Spatial Cross-correlation Research Articles

Spatial cross-correlation between physicochemical and microbiological variables at superficial soil with different levels of degradation

Best soil management practices can be achieved by evaluating the spatial cross-correlation variability ofsoil microbiological and physicochemical indicators, in order to comprehend the underlying relations between these mutually dependent properties, being valuable indicators for prospective evaluation of soil resources.The purposes of this study were to measure microbiological and physicochemical variables in superficial soil, spatially cross-correlate and determine spatial causalities between these two families of variables.The spatial variability of pH, gravimetric soil water content, 238U, 232Th, 40K, 137Cs, acid phosphatase, dehydrogenase activity, microbial biomass carbon, mesophilic aerobic bacteria and filamentous fungi was studied at a 13 ha field located in Uruguay.238U and 232Th were both negatively lineally correlated with gravimetric soil water content (−0.42), 137Cs (-0.45 and −0.48 respectively) and dehydrogenase activity (-0.44 and −0.48 respectively). A semivariogram analysis revealed that the best fit model for soil variables was spherical, with moderate to strong spatial dependence. Cross-correlation results suggest that there is an influence factor from spatial interaction. In this sense, gravimetric soil water content connects physicochemical variables with soil biodiversity. Spatially, soil water content is inversely influenced by 232Th and 238U (as indicators of sub-superficial soil), and directly influenced by dehydrogenase activity (indicator of soil microbial activity), revealing soil microbial activity as a possible indicator of water retention in drying soils. The combination of spatial patterns of environmental radionuclides with microbiological indicators of soil quality could represent a valuable integrated approach to assess soil conservation status and further explain the impact of anthropogenic disturbance.

Pulmonary Vein Activity Organization to Determine Atrial Fibrillation Recurrence: Preliminary Data from a Pilot Study

Ablation of pulmonary veins has emerged as a key procedure for normal rhythm restoration in atrial fibrillation patients. However, up to half of ablated Atrial fibrillation (AF) patients suffer recurrences during the first year. In this article, simultaneous intra-atrial recordings registered at pulmonary veins previous to the ablation procedure were analyzed. Spatial cross-correlation and transfer entropy were computed in order to estimate spatial organization. Results showed that, in patients with arrhythmia recurrence, pulmonary vein electrical activity was less correlated than in patients that maintained sinus rhythm. Moreover, correlation function between dipoles showed higher delays in patients with AF recurrence. Results with transfer entropy were consistent with spatial cross-correlation measurements. These results show that arrhythmia drivers located at the pulmonary veins are associated with a higher organization of the electrical activations after the ablation of these sites.

Developing a customized system for generating near real time ground motion ShakeMap of Iran’s earthquakes

ABSTRACT This paper provides a review of the procedure of customizing ShakeMap V4.0 provided by U.S. Geological Survey based on seismic characteristics of Iran. Selecting appropriate GMPEs, adopting a suitable spatial cross-correlation model and proper modeling of site condition are important factors in the context of the ShakeMap algorithm. The present paper technically reviews the aforementioned parameters. In addition, a number of statistical tests were performed to provide the optimum configuration. The initial prototype of the configured ShakeMap has been used to provide the ground motion shaking map in the aftermath of a great earthquake in Iran (since Aug, 2021).

An experimental investigation of a square supersonic jet and impinging jet on an inclined plate

Supersonic free jets and impinging jets are found in many engineering applications, such as short and vertical take-off and landing vehicles, cold gas dynamic spray processes, hot surface cooling mechanisms, and turbomachinery systems. The flow characteristics of a supersonic square jet discharging into the ambient and a supersonic jet impinging on a 45° inclined surface were experimentally investigated for nozzle-pressure-ratios (NPRs) of 4.8 and 5.9. Experimental measurements of impinging jets were acquired for nozzle-to-plate distances of 0.82Dj and 1.8Dj, where Dj is the jet hydraulic diameter. The velocity fields in the central plane of the jet were obtained using planar particle image velocimetry. The flow characteristics of the supersonic jets, including mean velocity and turbulent kinetic energy, were computed from the acquired two-dimensional two-component velocity vector fields, and statistical profiles were compared for different NPRs and nozzle-to-plate distances. For supersonic free jets, the acquired statistical results revealed the presence of multiple shock cells along the streamwise direction. Impinging jet measurements revealed the presence of shock cells in the vicinity of the nozzle outlet, oblique plate shocks near the impingement location, and several tail shocks along the streamwise direction. Spatial turbulent velocity cross correlations were calculated for various points located along the shear layers to investigate the characteristics of turbulent features, such as the shape, orientation, and integral length scales of the studied configurations. In addition, a proper orthogonal decomposition analysis was applied to the instantaneous velocity fields to identify the statistically dominant flow structures that play an important role in the flow field characteristics of supersonic free jets and supersonic impinging jets.

A 32 × 32-Pixel CMOS Imager for Quantum Optics With Per-SPAD TDC, 19.48% Fill-Factor in a 44.64-μm Pitch Reaching 1-MHz Observation Rate

This article reports the design and characterization of a 32 $\times $ 32 single-photon avalanche diode (SPAD) time-resolved image sensor for quantum imaging applications fabricated in a 150-nm CMOS standard technology. A per-SPAD time-to-digital converter (TDC) records the spatial cross correlation functions of a flux of entangled photons. Each 44.64- $\mu \text{m}$ pixel with 19.48% fill-factor features a 210.2-ps resolution, 50-ns (8-bit) range TDC with 1.28-LSB differential and 1.92-LSB integral nonlinearity (DNL/INL). The sensor achieves an observation rate of up to 1 MHz through a current-based mechanism that avoids reading empty frames when the photon rates are low. A row-skipping mechanism detects the absence of SPAD activity in a row to increase the duty cycle. These two features require only three transistors in each pixel. The sensor functionality is demonstrated in a quantum imaging experiment that achieves super-resolution.

Assessing the Spaceborne 183.31-GHz Radiometric Channel Geolocation Using High-Altitude Lakes, Ice Shelves, and SAR Imagery

The goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. We used SSMIS data at 183.31 GHz as a submillimeter proxy to identify the most suitable targets for geolocation error validation in very dry atmospheric conditions, as suggested by radiative transfer modeling. Using a yearly SSMIS data set, three candidates’ landmark targets are selected: 1) high-altitude lakes and high-latitude bays using a coastline reference database and 2) Antarctic ice shelves using coastlines derived from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. Data processing is carried out by using spatial cross correlation methods in the spatial frequency domain and performing a numerical sensitivity analysis to contour displacement. Cloud masking, based on a fuzzy-logic approach, is applied to automatically selected clear-air days. The results show that the average geolocation error is about 6.2 km for mountainous lakes and sea bays and 5.4 km for ice shelves, with a standard deviation of about 2.7 and 2.0 km, respectively. The results are in line with SSMIS previous estimates, whereas annual clear-air days are about 10% for mountainous lakes and sea bays and 18% for ice shelves.

Modeling spatial cross-correlation of multiple ground motion intensity measures (SAs, PGA, PGV, Ia, CAV, and significant durations) based on principal component and geostatistical analyses

Ground motion intensity measures (IMs) were observed to be spatially correlated during past earthquakes. In this article, a new spatial cross-correlation model for a vector-IM, which consists of spectral acceleration (SA) ordinates at 17 periods and six non-SA IMs (e.g. peak ground velocity, Arias intensity, cumulative absolute velocity, and significant durations), is proposed using principal component analysis (PCA) and geostatistical analysis. A total of 3797 ground motion records are selected from the NGA-West2 database for such analyses. PCA is used to transform the spatially correlated within-event residuals into uncorrelated principal components; a permissible function is then proposed to fit the empirical semivariograms calculated by the principal components. It is evident that the proposed model performs well in capturing the spatial variability characteristics of the multiple ground motion IMs. A simple example is presented to illustrate the use of the proposed model in realizing spatially correlated ground motion residuals of multiple IMs. The model developed enables one to simulate spatially cross-correlated IMs over a large area in a rapid way.

A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data

Hierarchical local nonlinear dynamic feature learning is of great importance for soft sensor modeling in process industry. Convolutional neural network (CNN) is an excellent local feature extractor that is suitable for process data representation. In this paper, a dynamic CNN (DCNN) strategy is designed to learn hierarchical local nonlinear dynamic features for soft sensor modeling. In DCNN, each 1D process sample is dynamically augmented into 2D data sample with lagged unlabeled process variables, which contains both spatial cross-correlations and temporal auto-correlations. Then, the convolutional and pooling layers are alternately utilized to extract the local nonlinear spatial–temporal feature from the 2D sample data matrix. Moreover, the principle is analyzed for DCNN on how it can learn the local nonlinear spatial–temporal feature from the network. The effectiveness of proposed DCNN is verified on an industrial hydrocracking process.

Spatial Cross-Correlation Models for Absolute and Relative Spectral Input Energy Parameters Based on Geostatistical Tools

ABSTRACTIn recent years, energy-based seismic design methodology has received increasing attention because it takes into account not only the force and displacement behavior of a structure but also the cumulative damage effect caused by seismic loading. Specifically, as a fundamental parameter, input energy parameters (both absolute and relative measures) are directly related to the cumulative damage potential; therefore, they are commonly used in energy-based seismic design and seismic risk assessment. This study thus proposes new spatial cross-correlation models for absolute and relative elastic input energy parameters, using 2219 ground-motion records selected from 12 earthquake events. The normalized within-event residuals for both absolute and relative measures are first calculated. Semivariogram analysis is then conducted to quantify the spatial correlation of residuals for the input energy parameters at multiple sites and multiple periods. The linear model of coregionalization (LMC) approach is adopted to fit the empirical data; it is observed that the proposed LMC-based function performs reasonably well in capturing the spatial variability of the input energy measures. The influence of regional site conditions on the spatial cross correlation of input energy parameters is also investigated, and generic models are proposed using the averaged standardized coregionalization matrices of 12 events. The spatial cross-correlation models developed for input energy parameters can be used in regional seismic risk assessment within an energy-based framework.

Probabilistic Analysis of Slopes with Linearly Increasing Undrained Shear Strength Using RLEM Approach

Shear strength parameters in naturally alluvial deposits bear variation with depth (non-stationarity). Probabilistic analysis of simple slopes with linearly increasing (mean) undrained shear strength with depth and spatial variability is explored using the 2D random limit equilibrium method (RLEM) along with the non-circular failure surface assumption. A deterministic factor of safety design chart method is extended to express margins of safety as probability of failure for soils with random variability and positive correlation between shear strength parameters and unit weight. The effects of isotropic and anisotropic spatial variability and cross-correlation of soil properties on probability of failure are also investigated. Combinations of low correlation length and positive cross-correlation between strength parameters are shown to reduce the probability of failure. This offers the possibility to bring probabilistic estimates of failure into alignment with expectations of slope stability based on experience with factors of safety. The results of a limited number of RLEM analyses are compared with some other results using the 2D random finite element method (RFEM). The influence of different involved parameters and assumptions, more specifically the nonlinear variation of the undrained cohesion and also the non-circular slip surface, on the embedded uncertainty and probability of failure was elaborated. The research has emphasized the importance of non-circular failure surface assumption along with the consideration of non-stationarity in shear strength parameters.

A Novel 3D UAV Channel Model for A2G Communication Environments Using AoD and AoA Estimation Algorithms

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

Environment from cross-correlations: connecting hot gas and the quenching of galaxies

ABSTRACT The observable properties of galaxies depend on both internal processes and the external environment. In terms of the environmental role, we still do not have a clear picture of the processes driving the transformation of galaxies. The use of proxies for environment (e.g. host halo mass, distance to the Nth nearest neighbour, etc.), as opposed to the real physical conditions (e.g. hot-gas density) may bear some responsibility for this. Here, we propose a new method that directly links galaxies to their local environments, by using spatial cross-correlations of galaxy catalogues with maps from large-scale structure surveys [e.g. thermal Sunyaev–Zel’dovich (tSZ) effect, diffuse X-ray emission, weak lensing of galaxies, or the cosmic microwave background (CMB)]. We focus here on the quenching of galaxies and its link to local hot gas properties. Maps of galaxy overdensity and quenched fraction excess are constructed from volume-limited Sloan Digital Sky Survey (SDSS) catalogues, which are cross-correlated with tSZ effect and X-ray maps from Planck and ROSAT, respectively. Strong signals out to Mpc scales are detected for most cross-correlations and are compared to predictions from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) and BAryons and Haloes of MAssive Systems (BAHAMAS) cosmological hydrodynamical simulations. The simulations successfully reproduce many, but not all, of the observed power spectra, with an indication that environmental quenching may be too efficient in the simulations. We demonstrate that the cross-correlations are sensitive to both the internal [e.g. active galactic nucleus (AGN) and stellar feedback] and external processes (e.g. ram pressure stripping, harassment, strangulation, etc.) responsible for quenching. The methods outlined in this paper can be adapted to other observables and, with upcoming surveys, will provide a stringent test of physical models for environmental transformation.

Investigation of anisotropic spatial correlations of intra-event residuals of multiple earthquake intensity measures using latent dimensions method

SUMMARYConsidering spatial correlation of multiple earthquake intensity measures (IMs) is of particular importance in loss assessment of spatially distributed assets. This subject has been investigated in previous studies under the assumption of isotropy. Considering the fact that the assumption of isotropy is not valid in general, the present study employs a non-separable covariance model based on latent dimensions method to investigate anisotropic properties of spatial correlations and cross-correlations of intra-event residuals of multiple earthquake IMs. This method leads to the generation of valid covariance matrix in order to model anisotropic spatially distributed multivariate random fields. Two sets of IMs are considered in this study; the first set consists of peak ground intensity values (acceleration, velocity, and displacement), and the second set consists of spectral accelerations at three different periods. Data of 10 earthquake events in California and Japan are utilized in this study to estimate parameters of marginal and cross-covariance models. Moreover, parameters of covariance model of regional site condition, which is considered as average shear wave velocity of top 30 m of soil profile (Vs30), are obtained in order to investigate the effect of local sited conditions on spatial correlations of IMs. It is shown that maximum range and anisotropy ratio of covariance models of intra-event residuals of IMs are correlated with those of Vs30 values. Also, it is observed that the anisotropy direction of residuals of IMs is consistent with anisotropy direction of Vs30 values. Finally, predictive models are proposed to obtain marginal and cross-covariance functions for different earthquake IMs considering anisotropy.

Study of turbulence modulation and core density peaking with CO2 laser collective scattering diagnostics in the EAST tokamak

We report experiments designed to help us understand the interaction between electron-scale turbulence and plasma poloidal flow in tokamaks. Multiple electron-scale turbulence (for ) in the plasma radial region (ρ = 0–0.8) is simultaneously monitored by CO2 laser collective scattering diagnostics in the EAST tokamak. In the stable discharge phase with electron cyclotron resonance heating (ECRH) power modulation and constant neutral beam injection (NBI), a periodic change in the core density peaking factor can be obtained. We note that the intensity of turbulence and the core density peaking factor show a negative correlation, while the poloidal rotation speed is inversely proportional to the intensity of electron-scale turbulence. The correlation between turbulence and plasma flow is found to be closely related to the density peaking factor. The quasi-linear theory of electron-scale turbulence-driven intrinsic poloidal rotation is adopted, and it shows that plasma flow may exhaust the free energy of turbulence through Reynolds stress. Besides, by comparing the plasma flow shear and spatial cross-correlation of turbulence we observed that the spatial structure of turbulence at is more sensitive to flow shear than that of turbulence at . The possible mechanism for controlling the core density peaking, together with the unchanged density gradient in the outer plasma region 0.5–0.8) are favorable for controlling the fusion reaction rate if they can be extrapolated to burning plasma. The results for ECRH power modulation experiments without NBI are listed and analyzed simultaneously; these serve as a comparison and enrich the physical picture.

A preliminary study on the active cold seeps flow field in the Qiongdongnan Sea Area, the northern South China Sea

Cold seep is an important geological process of methane fluid migration. Methane plumes at these seeps have a significant impact on the local carbon budget and may trigger massive precipitation of carbonates and cold-seep biome. As a powerful greenhouse gas, methane seeping/venting from a cold seep may be released into the atmosphere and have a huge effect on climate. Research on cold seep flow field is of great significance for understanding geological processes of cold seep and their impacts on global climate change. Several methods have been used to observe cold seeps, e.g., acoustical, geochemical, and optical methods. However, no flow field image of Haima cold seeps was shown by these methods so far. The particle image velocimetry (PIV) method is a commonly used approach in the study of fluid dynamics. It tracks the motion of tracer particles in a flow field over a small time interval, so that the instantaneous velocity field can be calculated using a spatial cross-correlation method. In this study, we applied the PIV method to analyze in-situ video image sequences of the newly discovered, active cold seep in the Qiongdongnan Basin of the northern South China Sea during 2019, and obtained some quantitative, preliminary information about the flow. According to the video image sequences, there are two ways of methane seeping from the seafloor in the study area. One is via methane bubbles, which are formed through gas hydrate decomposing, and then slowly escaping from the seafloor. The other is via plumes of methane-containing fluid, which erupt rapidly from the vent. Gas hydrate-coated methane bubbles may also exist over the cold seep. The velocity of the plumes is between 0.00756 and 10.984 px/frame, and the accelerated velocity is −233.077 to 169.617 px/frame2 (negative for velocity decreasing with time). The vertical velocity is −10.842 to 5.055 px/frame (positive direction for downward), and the horizontal velocity is −6.377 to 5.828 px/frame (positive direction to the right). The velocity of methane bubbles is about 0.238 m/s, consistent with previous studies’ findings; this agreement demonstrates the viability of the PIV method. These results show that the direction of the cold seep flow is generally upward. However, its internal flow field involves complex turbulence. The direction and velocity of the cold seep flow change with time, and its streamlines are sinuous. Although there are some velocity deviations, our results show that the PIV method is feasible for calculating the flow field image, and that it has some unique advantages, e.g., it is low-cost, non-invasive, and has high resolution. However, we stress that these results are preliminary, and research on cold seep flow field is just beginning. Compared to hydrothermal vents, the flow field of cold seeps is stilly poorly understood and more studies are needed, such as describing the cold seep flow field quantitatively and analyzing how and why it changes.

A 3D Wideband Two-Cluster Channel Model for Massive MIMO Vehicle-to-Vehicle Communications in Semi-Ellipsoid Environments

This paper presents a three dimensional (3D) wideband two-cluster channel model for massive multiple-input and multiple-output (MIMO) vehicle-to-vehicle (V2V) communications. In the model, we introduce multiple confocal semi-ellipsoid models to depict the distribution of clusters in roadside environments; therefore, the statistical properties for different propagation delays can be derived and investigated. Furthermore, we adopt the birth-death process to simulate the dynamic characteristics of clusters on both the array and time axes, which efficiently characterize the non-stationarity of massive MIMO communication systems. Some comparisons of the spatial cross-correlation functions (CCFs) of propagation links via the two-cluster model with and without the cluster evolution for different taps are made. In addition, we study the temporal auto-correlation functions (ACFs) of the propagation links with respect to the different moving time of the mobile transmitter (MT) and mobile receiver (MR). Simulation results of the statistical channel characteristics nicely match those of the theoretical results, which demonstrate that our model is suitable for describing real massive MIMO V2V communication environments.

In vitro pathological model of Alzheimer's disease based on neuronal network chip and its real-time dynamic analysis

Alzheimer's disease (AD) is a chronic central neurodegenerative disease. The pathological features of AD are the extracellular deposition of senile plaques formed by amyloid-β oligomers (AβOs) and the intracellular accumulation of neurofibrillary tangles formed by hyperphosphorylated tau protein. In this paper, an in vitro pathological model of AD based on neuronal network chip and its real-time dynamic analysis were presented. The hippocampal neuronal network was cultured on the microelectrode array (MEA) chip and induced by AβOs as an AD model in vitro to simultaneously record two firing patterns from the interneurons and pyramidal neurons. The spatial firing patterns mapping and cross-correlation between channels were performed to validate the degeneration of neuronal network connectivity. This biosensor enabled the detection of the AβOs toxicity responses, and the identification of connectivity and interactions between neuronal networks, which can be a novel technique in the research of AD pathological model in vitro.

Synthetic Aperture Radar Scene Classification Using Multiview Cross Correlation Attention Network

The second-order pooling manner, exploring higher feature statistics than the first-order pooling, has achieved impressive performance in scene classification. However, the object not only presents similarity but also exhibits diversified singularity on the synthetic aperture radar (SAR) image. These make the second-order pooling approaches to explore the single-view second-order feature statistics less adaptable for SAR scene classification. To solve this issue, an end-to-end training framework based on the multiview cross correlation attention network (MCAN) is proposed. The spatial and channelwise self-attention modules are first employed to model the interdependences between the spatial and channel dimensions of their convolutional features. Subsequently, the global spatial and channelwise covariance pooling layers are drawn into the MCAN, and then, they learn the spatial and channel cross correlations within the feature statistics, respectively. Finally, an iterative matrix square-root normalization layer, which owns the capability to fast computing approximate square root of the covariance matrix, is introduced for making the feature representation more discriminative. Experiments on the SAR data set from the TerraSAR-X images for scene classification demonstrate that the MCAN performs better than other related works.

Flowfield Characteristics of a Supersonic Jet Impinging on an Inclined Surface

The study of the flowfield and noise characteristics of supersonic impinging jets is important as these configurations can be found in many engineering applications, such as short takeoff and landing vehicles, hot surface cooling mechanisms, cold gas dynamic spray processes, and turbomachinery systems. This study experimentally investigates the flowfield characteristics of supersonic jets impinging on an inclined surface with nozzle-to-plate distances of and ( is the jet hydraulic diameter) and nozzle pressure ratios () of 3.7 and 5.9. The two-dimensional two-component velocity fields were acquired in the central plane of the test nozzle and along the 45° inclined impingement surface by using a planar particle image velocimetry (PIV) technique. The flow characteristics of supersonic impinging jets, such as mean velocity and turbulent kinetic energy, were computed from the acquired PIV velocity vector fields, and the statistical profiles were compared to study the effects of the impingement surface and on the flow characteristics. The obtained statistical results have shown the presence of shock cells near the nozzle outlet, oblique plate shocks near the impingement, and tail shocks along the inclined surface. In addition, spatial two-point cross-correlations of turbulent velocity were computed using the PIV velocity vectors to study the sizes, shapes, and orientation of turbulent flow structures in the impinging jets. Finally, a proper orthogonal decomposition analysis was performed on the collections of velocity snapshots extracting the coherent flow structures of a supersonic jet impinging on an inclined surface.

Distributed Turbulence Model with Rigorous Spatial Cross-Correlation for Simulation of Helicopter Flight in Atmospheric Turbulence

This paper presents a distributed turbulence model with rigorous spatial cross-correlation for helicopter flight simulation in atmospheric turbulence and for future handling-quality analysis. First, digital filters with longitudinal correlations of the von Kármán turbulence are developed to generate discrete turbulence velocity components. Meanwhile, transverse turbulence correlations are considered by relating the filters in different positions with mathematically rigorous spatial cross-correlation. Then, the distributions of the related filters on the transverse plane in front of helicopter and their velocity components in the longitudinal direction of airspeed, as well as turbulence models of helicopter aerodynamic surfaces, are established. Finally, a flight dynamics model coupled with the turbulence model is developed and validated against the flight-test data. The proposed model can achieve accurate real-time simulations of helicopter response to atmospheric turbulence in the frequency range of interest of handling qualities. The effect of transverse turbulence correlations on helicopter frequency response is also analyzed. The results show that the simulation model regardless of transverse turbulence correlations would aggravate the "rotor-to-body attenuation" effect of the main rotor and therefore underpredict the helicopter roll, pitch, and heave rate responses to atmospheric turbulence in the frequency range of interest.