Large-scale Parameters Research Articles

Relationship between the South Asian High and Western North Pacific tropical cyclone genesis

This study highlights a significant inter-decadal modulation of the South Asian high (SAH) on the meridional distribution of western North Pacific (WNP) tropical cyclone genesis frequency (TCGF). There is an obvious inter-decadal change in the first leading SAH mode, with an abrupt weakening since the early 1990s. During 1979–1993, when there was a stronger SAH, TCs were suppressed over the northern WNP and enhanced over the southern WNP in response to inter-annual SAH changes. The northern WNP TCGF had a significant negative correlation with the SAH (r = −0.69), while the southern WNP TCGF had a significant positive correlation with the SAH (r = 0.78). This north-south seesaw pattern driven by the SAH weakened during 1994–2019.This inter-decadal modulation of the inter-annual SAH-WNP TC genesis relationship can be explained by changes in large-scale environmental parameters. There is anomalous ascending (descending) motion and negative (positive) vorticity centered at 850-hPa as well as less (more) moisture at 600-hPa in the northern WNP (southern WNP) during 1979–1993. The maintenance of a strong low-level anomalous anticyclone accompanied by the concurrent influence of the SAH and East Asian upper-tropospheric jet stream decreases northern WNP TCGF. Linking to an El Niño-like SST pattern, the cyclonic circulation forced by a Gill-type forcing and enhanced by equatorial low-level westerly anomalies increases southern WNP TCGF. In contrast, we find a relatively weak inter-annual relationship of large-scale factors to the SAH since 1994. This weakening relationship is largely due to the inter-decadal weakening of the SAH and East Asian upper-tropospheric jet stream. Inter-decadal changes of the East Asian summer monsoon and the phase shift of the Atlantic Multi-decadal Oscillation appear to be two important factors modulating the inter-annual relationship between the SAH and the meridional distribution of WNP TCGF.

Learning-Based Resource Allocation for Ultra-Reliable V2X Networks With Partial CSI

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms

• ICESat-2 has the potential to improve the estimation accuracy and efficiency of forest aboveground biomass on a large scale. • The Google Earth Engine cloud platform was used for Sentinel-2 images acquisition and composition. • The optimized extreme learning machine achieved the highest estimation accuracy and reasonable forest aboveground biomass spatial distribution. Natural forests have the most complex structure and richest biodiversity among terrestrial ecosystems and are essential for maintaining the carbon balance and stability of the biosphere. Aboveground biomass (AGB) is a primary indicator used to evaluate forests and can directly measure forest growth and the quality of natural forests. Accurate and rapid AGB estimations can significantly improve the efficiency of forest management and deepen the understanding of the forest carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), one of the most recently launched spaceborne light detection and ranging (LiDAR) instruments, can penetrate forest canopies and obtain accurate, large-scale forest vertical parameter measurements. However, the discrete footprints offered by ICESat-2 cannot provide comprehensive spatial AGB distribution data. In this study, an optimized extreme learning machine (ELM) method was proposed to estimate the AGB of natural forests on the eastern Qinghai-Xizang Plateau, China. The synthetic Sentinel-2 images acquired from the Google Earth Engine (GEE) were synergized with ICESat-2 to realize continuous AGB mapping. To verify the effectiveness of the optimized method, support vector machine (SVM), k-nearest neighbor (kNN), and backpropagation (BP) neural network models were also established for comparison. The measured AGB data extracted from the forest management inventory (FMI) were used for model accuracy evaluation. The results show that the optimized ELM achieved the best estimation effect among all the analyzed models, with an R 2 value of 0.68 and a root mean square error (RMSE) value of 25.14 mg/ha. The optimized ELM obtained the minimum RMSE and greatly improved the AGB prediction efficiency. These findings prove the ability of ICESat-2 in estimating the AGB of natural forests, which can provide a new way for large-scale forest resources investigation in high-altitude areas with a harsh environment.

Spatiotemporal dynamics and anthropologically dominated drivers of chlorophyll-a, TN and TP concentrations in the Pearl River Estuary based on retrieval algorithm and random forest regression

Estimation of large-scale and high-precision water quality parameters is critical in explaining the spatiotemporal dynamics and the driving factors of water quality variability, especially in areas with environmental complexity (e.g., crisscrossing waterways, high flood risk in rainy season and seawater invasion). Thus, in this study, a retrieval algorithm was developed to predict chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP) concentrations in the Pearl River Estuary (PRE) based on a large amount of in situ measurements and Landsat 8 remote sensing images. Random Forest (RF) machine learning was conducted to identify the relationship between environmental indicators (pH, turbidity, conductivity, total dissolved solids and water temperature), Chl-a, TN and TP. The results showed that the NIR/R Binomial algorithm for Chl-a estimation presented appreciable reliability with R2 of 0.7429, root mean square error (RMSE) of 1.2089 and mean absolute percent error (MAPE) of 15.33%. The water quality variation in the PRE showed a characteristic of overall improvement and regional deterioration with average concentrations of 7.28 μg/L, 1.15 mg/L and 0.12 mg/L for Chl-a, TN, and TP respectively. Turbidity and pH were identified as the most important indicators to explain Chl-a (52.86%, 39.91%), TN (52.38%, 40.57%) and TP (55.23%, 40.03%) variation. Agricultural pollution was the main pollution source due to the intensive application of fertilizer and increased field size. Besides, land use patterns (e.g., increasing farmland but decreasing forest) greatly influenced water quality from 2010 to 2020. Moreover, light limitation caused by high turbidity reduced the algae productivity and further lowered the Chl-a concentration. The driving factors for regional water quality variations were anthropologically dominated and supplemented by climate change. This study improved the monitoring accuracy of regional water environment and provided quantitative early warning of water pollution events for environmental practitioners, so as to achieve long-term monitoring, precise pollution management and efficient water resources management.

Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea

Sea ice loss and accelerated warming in the Barents Sea have recently been one of the main concerns of climate research. In this study, we investigated the trends and possible relationships between sea surface temperature (SST), sea ice concentration (SIC), and local and large-scale atmospheric parameters over the last 39 years (1982 to 2020). We examined the interannual and long-term spatiotemporal variability of SST and SIC by performing an empirical orthogonal function (EOF) analysis. The SST warming rate from 1982 through 2020 was 0.35 ± 0.04 °C/decade and 0.40 ± 0.04 °C/decade in the ice-covered and ice-free regions, respectively. This climate warming had a significant impact on sea-ice conditions in the Barents Sea, such as a strong decline in the SIC (−6.52 ± 0.78%/decade) and a shortening of the sea-ice season by about −26.1 ± 7.5 days/decade, resulting in a 3.4-month longer summer ice-free period over the last 39 years. On the interannual and longer-term scales, the Barents Sea has shown strong coherent spatiotemporal variability in both SST and SIC. The temporal evolution of SST and SIC are strongly correlated, whereas the Atlantic Multidecadal Oscillation (AMO) influences the spatiotemporal variability of SST and SIC. The highest spatial variability (i.e., the center of action of the first EOF mode) of SST was observed over the region bounded by the northern and southern polar fronts, which are influenced by both warm Atlantic and cold Arctic waters. The largest SIC variability was found over the northeastern Barents Sea and over the Storbanken and Olga Basin. The second EOF mode revealed a dipole structure with out-of-phase variability between the ice-covered and ice-free regions for the SST and between the Svalbard and Novaya Zemlya regions for SIC. In order to investigate the processes that generate these patterns, a correlation analysis was applied to a set of oceanic (SST) and atmospheric parameters (air temperature, zonal, and meridional wind components) and climate indices. This analysis showed that SST and SIC are highly correlated with air temperature and meridional winds and with two climate indices (AMO and East Atlantic Pattern (EAP)) on an interannual time scale. The North Atlantic Oscillation (NAO) only correlated with the second EOF mode of SST on a decadal time scale.

Analysis of Multipath Components Distributions Over a Large Array in Indoor mmWave Channels

In this article, we analyze the characteristics of indoor radio channels at millimeter waves. Measurements were performed using a large virtual antenna array (VAA), for massive multiple input multiple output (MIMO) applications. The results in terms of correlation and multipath components (MPCs) characteristics are analyzed for the whole large array and virtual subarrays composing it. Common MPC among the subarrays are identified, and the impact on the overall channel large-scale parameters is discussed.

Three-Dimensional Dynamic Modeling and Motion Analysis of a Fin-Actuated Robot

Dynamic modeling has beencapturing the attention for its fundamentality in precise locomotion analyses and control of underwater robots. In this article, we focused on the 3-D dynamic modeling and the experimental validation of multiple motion patterns of underwater robots in large-scale parameter space. A 3-D dynamic model of an active-tail-actuated robotic fish with a barycentre regulating mechanism was first built by combining Newton’s second law for linear motion and Euler’s equation for angular motion. Then, the model parameters were determined by 3-D computer-aided design software SolidWorks, HyperFlow-based computational fluid dynamics simulation, and grey-box model estimation method. Finally, both kinematic experiments with a prototype and numerical simulations were applied to validate the dynamic model’s accuracy mutually. Based on the dynamic model, multiple 3-D motions, including rectilinear motion, turning motion, surfacing motion, and spiral motion, were analyzed. The experimental and simulation results demonstrated the effectiveness of the proposed model in evaluating the trajectory, attitude, and motion parameters such as the velocity, turning radius, turning angular velocity of the robotic fish.

Few-Shot aerial image classification with deep economic network and teacher knowledge

ABSTRACT Deep learning has achieved excellent achievements and has become the mainstream in the field of aerial image classification. While obtaining remarkable success, deep learning-based approaches are notoriously dependent on large amounts of labelled data. Few-shot learning uses existing knowledge to learn from few samples and quickly generalizes to new tasks. In this work, we proposed the few-shot learning with deep economic network and teacher knowledge for aerial image classification. Firstly, we performed simplification twice to reduce large-scale parameters and computational effort in deep networks. In the first simplification, the redundancy in feature inputs’ main components is reduced, and the implicit information in redundant components is extracted instead of directly discarding the redundant components. The channel and spatial redundancies in deeper layers’ inputs are reduced in the second simplification. Secondly, the teacher knowledge guides the random sampling and uses limited samples to improve classification performance. We conducted extensive experiments on NWPU-RESISC45, RSD46-WHU, and UC Merced datasets. The experimental results reveal that the proposed method has better classification accuracy, fewer network parameters, and less computational effort. Experiments on miniImageNet, FC100, CUB, and cross-domain datasets show that our method also maintains advanced classification accuracy on few-shot image classification benchmarks.

Hybrid millimetre‐wave channel simulation approach based on long short‐term memory networks

In order to overcome the disadvantages of traditional channel simulation approaches and achieve more accurate millimetre-wave (mmWave) channel simulation under the condition of limited measured data, a novel Long Short-term Memory Networks (LSTM) based hybrid mmWave channel simulation approach is proposed in this work. The proposed hybrid approach takes advantages of LSTM-based time-varying model of path loss and large-scale channel parameters, statistical model of intra-cluster multipath parameters and ray tracing, which are able to accurately simulate path loss, large-scale channel parameters, multipath parameters and channel impulse responses, respectively. Based on the mmWave channel data measured in the waiting hall of Qingdaobei Railway Station, it is verified that the simulation results of our proposed hybrid approach are in good agreement with the measured data and better than that of existing channel simulation approaches. The hybrid channel simulation approach proposed in this work has important application value for channel modelling and simulation in the case of small amount of data.

A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics

A reservoir computer (RC) is a type of recurrent neural network architecture with demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman filter—used in numerical weather prediction to compensate for sparse and noisy data. We explore here the architecture and design choices for a “best in class” RC for a number of characteristic dynamical systems. Our analysis points to the importance of large scale parameter optimization. We also note in particular the importance of including input bias in the RC design, which has a significant impact on the forecast skill of the trained RC model. In our tests, the use of a nonlinear readout operator does not affect the forecast time or the stability of the forecast. The effects of the reservoir dimension, spinup time, amount of training data, normalization, noise, and the RC time step are also investigated. Finally, we detail how our investigation leads to optimal design choices for a parallel RC scheme applied to the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics.

Analysing spatio-temporal patterns in wintertime rainfall across Iran’s deserts using GPM DPR data

Although known as the warmest deserts of the world, rainfall and integrated water of Lut and Kavir Deserts is still unknown due to insufficient weather stations. The Dual-Frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) mission satellite created an opportunity for use to study the rainfall and vertically integrated liquid water content (LWC) and integrated non-liquid (ice) water content (IWC) using statistical and distance analyses over the two deserts during winter months (December to March) of 2015–2020. The results showed good similarity between the GPM DPR and station data with a Pearson correlation coefficient of 0.81. March has the highest contribution in rain amount, which is about 37% in both deserts. March also has the largest contribution of the rainy days, being about 41.4 and 37.6% in the Lut and Kavir Deserts, respectively. LWC has the highest amount in the Lut Desert, while IWC is the largest in the Kavir Desert. Distance analysis showed that there is a significant increasing trend of rainfall from west to east in the Lut Desert. Elevation does not affect rainfall distribution strongly, but rainfall is highly influenced by the atmospheric-driven large-scale parameters.

Improvement of the Cluster-Level Spatial Consistency of Channel Simulator With Reference Points Transition Method

Spatial consistency is a basic physical characteristic of the wireless channel because the neighboring spatial positions share similar clusters, resulting in the spatial correlations of both large-scale parameters (LSPs) and small-scale parameters (SSPs). Although the existing geometry-based stochastic channel model (GSCM)-based channel simulator try to realize the spatial consistency of the channel parameters by the correlated map method or the sum-of-sinusoids (SOS) method, such a stochastic approach also results in that the change of cluster positions calculated by the SSPs appears considerable even in closely neighboring spatial positions, which violates the wide-sense stationary (WSS) conditions over a short distance. To further improve the cluster-level spatial consistency, we investigate the pattern of change of the root mean square (RMS)-delay spread (DS) and derive the continuity of RMS-DS in the entire space. Taking the continuity of RMS-DS as the optimization goal, we determine the change of clusters of the channel, i.e., cluster appearance and disappearance, when the spatial position of mobile terminal (MT) changes and propose the reference points (RPs) transition method. Specifically, we firstly choose the RPs in the target area according to the correlation distance, then generate the channels of RPs by the SOS method stochastically, and finally obtain the channel of MT through the transitions between the RPs deterministically. Simulation results demonstrate that the channel simulator with the proposed RPs transition method can achieve the spatial consistency of LSPs, SSPs, and clusters in the entire 2D or 3D space.

Semi-Deterministic Dynamic Millimeter-Wave Channel Modeling Based on an Optimal Neural Network Approach

Billions of mobile terminals will be deployed in various Internet of Things (IoT), in which millimeter-wave (mmWave) technology will be gradually applied. Accurate modeling and simulation of wireless channel is the base for efficient design and performance evaluation. This becomes more important for industrial scenarios, which might be highly dynamic and potentially different from well-investigated cellular deployment scenarios. In this work, a novel semi-deterministic mmWave dynamic channel modeling approach based on optimal neural network (ONN) principle is proposed. The ONNs are radial basis function (RBF) neural networks (NNs) trained with optimal variance parameters and are applied to predict large-scale channel parameters (LSCPs) [e.g., path loss (PL), delay spread (DS), and angle spread (AS)]. Based on the ONNs’ predicted large-scale parameters and simplified propagation environment including the layout of transmitter (TX), receiver (RX), and major scatterers, the proposed channel modeling approach can generate accurate dynamic channel parameters. The proposed approach is validated by the channel data measured at a high-voltage substation. Large-scale parameters, multipath component (MPC) distributions, and power delay profiles (PDPs) are validated. The proposed approach is demonstrated to be an accurate, fast, and robust channel modeling method, which can be used for both link-level and system-level channel simulation for future design and optimization of industrial IoT.

Long-Term Trends in Laying date and Fledged Young of Barn Swallow Hirundo rustica in Two Regions of the Netherlands

Since 1992, Dutch volunteers have monitored the breeding attempts of Barn Swallows on a systematic basis. For two regions – Noord-Veluwe and Twente – a dataset of 27 years (1992–2018) was available. This paper examines the timing of breeding and the number of fledglings per successful breeding attempt. As spring and summer weather in Western Europe became on average warmer during this period, we analysed whether Barn Swallows adapted to this shift. The laying date of first broods advanced on average 2.3 days per ten years. The average advancement of laying date of second broods was 2.9 days per decade. The average number of fledglings per successful breeding attempt was constant over time, but showed large inter-year-variation. First broods consistently produced 0.6 fledglings more than second broods. These results are in line with results from similar studies in Western Europe. Contrary to our expectations, neither large scale nor local weather parameters were found to explain the advancement of laying and the number of fledged young. It is essential to study key species like Barn Swallows during all stages of their life cycle over a long time span and large spatial scale to be able to unravel potential effects of climate change. Our study demonstrates that volunteers can contribute essential demographic data. Ideally, these demographic parameters would be monitored with individually marked (colour-ringed) birds. Frequent quality checks and feedback to volunteers are key to maximize the quality of the data collected through long-term volunteer surveys.

The parametrized superelement approach for lattice joint modelling and simulation

From large scale structures such as bridges and cranes to small-scale structures such as lattices, mechanical structures today increasingly consist of “tuneable parts”: parts with a simple geometry described by a small set of strongly varying parameters. For example, although a bridge is a macroscale structure with a complex geometry, it is made from simple beams, bolts and plates, all of which depend on spatially varying, geometrical parameters such as their length-to-width ratio. Accelerating this trend is the concurrent improvement of, on one hand, Additive Manufacturing techniques that allow for increasingly complex parts to be manufactured and, on the other, structural optimization techniques that exploit this expanded design space. However, this trend also poses a challenge to current simulation techniques, as for example the Finite Element Method requires large amounts of elements to represent such structures. We propose to exploit the large conformity between parts inside the mechanical structure through construction of semi-analytic “Parametrized Superelements”, built by meshing with solid elements, reduction to a fixed interface and approximation of the reduced stiffness matrices. These new elements can be employed next to standard elements, enabling the simulation of more complex geometries. The technique we propose is applied to lattice structures and provides a flexible, differentiable, more accurate but still efficient way to simulate their elastic response. A net gain in total computation time occurs after simulating more than twenty lattices. As such, the proposed method enables large-scale parameter exploration and optimization of lattice structures for improved energy absorption, mass and/or stiffness.

Inference-aware convolutional neural network pruning

Deep neural networks (DNNs) have become an important tool in solving various problems in numerous disciplines. However, DNNs are also known for their high resource requirement, weight redundancy, and large-scale parameters. As a result, the use of DNNs is restricted for devices that lack the necessary resources required to execute, especially resource-constrained devices such as mobile phones, wearable devices, and other edge devices. In recent years, pruning has emerged as an essential technique to reduce insignificant parameters and accelerate the model performance. However, finding the optimal number of parameters that can be pruned without significantly affecting the model performance is a time-consuming, tedious task and require a lot of manual tuning. This paper represent pruning as an optimization problem with the goal of improving DNN run-time inference performance by pruning low impacting parameters (filters) and their corresponding feature maps. To do this, we present a Bayesian optimization-based method for automatically determining the appropriate number of filters for each convolutional layer. Also, we proposed an objective function incorporating distinct model performance and resource-specific constraints. The proposed method is applied to two different kinds of convolutional network architectures (i.e., VGG16 and deeper network ResNet34) on CIFAR10, CIFAR100, and ImageNet datasets. The large-scale ImageNet experimental findings showed that the floating-point operations of the ResNet34 and VGG16 could be reduced by 35.46 percent and 84.97 percent, respectively, with negligible loss of accuracy.

Large-scale parameter modelling for millimeter-wave multiple-input multiple-output channel in 5G ultra-dense network

<span>Network densification (ND) in 5G has been suggested as a solution to improve network capacity. ND has small cell backhaul as its bottleneck in the ensuing ultra-dense network (UDN). Due to the new deployment scenarios of small cells, it becomes necessary to thoroughly investigate the radio-propagation characteristics of the new transmission path between the base station and the small cells. The problem of the impact of small cell height on the backhaul large-scale parameters under typical outdoor-to-indoor (high-rise) and outdoor-to-outdoor (street canyon) scenarios was first investigated. Next, the probability distribution functions of the various parameters were investigated and modeled. Novel use of 5G NR air interface using a deterministic ray-tracing engine to characterize the backhaul at <br /> 28 GHz center frequency and 100 MHz bandwidth using 4x4 cross-polarized uniform planar array (UPA) at the base station and 2x2 multiple input, multiple output (MIMO) antennas at the small cells was proposed. New sets of models for root mean square (RMS) delay spread and RMS angular spread suitable for predicting network deployment in the two scenarios and similar environments were presented.</span>

Impacts of Aerosols and Climate Modes on Tropical Cyclone Frequency over the North Indian Ocean: A Statistical Link Approach

Abstract North Indian Ocean (NIO) tropical cyclone activity is strongly influenced by aerosols and climate modes. In this study, we evaluated the impact of aerosols and climate modes on modulating tropical cyclone (TC) frequency over the NIO. A statistical generalized additive model based on Poisson regression was developed to assess their relative impacts. Aerosol optical depth for different compounds simulated by the Goddard Chemistry Aerosol Radiation and Transport model, sunspot number (SN) as solar variability, and eight climate modes—Atlantic meridional mode (AMM), El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean dipole (IOD), Pacific decadal oscillation (PDO), Pacific–North American teleconnection pattern (PNA), Arctic Oscillation (AO), and Antarctic Oscillation (AAO), all based on reanalysis datasets, were analyzed for the 40-yr period 1980–2019. A strong linkage was found between TC activity and the AMM, IOD, and ENSO over the NIO. In addition, black carbon, organic carbon, sea salt, and sulfate aerosols have a significant impact on the cyclone frequency. Among these factors, black carbon, organic carbon, sea salt, and AMM account for the most variance of TCs, and among the other climate modes, IOD contributes more than ENSO. This is the first attempt to have identified this ranked set of aerosols and climate indices according to their relative ability to impact NIO TCs. Possible linkages between the thermodynamic and dynamic effects of aerosols on the Indian monsoon environment and its modifications to the large-scale environmental parameters relevant to TC development, namely, sea surface temperature, vertical wind shear, relative vorticity, and relative humidity during different phases of the climate modes are discussed. Significance Statement Aerosols and climate modes have enormous impact on tropical cyclones (TCs). In this study, we evaluated the impact of aerosols and climate modes that modulate frequency of TCs over the north Indian Ocean. To assess the impact, a statistical generalized additive model based on Poisson regression was developed. A strong linkage was found between TC activity and Atlantic meridional mode, Indian Ocean dipole, and El Niño–Southern Oscillation, whereas other climate modes have no statistical significance. In addition, black carbon, organic carbon, sea salt, and SO4 aerosols have a strong linkage to cyclone frequency. The study postulates that most positive phases of these climate modes are associated with more TCs, while the negative phases are associated with fewer.

WISDOM Project – X. The morphology of the molecular ISM in galaxy centres and its dependence on galaxy structure

ABSTRACT We use high-resolution maps of the molecular interstellar medium (ISM) in the centres of 86 nearby galaxies from the millimetre-Wave Interferometric Survey of Dark Object Masses (WISDOM) and Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) surveys to investigate the physical mechanisms setting the morphology of the ISM at molecular cloud scales. We show that early-type galaxies tend to have smooth, regular molecular gas morphologies, while the ISM in spiral galaxy bulges is much more asymmetric and clumpy when observed at the same spatial scales. We quantify these differences using non-parametric morphology measures (Asymmetry, Smoothness, and Gini), and compare these measurements with those extracted from idealized galaxy simulations. We show that the morphology of the molecular ISM changes systematically as a function of various large-scale galaxy parameters, including galaxy morphological type, stellar mass, stellar velocity dispersion, effective stellar mass surface density, molecular gas surface density, star formation efficiency, and the presence of a bar. We perform a statistical analysis to determine which of these correlated parameters best predicts the morphology of the ISM. We find the effective stellar mass surface (or volume) density to be the strongest predictor of the morphology of the molecular gas, while star formation and bars maybe be important secondary drivers. We find that gas self-gravity is not the dominant process shaping the morphology of the molecular gas in galaxy centres. Instead effects caused by the depth of the potential well, such as shear, suppression of stellar spiral density waves, and/or inflow, affect the ability of the gas to fragment.

Impact of the Spring North Atlantic Oscillation on the Northern Hemisphere Tropical Cyclone Genesis Frequency

A majority of studies have documented basin-dependent factors for predicting interannual variability of tropical cyclone genesis frequency (TCGF) over basins. In this study, we find that the spring North Atlantic Oscillation (NAO) has cross-basin impacts on summer and autumn TCGF over the whole North Hemisphere. The positive NAO suppresses the TCGF in the North Atlantic (NA) but promotes the TCGF in the North Pacific (NP) via modulating the large-scale environment parameters and vice versa. The positive NAO in spring can induce negative sea surface temperature (SST) anomalies in the NA, which persists into summer via the ocean memory. The negative SST anomalies cool the overlying atmosphere and damp the precipitation, leading to a low-level anti-cyclonic circulation and thereby counteracting the TCGF over the NA in summer and autumn. The southerly anomaly west of the anti-cyclonic circulation increases the SST and precipitation over the northeast Pacific in summer. Accordingly, a cyclonic circulation appears western NP via Gill response and sustains by the warm advection via the air–ocean positive feedback, which devotes the NP TCGF in summer and autumn. The composite results in high-resolutions numerical model from Coupled Model Intercomparison Project Phase 6 further verify the relationship between the spring NAO and TCGF.