Large-scale Features Research Articles

Large-Scale Features of the CON Galaxy NGC4418 with MUSE

AbstractCompact obscured nuclei (CONs) are relatively common in the centers of local (U)LIRGs, yet their nature remains unknown. Both AGN activity and extreme nuclear starbursts have been suggested as plausible nuclear power sources. The prevalence of outflows in these systems suggest that CONs represent a key phase in the nuclear feedback cycle, in which material is ejected from the central regions of the galaxy. Here, we present results from MUSE for the confirmed local CON galaxy NGC4418. For the first time we spatially map the spectral features and kinematics of the galaxy in the optical, revealing several previously unknown structures. In particular, we discover a bilateral outflow along the minor axis, an outflowing bubble, several knot structures and a receding outflow partially obscured by the galactic disk. Based on the properties of these features, we conclude that the CON in NGC4418 is most likely powered by an AGN.

LDD-Net: Lightweight printed circuit board defect detection network fusing multi-scale features

The current printed circuit board (PCB) defect detection model is difficult to balance accuracy and computational cost and cannot satisfy the requirements of practical applications. In this regard, a lightweight PCB image defect detection network (LDD-Net) with high accuracy is proposed in this study. First, LDD-Net designs a novel lightweight feature extraction network (LFEN), which adds convolutional layers for extracting large-scale feature maps and uses an efficient downsampling method to extract richer defect features. LFEN not only greatly reduces resource requirements, but also improves detection accuracy. Second, LDD-Net devises multi-scale aggregation network (MAN) to facilitate information sharing among feature maps at different scales. In addition, MAN incorporates an efficient attention module to suppress similar background interference. Finally, LDD-Net uses the lightweight decoupling head to detect three groups of shallow feature maps with rich information of the small object. Experimental results on the PCB defect dataset ,and VisDrone public dataset show that the mean average precision (mAP), recall, and precision of LDD-Net outperform state-of-the-art lightweight object detection models. The mAP, computation, number of parameters, and detection speed of LDD-Net are 95.90%, 21.48 G, 5.13 M, and 78.13 frames per second, respectively. LDD-Net effectively balances accuracy, computational cost, and detection speed for applications in edge systems or resource-constrained embedded devices.

New insights into hypogenic non-matrix dissolution mega features driving subsurface fluid movement within the Arab Formation, Saudi Arabia

This paper reports the results of a large integrated study into the classification, distribution and significance of hypogenic non-matrix large-scale dissolution features present in the Arab Formation of Saudi Arabia. The investigation of these features was based primarily on extensive core, wireline caliper, wireline/log while drilling resistivity borehole image logs supported by encountered total lost circulation of drilling fluids and production log data. Dissolution features observed by this study were classified into three main categories: touching vugs, ramiform voids, and cavities. This study is the first to successfully quantify the presence and extent of these non-matrix intervals in this formation, which has greatly improved the 3D modelling efforts. Over 8100 feet of large-scale dissolution features were intersected in both vertical and horizontal wells. The hypogenic genesis of the large-scale dissolution features pre-dates replacement dolomitization in the area. Given the extensive data reviewed, the presented results give insight into the importance of the large-scale dissolution mega features as they are strongly related to total loss of mud circulation during drilling, and will have implications for well-to-well short-cutting during production and impact oil storage capacity.

Percolation Theories for Quantum Networks.

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network's indirect connectivity. This realization leads to the emergence of an alternative theory called "concurrence percolation", which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.

Long-term morphological and sedimentological changes caused by bottom trawling on the northern Catalan continental shelf (NW Mediterranean)

Bottom trawling is a fishing method that involves towing of nets along the seafloor to catch demersal species. The dragging of trawling gears along the seafloor results in scraping and ploughing of the surficial sediments, leading to the formation of turbid plumes of resuspended sediments and causing measurable changes in the seabed morphology. High-resolution multibeam data, side scan sonar, sediment grain size and vessel tracking data have been used to investigate the impact of bottom trawling on the seafloor morphology and surficial sediments of the northern Catalan continental shelf (NW Mediterranean), providing new insights into the impact of this anthropogenic activity on the seafloor. Multibeam data evidenced the occurrence of large-scale erosive features as a consequence of repeated scouring by fishing gears in localized areas. They are characterized by elongated (70-300 m wide and up to 8 km long) channelized areas of high backscatter with variable incision (from 0.2 m to 1.2 m). The spatial distribution of these morphologies shows a similar pattern to that observed in the fishing intensity, with maximum values in the areas of increased trawling intensity, corresponding to the main fishing grounds. Side scan sonar data also shows higher densities of trawl marks in these areas than in the surroundings. Sediment cores collected on these features show an upward-coarsening trend in the first 4-5 cm of the core, suggesting that part of the finer fraction resuspended by trawling is winnowed, increasing the sand content of the surface sediment. The identification of such large erosive morphologies in the main fishing grounds evidences that repeated trawling over the same fishing ground during decades can result in deep excavation of the seafloor, leading to permanent large-scale morphological changes. Furthermore, the evolution of these erosive features over a 13-year interval points towards long recovery periods after the cessation of trawling activities.

JRA55 is the best reanalysis representing observed near-surface wind speeds over India

Reanalyses are often regarded as the most reliable datasets that give a comprehensive picture of meteorological parameters all over the globe. They are the first choice of dataset for researchers interested in analysing meteorological variables. Recognizing their importance, several agencies worldwide produce reanalysis datasets using different methods. This study aims to reduce the uncertainty associated with choosing among these reanalyses by identifying the best reanalysis representing the near-surface wind speeds over India. For this purpose, 10 m wind speed data from six reanalyses are used: National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP1), National Centers for Environmental Prediction-Department of Energy reanalysis (NCEP2), Japanese 55-year Reanalysis (JRA55), Indian Monsoon Data Assimilation and Analysis (IMDAA), European Centre for Medium-Range Weather Forecasts reanalysis version 5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2). The evaluation is done by comparing the spatially-averaged reanalyses against the station observations obtained from National Centers for Environmental Information (NCEI) over seven homogenous climate zones of India for the 1980–2020 period. Results demonstrate wide divergence between the reanalyses and observations in terms of monthly mean statistics, inter-annual variability (IAV), and annual mean trend. Despite the differences, JRA55 best represents the trends, IAV, and shows the closest resemblance to observations. On an annual scale, the 10 m wind speed observations over India have a weak correlation with the Nino 3.4 index; however, they have a significant negative correlation with the Dipole Mode index (DMI), implying that SST anomalies over the Indian Ocean have a considerable impact on the weather and climate patterns of the Indian subcontinent. JRA55 closely reflects these relationships with large-scale ocean-atmospheric features as well. Overall, we conclude that JRA55 can be considered a proxy for 10 m wind speed observations over India.

Multi-Scale Feature Refined Network for Human Pose Estimation

Occlusive keypoints has been a challenge for human pose estimation, especially the mutual occlusion of human bodies. One possible solution to this problem is to utilize multi-scale features, where small scale features are capable of identifying keypoints, while large-scale features can capture the relationship between keypoints. Feature fusion among multi-scale features allows for the exchange of information between keypoints, facilitating the inference of occluded keypoints based on the identified keypoints. However, it’s found that there are invalid features in feature fusion which will interfere valid feature. In this paper, we propose multi-scale feature refined network (MSFRNet) based on HRNet and a new attention module namely multi-resolution attention module (MRAM). The proposed MRAM is designed to strengthen the effective information while suppressing redundant information. It has multiple inputs and outputs and can learn the relationships between keypoints while retaining detailed information. The proposed MSFRNet outperforms HRNet, achieving a 1.4[Formula: see text]AP improvement on the COCO dataset with only a marginal computational increase of 0.35 GFLOPs. Additionally, it demonstrates superior performance with a 0.9[Formula: see text]AP, 0.7[Formula: see text]AP, and 1.8[Formula: see text]AP improvement on the MPII, CrowdPose and OCHuman datasets, respectively. Furthermore, compared with the latest attention mechanism PSA, the MSFRNet exhibits lower computational cost while maintaining the same pose-estimation accuracy.

Reionisation time field reconstruction from 21 cm signal maps

Context. During the epoch of reionisation, the intergalactic medium is reionised by the UV radiation from the first generation of stars and galaxies. One tracer of the process is the 21 cm line of hydrogen that will be observed by the Square Kilometre Array (SKA) at low frequencies, thus imaging the distribution of ionised and neutral regions and their evolution. Aims. To prepare for these upcoming observations, we investigate a deep learning method to predict from 21 cm maps the reionisation time field (treion(r)), the time at which each location has been reionised. The treion(r) method encodes the propagation of ionisation fronts in a single field, and gives access to times of local reionisation or to the extent of the radiative reach of early sources. Moreover it gives access to the time evolution of ionisation on the plane of sky, when this evolution is usually probed along the line-of-sight direction. Methods. We trained a convolutional neural network (CNN) using simulated 21 cm maps and reionisation time fields produced by the simulation code 21cmFAST. We also investigated the performance of the CNN when adding instrumental effects. Results. Overall, we find that without instrumental effects the 21 cm maps can be used to reconstruct the associated reionisation times field in a satisfying manner. The quality of the reconstruction is dependent on the redshift at which the 21 cm observation is being made, and in general it is found that small-scale features (< 10 cMpc h−1) are smoothed in the reconstructed field, while larger-scale features are recovered well. When instrumental effects are included, the scale dependence of reconstruction is even further pronounced, with significant smoothing on small and intermediate scales. Conclusions. The reionisation time field can be reconstructed, at least partially, from 21 cm maps of IGM during the epoch of reionisation. This quantity can thus be derived in principle from observations, and should then provide a means to investigate the effect of local histories of reionisation on the first structures that appear in a given region.

Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)

Abstract. This paper describes the global eddying ocean–sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model (GLOB16) is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16∘ and 98 vertical levels and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To assess the accuracy of simulated 3D ocean fields and highlight the relative benefits of resolving mesoscale processes, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean–sea ice component of the fully coupled CMCC climate model. The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed-layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling efforts, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.

An external attention-based feature ranker for large-scale feature selection

An important problem in data science, feature selection (FS) consists of finding the optimal subset of features and eliminating irrelevant or redundant features. The FS task on high-dimensional data is challenging for the FS methods currently available in the literature. To overcome this limitation, we propose a novel feature selection method called External Attention-Based Feature Ranker for Large-Scale Feature Selection (EAR-FS) whose function is based on the logic of an attention mechanism and a hybrid metaheuristic. EAR-FS comprises three interdependent modules: (1) in the training module design, a multilayer perceptron network endowed with an attention module is trained to fit the dataset; (2) in feature ranking by attention, the trained attention module is used for attention updating and to rank features according to their importance; 3) in subset generation, a two-stage heuristic approach is applied to determine a small number of features that still guarantee high-accuracy performance. The experimental benchmark comprised 26 datasets of small, large and very large sizes, ranging from 15 to 12,533 features. Experiments performed against the state-of-the-art algorithms of FS show that our algorithm is efficient at selecting a small number of features from large datasets while guaranteeing excellent levels of classification accuracy. For instance, EAR-FS demonstrated its capability to reduce the features of the 11 Tumor dataset by 97% while maintaining a classifier accuracy of over 93%.

Investigation of a Near-Field Cylinder Wake in the Subsonic, Transonic, and Supersonic Regimes

The near wake of a circular cylinder in a high-Reynolds-number regime is investigated using the particle image velocimetry technique for an inflow Mach number spanning 0.3 to 2. The mean flow and turbulent kinetic energy field for different Mach number cases are presented. Furthermore, the influence of the Reynolds and Mach number effects is examined based on the mean separation point along the cylinder surface, which is extracted from complementary schlieren visualization measurements, and the literature. The wake width for the supersonic inflow case is found to be an order of magnitude smaller physically than the subsonic case with an identical cylinder diameter. Additionally, the swirl strength criterion is used to identify the eddies present within the wake, and the eddy size and convection velocity are characterized based on the spatiotemporal correlations. The result suggests that the mean wake features for the subsonic and supersonic cases are very different. Moreover, the interaction of eddies generated at the cylinder’s top and bottom shear layers leads to the large-scale features present in the wake, and the differing paths followed by the eddies between the subsonic and supersonic cases are responsible for the variation in the observed wake width.

Badminton video action recognition based on time network

With the continuous development of artificial intelligence research, computer vision research has shifted from traditional “feature engineering”-based methods to deep learning-based “network engineering” methods, which automatically extracts and classifies features by using deep neural networks. Traditional methods based on artificial design features are computationally expensive and are usually used to solve simple research problems, which is not conducive for large-scale data feature extraction. Deep learning-based methods greatly reduce the difficulty of artificial features by learning features from large-scale data and are successfully applied in many visual recognition tasks. Video action recognition methods also shift from traditional methods based on artificial design features to deep learning-based methods, which is oriented to building more effective deep neural network models. Through collecting and sorting related research results found that academic for timing segment network of football and basketball video action research is relatively rich, but lack of badminton research given the above research results, this study based on timing segment network of badminton video action identification can enrich the research results, provide reference for follow-up research. This paper introduces the lightweight attention mechanism into the temporal segmentation network, forming the attention mechanism-timing segmentation network, and trains the neural network to get the classifier of badminton stroke action, which can be predicted as four common types: forehand stroke, backhand stroke, overhead stroke and pick ball. The experimental results show that the recognition recall and accuracy of various stroke movements reach more than 86%, and the average size of recall and accuracy is 91.2% and 91.6% respectively, indicating that the method based on timing segmentation network can be close to the human judgment level and can effectively conduct the identification task of badminton video strokes.

Beyond 3×2-point cosmology: the integrated shear and galaxy 3-point correlation functions

We present the integrated 3-point correlation functions (3PCF) involving both the cosmic shear and the galaxy density fields. These are a set of higher-order statistics that describe the modulation of local 2-point correlation functions (2PCF) by large-scale features in the fields, and which are easy to measure from galaxy imaging surveys. Based on previous works on the shear-only integrated 3PCF, we develop the theoretical framework for modelling 5 new statistics involving the galaxy field and its cross-correlations with cosmic shear. Using realistic galaxy and cosmic shear mocks from simulations, we determine the regime of validity of our models based on leading-order standard perturbation theory with an MCMC analysis that recovers unbiased constraints of the amplitude of fluctuations parameter A s and the linear and quadratic galaxy bias parameters b 1 and b 2. Using Fisher matrix forecasts for a DES-Y3-like survey, relative to baseline analyses with conventional 3×2PCFs, we find that the addition of the shear-only integrated 3PCF can improve cosmological parameter constraints by 20–40%. The subsequent addition of the new statistics introduced in this paper can lead to further improvements of 10–20%, even when utilizing only conservatively large scales where the tree-level models are valid. Our results motivate future work on the galaxy and shear integrated 3PCFs, which offer a practical way to extend standard analyses based on 3×2PCFs to systematically probe the non-Gaussian information content of cosmic density fields.

Changes in Persistent Precipitation in Northwest China and Related Large-Scale Circulation Features

Changes in Persistent Precipitation in Northwest China and Related Large-Scale Circulation Features

Exploring the structure and kinematics of the Milky Way through A stars

Context. Despite their relatively high intrinsic brightness and the fact that they are more numerous than younger OB stars and kinematically colder than older red giants, A-type stars have rarely been used as Galactic tracers. They may, in fact, be used to fill the age gap between these two tracers, thereby allowing us to evaluate the evolutionary and dynamic processes underlying the transition between them. Aims. We analyse Galactic disc structure and kinematic perturbations up to 6 kpc from the Sun based on observations of A-type stars. Methods. This work presents a catalogue of A-type stars selected using the IGAPS photometric survey. It covers the Galactic disc within 30° ≤l ≤ 215° and |b|≤5° up to a magnitude of r ≤ 19 mag with about 3.5 million sources. We used Gaia Data Release 3 parallaxes and proper motions, as well as the line-of-sight velocities, to analyse the large-scale features of the Galactic disc. We carried out a study of the completeness of the detected density distributions, along with a comparison between the b < 0° and b > 0° regions. Possible biases caused by interstellar extinction or by the usage of some kinematic approximations were examined as well. Results. We find stellar overdensities associated with the Local and the Perseus spiral arms, as well as with the Cygnus region. We find that A-type stars also provide kinematic indications of the Galactic warp towards the anticentre, which displays a median vertical motion of ∼6 − 7 km s−1 at a Galactocentric radius of R = 14 kpc. It starts at R ≈ 12 kpc, which supports the scenario where the warp begins at larger radii for younger tracers when compared with other samples in the literature. We also detect a region with downward mean motion extending beyond 2 kpc from the Sun towards 60° ≲l ≲ 75° that may be associated with a compression breathing mode. Furthermore, A-type stars reveal very clumpy inhomogeneities and asymmetries in the VZ − Vϕ velocity space plane.

Online surface defect segmentation on aluminum strip production line using a lightweight and efficient model

A significant challenge in automated defect inspection (ADI) of aluminum strip surfaces is improving segmentation speed to satisfy the online inspection requirements of the production line while maintaining the precision of defect identification. This study proposes a lightweight and efficient defect segmentation model that can be applied in aluminum processing enterprises for fast and precise segmentation of aluminum strip surface defects. A novel fusion attention (FA) mechanism is first established to enhance the focus on critical characteristics along the spatial and channel dimensions. This mechanism adopts continuous dilated convolutions with appropriate dilation rates to effectively increase the range of the receptive field and improve defect localization accuracy. Subsequently, a lightweight MobileViTv2 with an embedded FA mechanism is employed as a multi-scale feature extractor to learn comprehensive representations from defect images. Next, a novel feature fusion method, named large-scale feature pyramid network (LSFPN), is introduced to enhance the focus on details within large-scale features. LSFPN establishes four progressively shallower top-down pathways with fast normalized fusion weights and incorporates lightweight aggregation nodes based on the MoblieNetv2 block. Surface images of straightened aluminum strips with five universal defects were collected, whereby a new dataset was established. The experimental outcomes demonstrate the proposed model outperforms other state-of-the-art techniques synthetically, achieving a mean Intersection over Union (mIoU) of 87.01%, a segmentation speed of 61.67 fps, and a model size of 16.23 MB. This model may serve as a valuable theoretical foundation for the online segmentation of aluminum strip surface defects in embedded devices.

Characterization of DNAPL source zones in clay-sand media via joint inversion of DC resistivity, induced polarization and borehole data

Toxic organic contaminants in groundwater are pervasive at many industrial sites worldwide. These contaminants, such as chlorinated solvents, often appear as dense non-aqueous phase liquids (DNAPLs). To design efficient remediation strategies, detailed characterization of DNAPL Source Zone Architecture (SZA) is required. Since invasive borehole-based investigations suffer from limited spatial coverage, a non-intrusive geophysical method, direct current (DC) resistivity, has been applied to image the DNAPL distribution; however, in clay-sand environments, the ability of DC resistivity for DNAPLs imaging is limited since it cannot separate between DNAPLs and surrounding clay-sand soils. Moreover, the simplified parameterization of conventional inversion approaches cannot preserve physically realistic patterns of SZAs, and tends to smooth out any sharp spatial variations. In this paper, the induced polarization (IP) technique is combined with DC resistivity (DCIP) to provide plausible DNAPL characterization in clay-sand environments. Using petrophysical models, the DCIP data is utilized to provide tomograms of the DNAPL saturation (SN) and hydraulic conductivity (K). The DCIP-estimated K/SN tomograms are then integrated with borehole measurements in a deep learning-based joint inversion framework to accurately parameterize the highly irregular SZA and provide a refined DNAPL image. To evaluate the performance of the proposed approach, we conducted numerical experiments in a heterogeneous clay-sand aquifer with a complex SZA. Results demonstrate the standalone DC resistivity method fails to infer the DNAPL in complex clay-sand environments. In contrast, the combined DCIP technique provides the necessary information to reconstruct the large-scale features of K/SN fields, while integrating DCIP data with sparse but accurate borehole data results in a high resolution characterization of the SZA.

Regional geomagnetic core field and secular variation model over the Iberian Peninsula from 2014 to 2020 based on the R-SCHA technique

The Earth’s magnetic field originated in the fluid core, the so-called core field, is the dominant contribution to the geomagnetic field. Since ancient times, the core geomagnetic field has been used primarily for geographical orientation and navigation by means of compasses. Nowadays, thanks to the large amount of geomagnetic data available, core field models can be developed on a global or regional scale. Global models resolve large-scale geomagnetic field features, while regional models can resolve greater detail over a particular region. The spherical harmonic cap analysis is a widely used technique for regional-scale modelling of the geomagnetic field. In this work we have developed a regional model of the core field and its secular variation between 2014.5 and 2020.5 over the Iberian Peninsula, based on data from Swarm satellites, geomagnetic observatories and repeat stations. Its performance has been validated by comparing the fit to the available geomagnetic data using the regional model and the global models IGRF and CHAOS over the whole spatio-temporal range studied. In order to optimise the model, a detailed study of its input parameters has been carried out, showing that not all parameters have an equal influence on the modelling. This new model reproduces the input data with a root mean square error of 2.9 nT, improving the outcome of global models on this region. The results of this work will allow the Spanish Instituto Geográfico Nacional to produce the magnetic cartography of Iberia and the Balearic Islands in 2020.0, which for the first time will be based on a regional core field model, replacing the polynomial variation method used in the past.Graphical abstract

3D9C seismic data reconstruction with multi-scale convolution neural network

The nine components (9C) seismic data acquired with three-component (3C) sources and 3C receivers is beneficial to the inversion of lithologic reservoirs with high resolution. However, physical constraints and the trade-off between cost and data quality often result in under-sampled acquisition. For the 3D9C incomplete seismic data, conventional methods generally reconstruct each component data separately, which could not fully use the implicit relationship between the multi-component data and it is time-consuming. In this paper, we present a novel method based on a multi-scale U-Net (MSU-Net) to reconstruct 3D9C seismic data simultaneously. Compared with the U-Net, MSU-Net introduces multiple encoder and decoder sub-networks with different layers, which are connected through a series of nested and dense skip pathways. Through these re-designed skip pathways, the large scale feature maps with low-frequency information from shallow sub-networks and the small scale feature maps with high-frequency information from deep sub-networks are concatenated in each up-sample operator, which is more useful to reconstruct the seismic data with big gaps. Additionally, the three-channel input layer is designed in the MSU-Net according to the characteristics of the 3D9C data to implement 3C data reconstruction simultaneously. The MSU-Net is trained to learn the nonlinear relationship between the 3C data generated by the x-component source adaptively, and then simultaneously reconstructs the other 9C data in three times. Several examples of 3D9C seismic data reconstruction are used to evaluate the performance of the trained MSU-Net in comparison with U-Net and other traditional methods like the parabolic Radon transform. The reconstruction results demonstrate the effectiveness of the proposed method.

Scale Analysis of Typhoon In-Fa (2021) Based on FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Observed and All-Sky-Simulated Brightness Temperature

Based on the Final Operational Global Analysis (FNL) data from the National Centers for Environmental Prediction and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts, the all-sky brightness temperatures of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A satellite (FY-4A) are simulated, which are then used to investigate Typhoon In-Fa (2021). The results show that the all-sky simulations based on ERA5 and FNL for FY-4A GIIRS channels 27 (716.25 cm−1), 90 (755.625 cm−1), and 417 (960 cm−1) can reproduce well the structure and intensity of Typhoon In-Fa. But the FNL simulations overestimate the typhoon intensity with more high ice clouds around the typhoon center. Fourier analysis of Typhoon In-Fa at severe tropical storm (STS) and typhoon (TY) stages is conducted. The results reveal that the dominant features of Typhoon In-Fa are primarily large-scale, with a relatively small proportion of observations and simulations dedicated to small-scale features at the STS stage. However, the proportion of large-scale features decreases while the amount of small-scale information increases during the TY stage. The purpose of this study is to assess the performance of FY-4A GIIRS all-sky simulations based on the ERA5 and FNL data, as well as to prepare for future all-sky data assimilations.