Range Of Spatial Scales Research Articles

CHEX-MATE: Pressure profiles of six galaxy clusters as seen by SPT and Planck

Context. Pressure profiles are sensitive probes of the thermodynamic conditions and the internal structure of galaxy clusters. The intra-cluster gas resides in hydrostatic equilibrium within the dark-matter gravitational potential. However, this equilibrium may be perturbed; for example, as a consequence of thermal energy losses, feedback, and non-thermal pressure supports. Accurate measures of the gas pressure over cosmic time are crucial for constraining cluster evolution as well as the contributions from astrophysical processes. Aims. In this work we present a novel algorithm for deriving the pressure profiles of galaxy clusters from the Sunyaev-Zeldovich (SZ) signal measured on a combination of Planck and South Pole Telescope (SPT) observations. The synergy of the two instruments makes it possible to track the profiles on a wide range of spatial scales. We exploited the sensitivity of the Planck High-Frequency Instrument to the larger scales in order to observe the faint peripheries, and took advantage of the higher spatial resolution of SPT to solve the innermost regions. Methods. We developed a two-step pipeline to take advantage of the specifications of each instrument. We first performed a component separation on the two data sets separately in order to remove the background (CMB) and foreground (Galactic emission) contaminants. We then jointly fitted a parametric pressure profile model on a combination of Planck and SPT data. Results. We validated our technique on a sample of six CHEX-MATE clusters detected by SPT. We compare the results of the SZ analysis with profiles derived from X-ray observations with XMM-Newton. We find excellent agreement between these two independent probes of the gas pressure structure.

Direct Simulation of the Surface Manifestation of Internal Gravity Waves with a Wave–Current Interaction Model

Abstract Internal solitary waves in the ocean are characterized by the surface roughness signature of smooth and rough bands that are observable in synthetic aperture radar satellite imagery, which is caused by the interaction between surface gravity waves and internal wave–induced surface currents. In this work, we study the surface signature of an internal wave packet in deep water over a large range of spatial scales using an improved wave–current interaction model that supports a moving surface current corresponding to a propagating internal gravity wave. After validating the model by comparison to previously published numerical results by Hao and Shen, we investigate a realistic case based on a recent comprehensive field campaign conducted by Lenain and Pizzo. Distinct surface manifestation caused by internal waves can be directly observed from the surface waves and the associated surface wave steepness. Consistent with observations, the surface is relatively rough where the internal wave–induced surface current is convergent (∂U/∂x < 0), while it is relatively smooth where the surface current is divergent (∂U/∂x > 0). The spatial modulation of the surface wave spectrum is rapid as a function of along-propagation distance, showing a remarkable redistribution of energy under the influence of the propagating internal wave packet. The directional wavenumber spectra computed in the smooth and rough regions show that the directional properties of the surface wave spectra are also rapidly modulated through strong wave–current interactions. Good agreement is found between the model results and the field observations, demonstrating the robustness of the present model in studying the impact of internal waves on surface gravity waves. Significance Statement The purpose of this study is to better understand the physical processes leading to the bands of rough and smooth surface waves arising from internal gravity waves. The surface manifestation of internal gravity waves allows them to be measured remotely via surface imagery, which can provide insight into their nonlinear behavior and sources and fate and which can ultimately inform the local stratification for assimilation into larger-scale models. Our results highlight the application of wave–current interaction models to the study of the interaction of surface waves with internal gravity waves and indicate strong modulation of the surface wave spectra over relatively short time scales despite the long time scales associated with the internal wave propagation.

Fourier Analysis of Magnetic Fields in the Ionosphere of Mars

AbstractThe two main sources of the magnetic field in the Martian ionosphere are the solar wind interaction with the planet, and, mainly in the southern hemisphere, remnant crustal magnetization. The magnetic fields measured by the Mars Atmosphere and Volatile EvolutioN (MAVEN) and Mars Global Surveyor spacecraft displayed a wide range of spatial scales, from the global (i.e., L ≈ 103 km) to mesoscale (L ≈ 102 km) to small‐scale (L < 10 km). Hamil et al. (2022) used MAVEN magnetometer and Langmuir Probe data to study these structures and suggested that they might be advected into the ionosphere from the solar wind and magnetosheath. In the current study, we apply a Fourier analysis to the fields and interpret the resulting power spectral density profiles versus frequency. The power spectral density function found from MAVEN data resembles that of the solar wind magnetic field (or interplanetary magnetic field) (i.e., power law with an index of about −2), but shifted upward in frequency by a factor of about 100. From a comparison of ionospheric power spectra with solar wind power spectra, we deduce that plasma, carrying a magnetic field with it moves from the magnetic pile‐up region downward into the ionosphere with speeds of roughly tens of meters per second. The derived power spectra in the ionosphere, in addition to the basic power law shape, show hints of extra power at a spatial scale of about 10 km, and this might be due to the creation of a magnetic structure within the ionosphere itself.

Population synchrony indicates functional connectivity in a threatened sedentary butterfly

Dispersal is a key influence on species’ persistence, particularly in the context of habitat fragmentation and environmental change. Previously, residual population synchrony has been demonstrated to be an effective proxy for dispersal in mobile butterflies (Powney et al. 2012). Here, we highlight the utility and limitations of population synchrony as an indicator of functional connectivity and persistence, at a range of spatial scales, in a specialist, sedentary butterfly. While at the local scale, population synchrony is likely indicative of dispersal in the pearl-bordered fritillary, Boloria euphrosyne, over larger scales, habitat is likely to influence population dynamics. Although declines in local-scale synchrony conformed to typical movement in this species, synchrony showed no significant trend with distance when studied at larger (between-site) scales. By focusing on specific site comparisons, we draw the conclusion that heterogeneity in habitat successional stage drives asynchrony between sites at larger distances and is, therefore, likely to be a more important driver of population dynamics over large distances than dispersal. Within-site assessments of synchrony highlight differences in dispersal based on habitat type, with movement shown to be most inhibited between transect sections with contrasting habitat permeability. While synchrony has implications for metapopulation stability and extinction risk, no significant difference was found in average site synchrony between sites that had gone extinct during the study period and those remaining occupied. We demonstrate that population synchrony may be used to assess local-scale movement between sedentary populations, as well as to understand barriers to dispersal and guide conservation management.

Multiple facets of cutting-edge mycorrhizal research: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, August 2022: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, 1-5 August 2022.

Multiple facets of cutting-edge mycorrhizal research: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, August 2022: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, 1-5 August 2022.

Capturing High‐Resolution Air Pollution Features Using the Multi‐Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) Global Modeling System

AbstractThe Multi‐Scale Infrastructure for Chemistry and Aerosols (MUSICA) enables the study of atmospheric chemistry and aerosols at global to local scales. MUSICA version 0 (MUSICAv0) is a configuration of the Community Earth System Model (CESM) with regional refinement (RR). We compared the regional scale spatial and temporal variabilities of air pollutants simulated by this newly developed multiscale model and a widely used regional model—WRF‐Chem (the Weather Research and Forecasting model coupled with Chemistry). The O3 and PM2.5 mean biases of the two WRF‐Chem simulations over the U.S. are 5–9 ppb and −3 μg/m3 during August and September 2020. The MUSICAv0 simulation has larger O3 mean bias (11 ppb) but smaller PM2.5 mean bias (−1 μg/m3) compared to the two WRF‐Chem simulations. As indicated by spatial and temporal statistical measures, the variability in meteorological parameters (temperature, precipitation), and ozone and PM2.5 are similar across a range of temporal and spatial scales in MUSICAv0 and WRF‐Chem. We demonstrate the new capability of the global MUSICAv0 at regional scale through two examples—(a) plumes from western U.S. fires that passed through the domain boundary of WRF‐Chem and were transported back into the domain and impacted air quality on the eastern U.S.; and (b) impact of stratospheric intrusion on the troposphere.

Finding patterns of atoll morphometrics at a range of spatial scales

Small Island Nations, often comprised of Atolls, are at considerable risk of climate change impacts from sea-level rise to coral acidification to increasing cyclone intensity; understanding how they will change in the coming century is vital for climate mitigation and resiliency. However, the morphology of atolls are not well quantified or summarized. In this work, we calculate modern atoll morphometrics on a global scale including 3,786 motu and 593 reef flats on 154 atolls. Temporal composites of Landsat imagery are created for 4 years (2015–2018), and are classified into motu, reef flat, open water/lagoon via unsupervised classification. Morphometrics, including widths, lengths, and area, are computed for each motu and reef flat of the atoll in Python, creating a database of atolls and their associated morphometrics. Consistency in automated processing of atoll morphometrics removes spatial and user bias, enabling a better understanding of geographic patterns of atoll area and other morphometrics. Distinct differences in the distribution of motu are seen on a regional scale in French Polynesia, while globally, wider reef flats and larger motu are found closest to the equator. Globally, there is a consistent reef flat width in front of large motu (>1 km length) of 184 ± 93 m (n = 724). Our atoll morphometric database creates a baseline of current atoll characteristics that can be expanded upon in the future and used for evaluating temporal changes to atoll islands.

Regional analysis of the 2015–16 Lower Mekong River basin drought using NASA satellite observations

Study regionLower Mekong River Basin (LMRB) Study focusSatellite remote sensing products are widely used for monitoring droughts. Using NASA satellite sensors of precipitation (Global Measurement Mission, GPM), soil moisture (Soil Moisture Active and Passive, SMAP), and terrestrial water storage (Gravity Recovery and Climate Experiment, GRACE), this study evaluates the historical drought in the LMRB during 2015–16. SMAP soil moisture was validated against in-situ soil moisture, and GPM precipitation and SMAP soil moisture were cross-validated with streamflow observations. The spatiotemporal dynamics of soil moisture were also examined in different ranges of catchment areas. In performing the analysis, we used lagged correlations between hydrological variables and the indices of the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI). New hydrological insights for the regionsSpatio-temporal patterns of drought in 2015–16 were examined from the entire basin to small watersheds. A mismatch occurs when using GRACE data to study droughts in small watersheds (many of the small watersheds would be a fraction of the few 100 km2 spatial resolutions of GRACE pixel). In smaller watersheds, hydrological drought (SSI) was closely defined with SMAP soil moisture downscaled to 1 km rather than the meteorological drought index (SPI). By leveraging satellite-based observations across a range of spatial scales, this study highlights the utility of Earth observations in informing water resources and land management decisions at the regional scale.

Rossby waves past the breaking point in zonally dominated turbulence

The spontaneous emergence of structure is a ubiquitous process observed in fluid and plasma turbulence. These structures typically manifest as flows which remain coherent over a range of spatial and temporal scales, resisting statistically homogeneous description. This work conducts a computational and theoretical study of coherence in turbulent flows in the stochastically forced barotropic$\beta$-plane quasi-geostrophic equations. These equations serve as a prototypical two-dimensional model for turbulent flows in Jovian atmospheres, and can also be extended to study flows in magnetically confined fusion plasmas. First, analysis of direct numerical simulations demonstrates that a significant fraction of the flow energy is organized into coherent large-scale Rossby wave eigenmodes, comparable with the total energy in the zonal flows. A characterization is given for Rossby wave eigenmodes as nearly integrable perturbations to zonal flow Lagrangian trajectories, linking finite-dimensional deterministic Hamiltonian chaos in the plane to a laminar-to-turbulent flow transition. Poincaré section analysis reveals that Lagrangian flows induced by the zonal flows plus large-scale waves exhibit localized chaotic regions bounded by invariant tori, manifesting as Rossby wave breaking in the absence of critical layers. It is argued that the surviving invariant tori organize the large-scale flows into a single temporally and zonally varying laminar flow, suggesting a form of self-organization and wave stability that can account for the resilience of the observed large-amplitude Rossby waves.

Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests

Biodiversity monitoring represents a major challenge to supporting proper forest ecosystem management and biodiversity conservation. The latter is indeed shifting in recent years from single-species to multi-taxon approaches. However, multi-taxonomic studies are quite rare due to the effort required for performing field surveys. In this context, remote sensing is a powerful tool, continuously providing consistent and open access data at a different range of spatial and temporal scales. In particular, the Sentinel-2 (S2) mission has great potential to produce reliable proxies for biological diversity. In beech forests of two Italian National Parks, we sampled the beetle fauna, breeding birds, and epiphytic lichens. First, we calculated Shannon’s entropy and Simpson’s diversity. Then, to produce variables for biodiversity assessment, we exploited S2 data acquired in the 4 years 2017–2021. S2 images were used to construct spectral bands and photosynthetic indices time series, from which 91 harmonic metrics were derived. For each taxon and multi-taxon community, we assessed the correlation with S2 harmonic metrics, biodiversity indices, and forest structural variables. Then, to assess the potential of the harmonic metrics in predicting species diversity in terms of Shannon’s and Simpson’s biodiversity indices, we also fit a random forests model between each diversity index and the best 10 harmonic metrics (in terms of absolute correlation, that is, the magnitude of the correlation) for each taxon. The models’ performance was evaluated via the relative root mean squared error (RMSE%). Overall, 241 beetle, 27 bird, and 59 lichen species were recorded. The diversity indices were higher for the multi-taxon community than for the single taxa. They were generally higher in the CVDA site than in GSML, except for the bird community. The highest correlation values between S2 data and biodiversity indices were recorded in CVDA for multi-taxon and beetle communities (| r| = 0.52 and 0.38, respectively), and in GSML for lichen and beetle communities (| r| = 0.34 and 0.26, respectively). RMSE% ranged between 2.53 and 9.99, and between 8.1 and 16.8 for the Simpson and Shannon index, respectively. The most important variables are phase and RMSE of red-Edge bands for bird and lichen communities, while RMSE and time of tassel cap and from EVI indices for beetles and multi-taxon diversity. Our results demonstrate that S2 data can be used for identifying potential biodiversity hotspots, showing that the herein presented harmonic metrics are informative for several taxa inhabiting wood, giving concrete support to cost-effective biodiversity monitoring and nature-based forest management in complex mountain systems.

Reconciling the paradox of soil organic carbon erosion by water

Abstract. The acceleration of erosion, transport, and burial of soil organic carbon (OC) by water in response to agricultural expansion represents a significant perturbation of the terrestrial C cycle. Recent model advances now enable improved representation of the relationships between sedimentary processes and OC cycling, and this has led to substantially revised assessments of changes in land OC as a result of land cover and climate change. However, surprisingly a consensus on both the direction and magnitude of the erosion-induced land–atmosphere OC exchange is still lacking. Here, we show that the apparent soil OC erosion paradox, i.e., whether agricultural erosion results in an OC sink or source, can be reconciled when comprehensively considering the range of temporal and spatial scales at which erosional effects on the C cycle operate. We developed a framework that describes erosion-induced OC sink and source terms across scales. We conclude that erosion induces a source for atmospheric CO2 when considering only small temporal and spatial scales, while both sinks and sources appear when multi-scaled approaches are used. We emphasize the need for erosion control for the benefits it brings for the delivery of ecosystem services, but cross-scale approaches are essential to accurately represent erosion effects on the global C cycle.

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Mapping near-field pollutant concentration is essential to track plume dispersion in urban areas. By solving most of the turbulence spectrum, large-eddy simulations (LES) can accurately represent concentration spatial variability. Synthesizing this large amount of information to improve the accuracy of lower-fidelity operational models is particularly appealing. Due to the computational cost of LES, it becomes a challenge in multi-query contexts, to quantify how tracer dispersion changes with atmospheric and source parametric variations. We propose a data-driven reduced-order model (ROM) combining proper orthogonal decomposition (POD) and probabilistic Gaussian process regression (GPR) to efficiently predict tracer concentration field first- and second-order statistics. The novelty of this work lies in the original approach used to efficiently tune the GPR based on the available reduced-basis information and properties. In practice, the GPR hyperparameters are optimized through the hierarchy of POD modes thanks to Bayesian optimization informed by POD. The robustness of the approach is also addressed under small data constraint. A detailed analysis of our model performance is carried out in the case of a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle for which the convective character of the system together with the uncertainty of the pollutant source location make it very challenging for ROM. The model is able to capture the wide range of spatial scales across the POD modes. The ROM accuracy remains acceptable for a minimal budget of about one hundred LES snapshots, providing a first estimation moving towards more realistic atmospheric dispersion applications.

An adaptive wavelet method for nonlinear partial differential equations with applications to dynamic damage modeling

Multiscale and multiphysics problems need novel numerical methods in order for them to be solved correctly and predictively. To that end, we develop a wavelet-based technique to solve a coupled system of nonlinear partial differential equations (PDEs) while resolving features on a wide range of spatial and temporal scales. The algorithm exploits the multiresolution nature of wavelet basis functions to solve initial-boundary value problems on finite domains with a sparse multiresolution spatial discretization. By leveraging wavelet theory and embedding a predictor-corrector procedure within the time advancement loop, we dynamically adapt the computational grid and maintain accuracy of the solutions of the PDEs as they evolve. Consequently, our method provides high fidelity simulations with significant data compression. We present verification of the algorithm and demonstrate its capabilities by modeling high-strain rate damage nucleation and propagation in nonlinear solids using a novel Eulerian-Lagrangian continuum framework.

Statistical Characteristics of the Multiscale SST Fractal Structure over the Kuroshio Extension Region Using VIIRS Data

The ocean behaves as a typical multiscale fractal structure, whose dynamic and thermal variabilities extend over a wide range of spatial scales, r, spanning from 10−3 to 107 m. Studying the statistical characteristics of multiscale fractal structures is crucial to understanding the interactions and energy cascade processes between different spatial scales. Remote sensing data are one of the best choices for revealing these statistical characteristics. This work analyzes the multiscale (1–1000 km) fractal structures of sea surface temperature (SST) from the Level-2+ (L2P) satellite orbit Visible Infrared Imaging Radiometer Suite (VIIRS) products over the Kuroshio Extension (KE) region (145°E–160°W, 20°N–50°N), using a conventional method (second-order structure function, D(r)) and a newly developed statistical method (spatial variance, V(r)). The results show that both the power-law distribution slopes of D(r) and V(r) are close to 2/3, which is equivalent to the −5/3 wavenumber spectrum. V(r) is found to be more robust when depicting the fractal structure and variance density, V’(r), compared with D(r). V’(r) is slightly larger at the mesoscale (50–150 km) than at the large scale (higher than 150 km) and is much smaller than that at the submesoscale (smaller than 50 km). Additionally, V’(r) has an indiscernible diurnal variation but remarkable seasonal and latitudinal variations. For the seasonal variability, the maximum V’(r) appears in winter at the large scale and mesoscale, and gradually shifts towards spring at the submesoscale, which implies that a forward energy cascade process may occur during this period. The maximum of the latitude-dependent V’(r) appears around 40°N for all the scales. It is consistent with the latitude of the strongest background SST gradient, indicating that the background SST front is the main source of the strong SST multiscale spatial variabilities over the KE region. This work benefits the application of other high-resolution remote sensing data in this research field, including the forthcoming Surface Water Ocean Topography (SWOT) satellite product.

Isotopic approach to linking landscape and catchment storage across multiple spatial scales

Catchment heterogeneity leads to uncertainty about estimating timescales of storage and difficulty in extrapolating to larger scales. A heterogeneity-independent storage index (SI) was employed to build a connection with catchment characteristics, especially for landscapes, over six humid, forestry catchments. The SI is derived from the amplitudes of tracer cycles in precipitation and ephemeral/intermittent streamflow. The SI was significantly correlated with landscape metric (R2 = 0.81 and p = 0.014), specifically the fractal dimension index (FRAC) indicating the shape complexity of the landscape. Meanwhile, the topographic wetness index (TWI) (R2 = 0.77, p = 0.021) showed a close connection with the storage index as well. Close correlations suggested that the FRAC and TWI regulate the timescale of storage over the range of spatial scales, in particular the young water fraction in streams, because the SI is a heterogeneity-independent metric derived from seasonal tracer cycle amplitudes, which has a close relation to young water fraction. This also indicated the SI can be treated as an alternative travel-time metric and a reflection of the hydrological nature. Moreover, the findings implied the possibility of hydrological modeling over multiple spatial scales. The linkage between SI and landscape and topography provides insight into the mechanism of streamflow generation. Based on ephemeral streams, the crucial control of landscape and topography on the timescale of storage contributes to available water assessment and sustainable ecosystem management.

The Impact of Stochastic Mesoscale Weather Systems on the Atlantic Ocean

Abstract The ocean is forced by the atmosphere on a range of spatial and temporal scales. In numerical models the atmospheric resolution sets a limit on these scales and for typical climate models mesoscale (<500 km) atmospheric forcing is absent or misrepresented. Previous studies have demonstrated that mesoscale forcing significantly affects key ocean circulation systems such as the North Atlantic subpolar gyre (SPG) and the Atlantic meridional overturning circulation (AMOC). Here we present ocean model simulations that demonstrate that the addition of realistic mesoscale atmospheric forcing leads to coherent patterns of change: a cooler sea surface in the tropical and subtropical Atlantic Ocean and deeper mixed layers in the subpolar North Atlantic in autumn, winter, and spring. These lead to robust statistically significant increases in the volume transport of the North Atlantic SPG by 10% and the AMOC by up to 10%. Our simulations use a novel stochastic parameterization—based on a cellular automata algorithm—to represent spatially coherent weather systems realistically over a range of scales, including down to the smallest resolvable by the ocean grid (∼10 km). Convection-permitting atmospheric models predict changes in the intensity and frequency of mesoscale weather systems due to climate change, so representing them in coupled climate models would bring higher fidelity to future climate projections.

Comprehensive fluorescence lifetime fluctuation spectroscopy to investigate protein mobility during aggregation processes in living cells.

How proteins move and interact in the complicated, crowded cellular environment is still unknown. The formation of membrane-less condensates, caused by interacting proteins and RNA molecules, in the so-called liquid-to-liquid phase transition has recently raised scientific interest. Primarily related to protein mutations, the condensates can undergo a liquid-to-solid transition often associated with neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS). Here, we introduce a novel method based on fluorescence fluctuation spectroscopy (FFS) as a tool to study proteins interactions, movement, and aggregate formation comprehensively. FFS is a family of analytical methods that allow quantifying molecular processes by studying the fluorescence intensity fluctuations generated by a population of fluorescent molecules entering and leaving a confined microscope focal volume. We enhance conventional FFS methods by employing a novel single-photon-avalanche-diode array detector in an ISM confocal microscope, which allows us to spatially and temporally tag single photons. Within the same system, we can systematically, and with high throughput, access a wide range of spatial and temporal scales, leading to a comprehensive knowledge of the process. In particular, we can follow slow changes in the overall cell organization by performing time-lapse super-resolved image scanning microscopy, fast-slow molecular movements on the level of single proteins, e.g., time-resolved FFS, and interaction with dual-color cross-correlation methods. Moreover, we introduce fluorescence lifetime fluctuation spectroscopy, a novel paradigm where the molecule's mobility and diffusion mode are simultaneously investigated with fluorescence lifetime, allowing us to directly correlate changes in diffusion mode/mobility with structural changes in the molecule's environment. We validated our methods by measuring the time evolution of stress granules formation, focusing on G3BP1 and FUS proteins, in an in vitro cellular system that recapitulates several features of the ALS pathology.

Fluctuating characteristics of streamwise wind speed and total saltation mass flux in the near-neutral atmospheric surface layer

Eolian sand transport in the atmospheric surface layer (ASL) is a typical kind of gas–solid two-phase flow at a very high Reynolds number that fluctuates over a wide range of spatial and temporal scales. Based on the high-frequency time series of streamwise wind speed (u) and total saltation mass flux (q) measure at three different observation sites, the fluctuating characteristics of u and q in the near-neutral ASL have been analyzed. Our study suggests that the probability density function (PDF) of the normalized streamwise wind speed fluctuations at the height of approximately 0.5 m follows a standard normal distribution, and the PDF of the q presents a lognormal distribution in the near-neutral ASL during strong and steady wind-blown sand. The investigations on the premultiplied spectral of q and u show that, as the energy of very large-scale motions (VLSMs) increases, the influence of the VLSMs on q becomes more significant. In addition, the stochastic couplings between u and q have been analyzed by using space–time correlations and linear coherence spectrum. Our results suggest that the VLSMs have a significant effect on the low-frequency fluctuations of q in the near-neutral ASL.

Phase-modulated Rice model for statistical distributions of complex signals.

The basic Rice model is commonly used to describe complex signal statistics from randomly scattered waves. It correctly describes weak (Born) scattering, as well as fully saturated scattering, and smoothly interpolates between these extremes. However, the basic Rice model is unsuitable for situations involving scattering by random inhomogeneities spanning a broad range of spatial scales, as commonly occurs for sound scattering by turbulence in the atmospheric boundary layer and other scenarios. In such scenarios, the phase variations are often considerably stronger than those predicted by the basic Rice model. Therefore, the basic Rice model is extended to include a random modulation in the signal phase, which is attributable to the influence of the largest, most energetic inhomogeneities in the propagation medium. Various joint and marginal distributions for the complex signal statistics are derived to incorporate the phase-modulation effect. Approximations of the phase-modulated Rice model involving the Nakagami distribution for amplitude, and the wrapped normal and von Mises distributions for phase, are also developed and analyzed. The phase-modulated Rice model and various approximations are shown to greatly improve agreement with simulated data for sound propagation in the near-ground atmosphere.

Unified Relationship between Cold Plasma Ejections and Flare Energies Ranging from Solar Microflares to Giant Stellar Flares

We often find spectral signatures of chromospheric cold plasma ejections accompanied by flares in a wide range of spatial scales in the solar and stellar atmospheres. However, the relationship between physical quantities (such as mass, kinetic energy, and velocity) of cold ejecta and flare energy has not been investigated in a unified manner for the entire range of flare energies to date. This study analyzed the spectra of cold plasma ejections associated with small-scale flares and solar flares (energy 1025–1029 erg) to supply smaller energy samples. We performed Hα imaging spectroscopy observation by the Solar Dynamics Doppler Imager on the Solar Magnetic Activity Research Telescope. We determined the physical quantities of the ejecta by cloud model fitting to the Hα spectrum. We determined the flare energy by differential emission measure analysis using the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory for small-scale flares and by estimating the bolometric energy for large-scale flares. As a result, we found that the ejection mass M and the total flare energy E tot follow a relation of . We show that the scaling law derived from a simple physical model explains the solar and stellar observations with a coronal magnetic field strength as a free parameter. We also found that the kinetic energy and velocity of the ejecta correlate with the flare energy. These results suggest a common mechanism driven by magnetic fields to cause cold plasma ejections with flares on the Sun and stars.