared with classical estimation algorithms on a TerraSAR-X/TanDEM-X PolInSAR data set. These experimental results show that the proposed method provides better performance in speckle reduction, detail preservation, and complex coherence estimation.

Mesoscopic Limit Cycles in Coupled Nanolasers.

Two coupled nanolasers exhibit a mode switching transition, theoretically described by mode beating limit cycle oscillations. Their decay rate is vanishingly small in the thermodynamic limit, i.e., when the spontaneous emission noise tends to zero. We provide experimental statistical evidence of mesoscopic limit cycles (∼10^{3} intracavity photons). Specifically, we show that the order parameter quantifying the limit cycle amplitude can be reconstructed from the mode intensity statistics. We observe a maximum of the averaged amplitude at the mode switching, accounting for limit cycle oscillations. We finally relate this maximum to a dip of mode cross-correlations, reaching a minimum of g_{ij}^{(2)}=2/3, which we show to be a mesoscopic limit. Coupled nanolasers are thus an appealing test bed for the investigation of spontaneous breaking of time translation symmetry in the presence of strong quantum fluctuations.

Automated noise estimation in polarization-sensitive optical coherence tomography.

Advanced signal reconstruction in polarization-sensitive optical coherence tomography (OCT) frequently relies on an accurate determination of the signal noise floor. However, current methods for evaluating the noise floor are often impractical and subjective. Here we present a method using the degree of polarization uniformity and known speckle intensity statistics to model and estimate the OCT noise floor automatically. We establish the working principle of our method with a series of phantom experiments and demonstrate the robustness of our noise estimation method across different imaging systems and applications in vivo.

Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels

The wake of upstream wind turbine is known to affect the operation of downstream turbines and the overall efficiency of the wind farm. Wind tunnel experiments provide relevant information for understanding and modeling the wake and its dependency on the turbine operating conditions. There are always two main driving modes to operate turbines in a wake experiment: (1) the turbine rotor is driven and controlled by a motor, defined active driving mode; (2) the rotor is driven by the incoming wind and subject to a drag torque, defined passive driving mode. The effect of the varying driving mode on the turbine wake is explored in this study. The mean wake velocities, turbulence intensities, skewness and kurtosis of the velocity time-series estimated from hot-wire anemometry data, were obtained at various downstream locations, in a uniform incoming flow wind tunnel and in an atmospheric boundary layer wind tunnel. The results show that there is not a significant difference in the mean wake velocity between these two driving modes. An acceptable agreement is observed in the comparison of wake turbulence intensity and higher-order statistics in the two wind tunnels.

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.

To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade. Seventy-one ccRCC patients (31 low grade and 40 high grade) were included in this study. Tumors were manually segmented on CT images followed by the application of three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) on delineated tumor volumes. Overall, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association between each feature and the histological condition. Multivariate analysis involved the use of machine learning (ML) algorithms and the following three feature selection algorithms: the least absolute shrinkage and selection operator, Student's t test, and minimum Redundancy Maximum Relevance. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under the receiver operating characteristic curve (AUC) metric. The univariate analysis demonstrated that among the different image sets, 128 bin-discretized images have statistically significant different texture parameters with a mean AUC of 0.74 ± 3 (q value < 0.05). The three ML-based classifiers showed proficient discrimination between high and low-grade ccRCC. The AUC was 0.78 for logistic regression, 0.62 for random forest, and 0.83 for the SVM model, respectively. CT radiomic features can be considered as a useful and promising noninvasive methodology for preoperative evaluation of ccRCC Fuhrman grades.

A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets.

Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm’s outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm’s full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools.

An Environment‐Dependent Probabilistic Tropical Cyclone Model

AbstractThe Princeton environment‐dependent probabilistic tropical cyclone (PepC) model is developed for generating synthetic tropical cyclones (TCs) to support TC risk assessment. PepC consists of three components: a hierarchical Poisson genesis model, an analog‐wind track model, and a Markov intensity model. The three model components are dependent on environmental variables that vary with the climate, including potential intensity, advection flow, vertical wind shear, relative humidity, and ocean‐cooling parameters. The present model is developed for the North Atlantic Basin. The three model components and the integrated model are verified against observations using out‐of‐sample testing. The model can generally capture the TC climatology and reproduce statistics of TC genesis, movement, rapid intensification, and lifetime maximum intensity, as well as local landfall frequency and intensity. It can be coupled with climate models and TC hazard models to quantify TC‐related wind, surge, and rainfall risks under various climate conditions. The modeling framework can be further improved when more relevant environmental variables are identified and become available in climate model outputs.

Studying the Anisotropy of Compressible Magnetohydrodynamic Turbulence by Synchrotron Polarization Intensity

Abstract Based on statistical analysis of synchrotron polarization intensity, we study the anisotropic properties of compressible magnetohydrodynamic (MHD) turbulence. The second-order normalized structure function, quadrupole ratio modulus, and anisotropic coefficient are synergistically used to characterize the anisotropy of the polarization intensity. On the basis of predecomposition data cubes, we first explore the anisotropy of the polarization intensity in different turbulence regimes and find that the most significant anisotropy occurs in the sub-Alfvénic regime. Using postdecomposition data cubes in this regime, we then study the anisotropy of the polarization intensity from Alfvén, slow, and fast modes. The statistics of the polarization intensity from Alfvén and slow modes demonstrate the significant anisotropy, while the statistics of the polarization intensity from fast modes show isotropic structures. This is consistent with earlier results provided in Cho &amp; Lazarian. As a result, both quadrupole ratio modulus and anisotropic coefficient for polarization intensities can quantitatively recover the anisotropy of underlying compressible magnetohydrodynamic turbulence. The synergistic use of the two methods helps enhance the reliability of the magnetic field measurement.

Two-way ANOVA gage R&amp;R working example applied to speckle intensity statistics due to different random vertical surface roughness characteristics using the Fresnel diffraction integral

Two-way ANOVA gage R&amp;R working example applied to speckle intensity statistics due to different random vertical surface roughness characteristics using the Fresnel diffraction integral

Image-based deterioration assessment of concrete

The present study quantifies concrete deterioration due to chemical attack using a simple visible-spectrum based digital image processing technique. Concrete cubes of M30 grade were cast for durability based studies. Concrete specimens were exposed to deterioration due to acid (HCl, H2SO4) chloride (NaCl) and sulphate (MgSO4) at 5% concentration for a period of 3, 7, 14 and 28 days. After the requisite period of exposure each face of the individual cube is photographed and the corresponding statistics of grey scale intensity are obtained. Digital image analysis enables development of correlation with greyscale intensities of various established deterioration measures such as mass loss, dimension loss and strength loss. Both the intensity of grey scale as well as mean grey scale are found to be good predictors of the extent of deterioration in concrete exposed to HCl and MgSO4 and moderate predictors in case H2SO4 and NaCl, The mean grey scale intensities enable identification of exposure and deterioration due to different chemicals.