Cloud Structure Research Articles

The second Venus flyby of BepiColombo mission reveals stable atmosphere over decades

Studies of the Venusian mesosphere provide important information about the current state of the entire Venusian atmosphere. This includes information about the dense cloud structure, its vertical thermal profile, temperature fields, and the resulting dynamical and meteorological processes that contribute to a deeper understanding of the climatologically different evolutionary paths of Earth and Venus. However, the last measurements were acquired in 1983 during Venera-15 mission. In this paper, results of mid-infrared spectral measurements of the Venusian atmosphere are presented. Here we show Mercury Radiometer and Thermal Infrared Spectrometer (MERTIS) measurements of the Venusian atmosphere during the second flyby of BepiColombo mission on its way to Mercury. Our Venus measurements provide reliable retrievals of mesospheric temperature profiles and cloud parameters between 60 and 75 km altitude, although MERTIS was only designed to operate in Mercury environment. Our results are in good agreement with the Venera-15 mission findings. This indicates the stability of the Venusian atmosphere on time scales of decades.

A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements

The estimate of precipitation from satellite measurements is an indirect estimate if compared to rain gauges or disdrometer measurements, but it has the advantage of complete coverage over oceans, mountainous regions, and sparsely populated areas where other sources of precipitation data (e.g., weather radar) are unavailable or unreliable. Among the satellite-based precipitation estimates, geostationary (GEO) data ensure the highest spatial and temporal resolution. At the same time, the IR/VIS channels deployed on GEO satellites have lower capabilities than microwave (MW) channels in characterizing the cloud structure. Machine learning (ML) techniques can be considered a powerful tool to overcome the limitations related to the physical relationship between IR/VIS channels and precipitation estimation. This study describes the development of a convolutional neural network (U-Net) to retrieve the precipitation rate using IR measurements only from the Meteosat Second Generation (MSG) satellite. Its performances are evaluated through a comparison with H SAF and NASA operational products (e.g., H60B or H03B and IMERG-E, respectively), of which the algorithms are based on different principles. The results highlight a lower error in precipitation rate estimates for the U-Net with respect to the other products but also some issues in correctly estimating the more intense precipitation (>5 mmh−1). On the other hand, the precipitation detection capabilities of the U-Net outperform the H SAF products for lower precipitation rate, while IMERG-E shows the best performance regardless of the precipitation regime. Furthermore, the U-Net is able to account for and correct the parallax displacement that affects the measurement as the satellite viewing angle increases.

Geometric Properties of Water-ice Clouds as Observed from Jezero Crater in the First 600 sols with the NavCam Instrument On Board the Mars2020 Rover, Perseverance

In the first 600 sols of the Mars2020 mission, LS 5.6o – 316.8o, 46 cloud movies and 145 cloud surveys were collected to observe clouds at Jezero Crater, the landing site of the Perseverance Rover. Cloud movies were processed using the Mean-Frame Subtraction (MFS) method for revealing cloud structures, which were subsequently analyzed using digital-image processing. Two-dimensional Fast Fourier Transforms (2D-FFT) were used to compute cloud structure sizes ranging from 2.90 to 15.25 km for clouds between 30 and 50 km altitude, based on coincident Mars Climate Sounder vertical profiles of atmospheric water-ice. Same-value thresholding was used to detect the cloud structures in MFS-processed and projected cloud movies. The resolution dependence that was needed to resolve these structures over various thresholds was examined to find multifractal scaling of Mars clouds for resolutions between 0.1 and 1.6 km. We characterize the multiscaling observed in the images and its implications for the design of cloud-tracking cameras from the surface as well as for cloud-resolving models.

Asymptotic Matching between Weather and Climate Models

Abstract The Deep Numerical Analysis for Climate (DNA-Climate) is a pilot project to develop an Earth system model on a kilometer-scale horizontal mesh. The acronym “DNA” is based on the analogies between the hierarchical structures of atmospheric phenomena and living organisms. The multiscale structure of clouds and circulations may be analogous to the multiscale structure of cells and organs organized according to the blueprint, deoxyribonucleic acid (DNA). Whereas global cloud-resolving models (CRMs) can produce better solutions on shorter time scales that are decisively governed by the initial conditions, global climate models (GCMs) may generate reliable solutions on longer time scales that are largely determined to balance energy inputs and outputs. Our challenge is to build a physically valid model that consistently bridges the shorter- and longer-time-scale solutions in the intermediate time scales. Research topics of DNA-Climate are configured in consideration of the structural similarity between the climate modeling and the technique of matched asymptotic expansions in mathematics. The central question is whether a single modeling framework using only either global CRM or GCM will work adequately at all time scales of climate, or whether a multiscale modeling framework combining several models, of which each is only valid for limited time scales, will be needed. A multiscale modeling is an attractive framework for advancing climate modeling and would be an intriguing topic to be studied in parallel with global CRMs and GCMs.

Airborne Radar Doppler Spectrum Width as a Scale-Dependent Turbulence Metric

Abstract High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases. Significance Statement Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.

Modelling molecular clouds and CO excitation in AGN-host galaxies

ABSTRACT We present a new physically motivated model for estimating the molecular line emission in active galaxies. The model takes into account (i) the internal density structure of giant molecular clouds (GMCs), (ii) the heating associated with both stars and the active galactic nuclei (AGNs), respectively, producing photodissociation regions (PDRs) and X-ray-dominated regions (XDRs) within the GMCs, and (iii) the mass distribution of GMCs within the galaxy volume. The model needs, as input parameters, the radial profiles of molecular mass, far-UV flux and X-ray flux for a given galaxy, and it has two free parameters: the CO-to-H2 conversion factor αCO, and the X-ray attenuation column density NH. We test this model on a sample of 24 local (z ≤ 0.06) AGN-host galaxies, simulating their carbon monoxide spectral-line energy distribution (CO SLED). We compare the results with the available observations and calculate, for each galaxy, the best (αCO, NH) with a Markov chain Monte Carlo algorithm, finding values consistent with those present in the literature. We find a median αCO = 4.8 M⊙ (K km s−1 pc2)−1 for our sample. In all the modelled galaxies, we find the XDR component of the CO SLED to dominate the CO luminosity from Jupp ≥ 4. We conclude that, once a detailed distribution of molecular gas density is taken into account, PDR emission at mid-/high-J becomes negligible with respect to XDR.

The Magellanic Puzzle: origin of the periphery

ABSTRACT In this paper, we analyse the metallicity structure of the Magellanic Clouds using parameters derived from the Gaia Data Release 3 (DR3) low-resolution XP (for Blue/Red Photometer) spectra, astrometry, and photometry. We find that the qualitative behaviour of the radial metallicity gradients in the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) is quite similar, with both of them having a metallicity plateau at intermediate radii and a second at larger radii. The LMC has a first metallicity plateau at [M/H] ≈ −0.8 for 3–7°, while the SMC has one at [M/H] ≈ −1.1 for 3–5°. The outer LMC periphery has a fairly constant metallicity of [M/H] ≈ −1.0 (10–18°), while the outer SMC periphery has a value of [M/H] ≈ −1.3 (6–10°). The sharp drop in metallicity in the LMC at ∼8° and the marked difference in age distributions in these two regions suggest that there were two important evolutionary phases in the LMC. In addition, we find that the Magellanic periphery substructures, likely Magellanic debris, are mostly dominated by LMC material stripped off in old interactions with the SMC. This presents a new picture in contrast with the popular belief that the debris around the clouds had been mostly stripped off from the SMC due to having a lower mass. We perform a detailed analysis for each known substructure and identify its potential origin based on metallicities and motions with respect to each galaxy.

Case Studies of the Microphysical and Kinematic Structure of Summer Mesoscale Precipitation Clouds over the Eastern Tibetan Plateau

Case Studies of the Microphysical and Kinematic Structure of Summer Mesoscale Precipitation Clouds over the Eastern Tibetan Plateau

Characteristics and Variability of Precipitation Across Different Sectors of an Extra‐Tropical Cyclone: A Case Study Over the High‐Latitudes of the Southern Ocean

AbstractShipborne observations from the CAPRICORN‐2018 field campaign were used to investigate the characteristics and variability of precipitation across different sectors of an extra‐tropical cyclone on 16 February 2018, over the Southern Ocean (SO). Three distinct time periods—frontal, post‐frontal, and cyclone—were identified during the day. The frontal passage recorded a total accumulation of 1.9 mm, where the precipitation phases were primarily composed of rain (96%), while the cyclone period recorded the largest precipitation (4.0 mm), where the precipitation phases varied with snow (10%), mixed‐phase (40%), and rain (50%). The BASTA radar suggests the freezing level was shallow (∼500 m) with snow present above. The cloud top heights, observed by a C‐band radar, were shallower in the cyclone period, although deeper cloud depths of ∼6 km were sporadically recorded. Increased surface fluxes and a southerly wind direction indicate that cold air advection, was likely the main cause of high precipitation during the cyclone period. A non‐precipitating multi‐layer cloud structure with a geometrically thin (200 m) homogeneous layer of supercooled liquid water (SLW) overlaying shallow boundary layer convection was seen during the post‐frontal period. The ship‐borne observations were used to evaluate Weather Research & Forecasting (WRF) simulations with different microphysics settings. We found the frontal precipitation intensity is well reproduced, but it is underestimated during the cyclone period. This study represents a unique set of observations and highlights the need for understanding how ice processes and potentially horizontal advection contribute to the development of precipitation and convection over the SO.

Deep convective cloud system size and structure across the global tropics and subtropics

Abstract. A new database is constructed comprising millions of deep convective clouds that spans the global tropics and subtropics for the entire record of the MODIS instruments on the Terra and Aqua satellites. The database is a collection of individual cloud objects ranging from isolated convective cells to mesoscale convective cloud systems spanning hundreds of thousands of square kilometers in cloud area. By matching clouds in the database with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis dataset and microwave imager brightness temperatures from the Advanced Microwave Scanning Radiometer – EOS (AMSR-E) instrument, the database is designed to explore the relationships among the horizontal scale of cloud systems, the thermodynamic environment within which the cloud resides, the amount of aerosol in the environment, and indicators of the microphysical structure of the clouds. We find that the maximum values of convective available potential energy and vertical shear of the horizontal wind associated with a cloud impose a strong constraint on the size attained by a convective cloud system, although the relationship varies geographically. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems. The observed relationships between cloud system size and structure from this database can be compared with similar relationships derived from simulated clouds in atmospheric models to evaluate the representation of clouds and convection in weather forecast and climate projection simulations, including whether models exhibit the same relationships between the atmospheric environment and cloud system size and structure. Furthermore, the dataset is designed to probe the impacts of aerosols on the size and structure of deep convective cloud systems.

The Diurnal Cycle of Rainfall and Deep Convective Clouds Around Sumatra and the Associated MJO‐Induced Variability During Austral Summer in Himawari‐8

AbstractThe effects of the diurnal cycle and large‐scale atmospheric disturbances dominate rainfall and cloud variability in the Maritime Continent. This study examines the modulation of the Austral Summer diurnal cycle by the Madden–Julian Oscillation (MJO) using cloud populations through precipitation and deep convective cloud derived from satellite measurements. Using Rainfall Potential Areas from Himawari‐8 Advanced Himawari Imager as a proxy for deep convection, our analysis shows that convective clouds are present ∼55% of the time over land in Sumatra during the afternoon and night. Cloud signatures reveal semi‐diurnal structures of deep convective clouds off the West Coast of Sumatra. In contrast, the East Coast exhibits explicit sea‐ward propagation patterns of deep convective controlled by the coastal effects around the Strait of Malacca and Java Sea, together with the influence of synchronized diurnal forcing between islands. We show that the MJO drives the enhanced convective phases, changing the cloud top type distribution, moisture convergence, and moisture transport over the equatorial Indian Ocean. The cold cloud area also increases during the MJO active phases, which is linked to frequent deep convective cloud development near the mountain ranges of Sumatra and the adjacent ocean. The analyses of cloud variations based on the rainfall potential areas and cloud top type provide evidence of the effects of convective processes on the diurnal cycle of ice and water vapor distribution in the troposphere.

Completing 3D Point Clouds of Thin Corn Leaves for Phenotyping Using 3D Gridding Convolutional Neural Networks

Estimating the complete 3D points of crop plants from incomplete points is vital for phenotyping and smart agriculture management. Compared with the completion of regular man-made objects such as airplanes, chairs, and desks, the completion of corn plant points is more difficult for thin, curled, and irregular corn leaves. This study focuses on MSGRNet+OA, which is based on GRNet, to complete a 3D point cloud of thin corn plants. The developed MSGRNet+OA was accompanied by gridding, multi-scale 3DCNN, gridding reverse, cubic feature sampling, and offset-attention. In this paper, we propose the introduction of a 3D grid as an intermediate representation to regularize the unorganized point cloud, use multi-scale predictive fusion to utilize global information at different scales, and model the geometric features by adding offset-attention to compute the point position offsets. These techniques enable the network to exhibit good adaptability and robustness in dealing with irregular and varying point cloud structures. The accuracy assessment results show that the accuracy of completion using MSGRNet+OA is superlative, with a CD (×10−4) of 1.258 and an F-Score@1% of 0.843. MSGRNet+OA is the most effective when compared with other networks (PCN, shape inversion, the original GRNet, SeedFormer, and PMP-Net++), and it improves the accuracy of the CD (×10−4)/F-Score@1% with −15.882/0.404, −15.96/0.450, −0.181/0.018, −1.852/0.274, and −1.471/0.203, respectively. These results reveal that the developed MSGRNet+OA can be used to complete a 3D point cloud of thin corn leaves for phenotyping.

GoComfort: Comfortable Navigation for Autonomous Vehicles Leveraging High-Precision Road Damage Crowdsensing

Recent years have witnessed rapid advances in autonomous driving technologies. Autonomous vehicles are more likely to be accepted if they drive comfortably to avoid potholes, bumps, and other road damage conditions, especially when there are elderly and disabled passengers on board. Traditionally, sensing road damage conditions is either labor-intensive by field investigation and reporting, or inaccurate by surveillance cameras and driving recorders due to limited perspective. In this paper, we propose GoComfort, a crowdsensing-based framework to provide low-cost and fine-grained comfortable navigation for autonomous vehicles with road damage identification leveraging high-precision road sensing data. First, we propose to exploit city-wide autonomous vehicle fleets as crowdsensing participants, and employ an edge-cloud-hybrid computing paradigm to efficiently collect high-precision road damage-related data, including 3D LiDAR point clouds and street view images. Second, we design an accurate road damage identification model fusing spatial structures of point clouds and texture features of street view images, and use an active learning-based method to address the sparse labels issue. Finally, we devise two comfortable navigation scenarios, i.e., fine-grained road damage avoidance and coarse-grained city-wide navigation, and propose a hierarchical road damage assessment diagram for comfortable route planning. Experiments using real-world road sensing data in Xiamen, China show that our approach identifies road damage conditions with an accuracy of 87.5%, and achieves a user acceptance rate of 93.8% in riding comfort evaluation, outperforming the state-of-the-art baselines.

Cloud type identification algorithm to simulate MSG infrared radiance using the Radiative Transfer Model RTTOV and ALADIN forecasting output

In numerical weather prediction (NWP), the big challenger in satellite data assimilation is the presence of cloud essentially the convective cloud. In this work, we focus only on the simulation of SEVIRI radiances of MSG2 satellite, using the radiative transfer operator RTTOV in its 9.3 version, by introducing the fields provided by the model ALADIN/Algeria. This simulation can have two objectives. The first one is the validation of the simulated radiances for data assimilation; the second one is to generate synthetic images that will serve as a help tool to the localization of the storms using the short time forecast. Many improvements have been made in the source code for these operators in order to try to resolve this problem. This latest model RTTOV assimilates cumuliform clouds, stratiform clouds and the ones of the upper levels as cirrus. For the simulation of these clouds, we will developed an algorithm for the identification of cloud types and cloud structure, based on the vertical profile of the cloud liquid water (CLW) and the cloud solid water (CSW) forecasted by ALADIN model at all the forty three (43) levels of the RTTOV model. A comparative study using the MSG2 data has shown on a number of weather situations, a good correlation between simulated and observed radiances, but this correlation is locally random for isolated clouds.

Dense gas properties around the centre of the Circinus galaxy

ABSTRACT Studies of spatial distribution, kinematics, and physical structure of dense molecular gas in the centres of galaxies is crucial in understanding the nature of physical processes, especially those related to active galactic nuclei (AGNs) and starbursts, in the vicinity of central massive black holes. However, our current understanding of dense gas in the circumnuclear discs (CND) remains limited, particularly those around AGNs. Here-, we report high-resolution deep observations of the CS (3−2), HC3N (15−14), H2CO (2−1), CH3CCN (8−7), CO (1−0), and (3−2) lines, toward the central region (R ∼ 0.4 kpc) of the nearest Seyfert-2 galaxy, the Circinus galaxy, using the Atacama Large Millimeter/submillimeter Array. The dense molecular gas traced by CS (3−2) and HC3N (15−14) exhibit an asymmetric and cloud structure, which is in sharp contrast against the symmetric low-density structure traced by CO (1−0). Four molecular cloud structures surrounding the AGN, which have a typical size of ∼ 20 pc, are detected with the optically thin HC3N (15−14) line. HC3N (15−14)/CO (1−0) line ratio in the CND is found to be higher than that in the nuclear region, indicating that the bulk of dense gas accumulates in the CND, instead of the nucleus. HC3N (15−14) line and optically thick tracers in four molecular clouds, provide a good comparison between density layers and show evidence for a density stratification of the CND.

TransPCGC: Point Cloud Geometry Compression Based on Transformers

Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCGC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size.

Enhancing IoT Connectivity and Services for Worldwide Ships through Multi-Region Fog Cloud Architecture Platforms

As technologies such as eco-friendly ships, electric propulsion vessels, and multi-fuel propulsion systems advance, the scope of IoT applications in maritime fields is expanding, resulting in increased complexity in control factors. The gradual progression towards Maritime Autonomous Surface Ships (MASS) is further driving the evolution of ship-based IoT applications. These advancements underscore the necessity for a platform capable of ensuring reliable connectivity between ships and onshore. The limitations of the existing single cloud architecture become evident in this context. In response to these emerging challenges, this paper presents a cloud-based data platform structure anchored in the architecture of a multi-region fog cloud. Concurrently, we propose a strategic approach aimed at enhancing collision avoidance performance. This is achieved through the seamless sharing of navigation plan data among ships facilitated by the proposed platform structure. In regions densely populated with ships, there looms a potential for packet loss as data traffic sharing intensifies through the platform. To address this concern, we devised a traffic model based on the AIS data generation cycle and proposed an algorithm for subscription decision. Subsequently, we conducted comparative analyses of packet loss probabilities between the single cloud structure and the multi-region fog cloud structure. This was achieved through experimental packet loss data collected via the AWS cloud. Simulation results underscored a notable difference: with 100 subscribed ships, the packet loss probability in regions assuming a single cloud was about 28 times higher compared to the same region within the multi-region fog cloud structure. These simulations affirm the stable and effective implementation of the proposed collision avoidance performance enhancement method within the multi-region fog cloud structure. Furthermore, feasibility was corroborated through the successful implementation of the proposed platform via the AWS Cloud.

Detection of 3D Object in Point Cloud: Cloud Semantic Segmentation in Lane Marking

Managing a city efficiently and effectively is more important than ever as growing population and economic strain put a strain on infrastructure like transportation and public services like keeping urban green areas clean and maintained. For effective administration, knowledge of the urban setting is essential. Both portable and stationary laser scanners generate 3D point clouds that accurately depict the environment. These data points may be used to infer the state of the roads, buildings, trees, and other important elements involved in this decision-making process. Perhaps they would support "smart" or "smarter" cities in general. Unfortunately, the point clouds do not immediately supply this sort of data. It must be eliminated. This extraction is done either by human specialists or by sophisticated computer programmes that can identify objects. Because the point clouds might represent such large locations, relying on specialists to identify the things may be an unproductive use of time (streets or even whole cities). Automatic or nearly automatic discovery and recognition of essential objects is now possible with the help of object identification software. In this research, In this paper, we describe a unique approach to semantic segmentation of point clouds, based on the usage of contextual point representations to take use of both local and global features within the point cloud. We improve the accuracy of the point's representation by performing a single innovative gated fusion on the point and its neighbours, which incorporates the knowledge from both sets of data and enhances the representation of the point. Following this, we offer a new graph point net module that further develops the improved representation by composing and updating each point's representation inside the local point cloud structure using the graph attention block in real time. Finally, we make advantage of the global structure of the point cloud by using spatial- and channel-wise attention techniques to construct the ensuing semantic label for each point.

The Global Structure of Molecular Clouds. I. Trends with Mass and Star Formation Rate

We introduce a model for the large-scale, global 3D structure of molecular clouds. Motivated by the morphological appearance of clouds in surface density maps, we model clouds as cylinders, with the aim of backing out information about the volume density distribution of gas and its relationship to star formation. We test our model by applying it to surface density maps for a sample of nearby clouds and find solutions that fit each of the observed radial surface density profiles remarkably well. Our most salient findings are that clouds with higher central volume densities are more compact and also have lower total mass. These same lower-mass clouds tend to have shorter gas depletion times, regardless of whether we consider their total mass or dense mass. Our analyses lead us to conclude that cylindrical clouds can be characterized by a universal structure that sets the timescale on which they form stars.

What Are the Radial Distributions of Density, Outflow Rates, and Cloud Structures in the M82 Wind?

Galactic winds play essential roles in the evolution of galaxies through the feedback they provide. Despite intensive studies of winds, the radial distributions of their properties and feedback are rarely observable. Here we present such measurements for the prototypical starburst galaxy, M82, based on observations by the Subaru Telescope. We determine the radial distribution of outflow densities (n e ) from the spatially resolved [S ii] λλ6717, 6731 emission lines. We find that n e drops from 200 to 40 cm−3 with radius (r) between 0.5 and 2.2 kpc with a best-fit power-law index of r −1.2. Combined with resolved Hα lines, we derive mass, momentum, and energy outflow rates, which drop quite slowly (almost unchanged within error bars) over this range of r. This suggests that the galactic wind in M82 can carry mass, momentum, and energy from the central regions to a few kiloparsecs with minimal losses. We further derive outflow cloud properties, including size and column densities. The clouds we measure have pressures and densities that are too high to match those from recent theoretical models and numerical simulations of winds. By comparing with a sample of outflows in local star-forming galaxies studied with UV absorption lines, the above-derived properties for M82 outflows match well with the published scaling relationships. These matches suggest that the ionized gas clouds traced in emission and absorption are strongly related. Our measurements motivate future spatially resolved studies of galactic winds, which is the only way to map the structure of their feedback effects.