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At least 109 records · Page 6

Preliminary Evaluation of MapReduce for High-Performance Climate Data Analysis

MapReduce is an approach to high-performance analytics that may be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. We are particularly interested in the potential of MapReduce to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we are prototyping a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. Our initial focus has been on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. Preliminary results suggest this approach can improve efficiencies within data intensive analytic workflows.

Duffy, Daniel Q.↗

Evolution of X-Ray Activity in <25 Myr Old Pre-main Sequence Stars

Measuring the evolution of X-ray emission from pre-main-sequence (PMS) stars gives insight into two issues: the response of magnetic dynamo processes to changes in the interior structure, and the effects of high-energy radiation on protoplanetary disks and primordial planetary atmospheres. We present a sample of 6003 stars with ages 7–25 Myr in 10 nearby open clusters from Chandra X-ray and Gaia-EDR3 surveys. Combined with previous results in large samples of younger (≲5 Myr) stars in MYStIX and SFiNCs star-forming regions, mass-stratified activity-age relations are derived for the early phases of stellar evolution. X-ray luminosity (LX) is constant during the first few Myr, possibly due to the presence of extended X-ray coronas insensitive to temporal changes in stellar size. LX then decays during the 7–25 Myr period, more rapidly as stellar mass increases. This decay is interpreted as decreasing efficiency of the α2 dynamo as radiative cores grow and a solar-type αΩ dynamo emerges. For more massive 3.5–7 M⊙ fully radiative stars, the X-ray emission plummets—indicating the lack of an effective magnetic dynamo. The findings provide improved measurements of high-energy radiation effects on circumstellar material, first for the protoplanetary disk and then for the atmospheres of young planets. The observed X-ray luminosities can be so high that an inner Earth-mass rocky, unmagnetized planet around a solar-mass PMS star might lose its primary and secondary atmospheres within a few (several) million years. PMS X-ray emission may thus have a significant impact on the evolution of early-planetary atmospheres and the conditions promoting the rise of habitability.

Konstantin V Getman↗

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

GOES-1↗

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗

Stellar photometry with the Hubble Space Telescope Wide-field/Planetary camera - A progress report

The prospects for the use of the Wide-Field/Planetary Camera (WFPC) for stellar photometry are described. The large halos of the point-spread function (PSF) resulting from spherical aberration and from spatial, temporal, and color variations of the PSF are the main limitations to accurate photometry. Degradations caused by crowding are exacerbated by the halos of the PSF. An attempt is made to quantify these effects and determine the current accuracy of stellar photometry with the WFPC. In realistic cases, the brighter stars in crowded fields have 0.09 mag errors; fainter stars have larger errors depending on the degree of crowding. It is shown that measuring Cepheids in Virgo Cluster galaxies is not currently possible without inordinate increases in exposure times.

Holtzman, Jon A.↗

Multiyear Dry Periods in Southern Africa

Characteristics and physical features related to low precipitation across many years in Southern Africa that lead to societal disruptions are diagnosed using observed analyses and an ensemble of historical coupled climate model simulations during 1921 to 2014. Four regions are evaluated, as identified through a hierarchical clustering algorithm applied to the Standardized Precipitation Index (SPI) during the October–April precipitation season. Although dryness spanning many October–April occurs periodically in each region, they seldom occur simultaneously, consistent with largely insignificant SPI cross-correlations between them. However, characteristics relevant to low precipitation across many years are generalizable between the four regions, including the serial persistence of October–April precipitation, the likelihood of consecutive dry October–April, and the likelihood of dry October–April in temporal extents of up to 10 consecutive such 7-month seasons. Systematic precipitation persistence is not a feature in any of the four Southern Africa regions, as serial correlations of October–April SPI are not statistically significant at any time lags. It follows that there is an exponential-folding decay in the likelihood of consecutive October–April for various SPI thresholds and that there is a large spread in the likelihood of low October–April SPI across many years. In terms of physical features, low October–April SPI in each Southern Africa region is closely related to local atmospheric circulations; however, they are not as closely related to sea surface temperatures (SSTs). These results suggest that dryness spanning many years is determined primarily by persistent local circulations related to atmospheric variability and to a lesser extent variability related to SST anomalies, including the El Niño–Southern Oscillation.

subtropical Indian Ocean dipole↗

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Rainfall↗

Pathways for Energization of Ca in Mercury's Exosphere

We investigate the possible pathways to produce the extreme energy observed in the calcium exosphere of Mercury. Any mechanism must explain the facts that Ca in Mercury's exosphere is extremely hot, that it is seen almost exclusively on the dawnside of the planet, and that its content varies seasonally, not sporadically. Simple diatomic molecules or their clusters are considered, focusing on calcium oxides while acknowledging that Ca sulfides may also be the precursor molecules. We first discuss impact vaporization to justify the assumption that CaO and Ca-oxide clusters are expected from impacts on Mercury. Then we discuss processes by which the atomic Ca is energized to a 70,000 K gas. The processes considered are (1) electron-impact dissociation of CaO molecules, (2) spontaneous dissociation of Ca-bearing molecules following impact vaporization, (3) shock-induced dissociative ionization, (4) photodissociation and (5) sputtering. We conclude that electron-impact dissociation cannot produce the required abundance of Ca, and sputtering cannot reproduce the observed spatial and temporal variation that is measured. Spontaneous dissociation is unlikely to result in the high energy that is seen. Of the two remaining processes, shock induced dissociative ionization produces the required energy and comes close to producing the required abundance, but rates are highly dependent on the incoming velocity distribution of the impactors. Photodissociation probably can produce the required abundance of Ca, but simulations show that photodissociation cannot reproduce the observed spatial distribution.

Sulfides↗

Wildfire Emission Transport and Its Impact on the Local Air Quality – Case Study Example from the LISTOS Campaign

The high O3 concentration and large aerosol backscatter were measured in New York City(NYC) region and Connecticut (CT) coastline between August 15-16, 2018 during the Long Island Sound Tropospheric Ozone Study (LISTOS) campaign. Two TOLNet ozone lidar systems, NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar (GSFC TROPOZ DIAL)and Langley Research Center (LaRC) Mobile Ozone Lidar (LMOL),were used to obtain vertical and temporal variation of local O3 concentration. The airborne High Altitude Lidar Observatory (HALO) system was used to detect the regional aerosol characteristic during this episode. The complex relationship between ozone and aerosol characteristics of wildfire emission layers was investigated. The HYSPLIT back-trajectory of the measured air parcel shows that the increase of the O3 concentration and aerosol backscatter are attributed to the significant wildfires in the Pacific Northwest and British Columbia regions during August 2018. Through correlation analyses, unique clustering relationships are identified between ozone and aerosol for different air mass types. This case study is further investigated in relation to satellite data from MODIS, MISR and CALIPSO to characterize plume behavior during transport. The importance of wildfire emission transport will be discussed in context of its impact to surface air quality at significant distances from fire events.

Liqiao Lei↗

Fine-Scale Fluctuations in the Corona Observed with Hi-C

The High Resolution Coronal Imager(HiC) flew aboard a NASA sounding rocket on 2012 July11 and captured roughly 345 s of high spatial and temporal resolution images of the solar corona in a narrowband 193 Angstrom channel. We have analyzed the fluctuations in intensity of Active Region11520.We selected events based on a lifetime greater than 11s (twoHiC frames)and intensities greater than a threshold determined from the average background intensity in a pixel and the photon and electronic noise. We find fluctuations occurring down to the smallest timescale(~11s).Typical intensity fluctuations are 20% background intensity, while some events peaka t100%the background intensity.Generally the fluctuations are clustered in solar structures, particularly the moss.We interpret the fluctuations in the moss as indicative of heating events. We use the observed events to model the active region core.

Winebarger, Amy↗

Extracting Independent Local Oscillatory Geophysical Signals by Geodetic Tropospheric Delay

Zenith Tropospheric Delay (ZTD) due to water vapor derived from space geodetic techniques and numerical weather prediction simulated-reanalysis data exhibits non-linear and non-stationary properties akin to those in the crucial geophysical signals of interest to the research community. These time series, once decomposed into additive (and stochastic) components, have information about the long term global change (the trend) and other interpretable (quasi-) periodic components such as seasonal cycles and noise. Such stochastic component(s) could be a function that exhibits at most one extremum within a data span or a monotonic function within a certain temporal span. In this contribution, we examine the use of the combined Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA): the EEMD-ICA algorithm to extract the independent local oscillatory stochastic components in the tropospheric delay derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) over six geodetic sites (HartRAO, Hobart26, Wettzell, Gilcreek, Westford, and Tsukub32). The proposed methodology allows independent geophysical processes to be extracted and assessed. Analysis of the quality index of the Independent Components (ICs) derived for each cluster of local oscillatory components (also called the Intrinsic Mode Functions (IMFs)) for all the geodetic stations considered in the study demonstrate that they are strongly site dependent. Such strong dependency seems to suggest that the localized geophysical signals embedded in the ZTD over the geodetic sites are not correlated. Further, from the viewpoint of non-linear dynamical systems, four geophysical signals the Quasi-Biennial Oscillation (QBO) index derived from the NCEP/NCAR reanalysis, the Southern Oscillation Index (SOI) anomaly from NCEP, the SIDC monthly Sun Spot Number (SSN), and the Length of Day (LoD) are linked to the extracted signal components from ZTD. Results from the synchronization analysis show that ZTD and the geophysical signals exhibit (albeit subtle) site dependent phase synchronization index.

Botai, O. J.↗

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗

The response of the near earth magnetotail to substorm activity

The large scale structure of the current sheet in the terrestrial magnetotail is often represented as the superposition of a constant northward-oriented magnetic field component (B(sub z)) and a component along the Earth-Sun direction (B(sub x)) that varies with distance from the center of the sheet (z(sub o) in GSM) as in a Hams neutral sheet. The latter implies that B(sub x) = B(sub Lx) tanh((z - z(sub o))/h) where B(sub Lx) is the magnitude of the B(sub x) component in the northern lobe. Correspondingly, the cross-tail current should be approximated by J(sub y) = (B(sub Lx)/h) sech(sup 2)((z - z(sub o))/h). Using data from the fluxgate magnetometer (FGM) on the Cluster I1 spacecraft tetrad, we have used measured fields and currents to ask if this model represents the large-scale properties of the system. During very quiet crossings of the plasmasheet, we find that the model gives a reasonable estimate of the trend of the average current and field distributions, but during disturbed intervals, the best fit fails to represent the data. If, however, the parameters z(sub o) and h of the model are taken as variable functions of time, the fits can be reasonably good. The temporal variation of the fit parameter h that characterizes the thickness of the current sheet can be interpreted in terms of thinning during the growth phase of a substorm and thickening following the expansion phase. Ground signatures that give insight into the local time of substorm onset can be used to interpret the response of the plasmasheet to substorm related changes of the global system. During a substorm, the field magnitude in the central plasmasheet fluctuates at the period of Pi2 pulsations.

Kivelson, M. G.↗

Plasma sheet turbulence observed by Cluster II

Cluster fluxgate magnetometer (FGM) and ion spectrometer (CIS) data are employed to analyze magnetic field fluctuations within the plasma sheet during passages through the magnetotail region in the summers of 2001 and 2002 and, in particular, to look for characteristics of magnetohydrodynamic (MHD) turbulence. Power spectral indices determined from power spectral density functions are on average larger than Kolmogorov's theoretical value for fluid turbulence as well as Kraichnan's theoretical value for MHD plasma turbulence. Probability distribution functions of the magnetic fluctuations show a scaling law over a large range of temporal scales with non-Gaussian distributions at small dissipative scales and inertial scales and more Gaussian distribution at large driving scales. Furthermore, a multifractal analysis of the magnetic field components shows scaling behavior in the inertial range of the fluctuations from about 20 s to 13 min for moments through the fifth order. Both the scaling behavior of the probability distribution functions and the multifractal structure function suggest that intermittent turbulence is present within the plasma sheet. The unique multispacecraft aspect and fortuitous spacecraft spacing allow us to examine the turbulent eddy scale sizes. Dynamic autocorrelation and cross correlation analysis of the magnetic field components allow us to determine that eddy scale sizes fit within the plasma sheet. These results suggest that magnetic field turbulence is occurring within the plasma sheet resulting in turbulent energy dissipation.

Weygand, James M.↗

Subseasonal and interannual variations of tropical convection and the El Nino/Southern Oscillation

It was found that during the northern winter, fluctuation in tropical convection is dominated by eastware propagating cloud clusters from the Indian Ocean. Most of these clusters reach as far as the central Pacific. Longitude time sections of outgoing longwave radiation (OLR) anomaly along the equator show that there are about 3 to 4 such episodes each winter. Also, simultaneous OLR anomalies over the equatorial central Pacific (ECP) and the maritime continent (MC) are negatively correlated and their variations are dominated by a 40 to 60 day period, suggesting the presence of a standing wave phenomenon. Such combination of propagating and standing phenomena can be captured by the extended empirical orthogonal function (EEOF) analysis. The EEOF method combines the temporal as well as the spatial correlation information to produce eigenmodes which have space time dependence. A 5 day and a 10 day lag in addition to the simultaneous spatial correlation is used.

Lau, K. M.↗

Some Thermodynamic Considerations on the Physical and Quantum Nature of Space and Time

It is suggested that the Planck h = m(sub k)c Lambda(sub k) and the Boltzmann k = m(sub k)c nu(sub k)Constants have stochastic foundation. It is further suggested that a body of fluid at equilibrium is composed of a spectrum of molecular clusters (energy levels) the size of which are governed by the Maxwell-Boltzmann distribution function. Brownian motions are attributed to equilibrium between suspensions and molecular clusters. Atomic (molecular) transition between different size atomic- (molecular-) clusters (energy levels) is shown to result in emission/absorption of energy in accordance with Bohr's theory of atomic spectra. Physical space is identified as a tachyonic fluid that is Dirac's stochastic ether or de Broglie's hidden thermostat. Compressibility of physical space, in accordance with Planck's compressible ether, is shown to result in the Lorentz-Fitzgerald contraction, thus providing a causal explanation of relativistic effect in accordance with the perceptions of Poincare and Lorentz. The invariant Schrodinger equation is derived from the invariant Bernoulli equation for incompressible potential flow. Following Heisenberg a temporal uncertainty relation is introduced as Delta(nu(sub Beta)) Delta(Rho(sub Beta)) > = k.

Sohrab, Siavash H.↗

The SOHO-Stellar Connection

I discusses practical aspects of the so-called "solar-stellar" connection; namely, the fundamental principles, the tools at the disposal of the stellar astronomer, and a few recent examples of the connection in action. I provide an overall evolutionary context for coronal activity, calling attention to the very different circumstances of low mass main sequence stars like the Sun, which are active mainly early in their lives; compared with more massive stars, whose coronally active phase occurs near the end of their lives, during their brief incursion into the cool half of the Hertzsprung-Russell diagram as yellow and then red giants. On the instrumental slide, I concentrate primarily on spectroscopy, in the ultraviolet and X-ray bands where coronae leave their most obvious signatures. I present an early glimpse of the type of moderate resolution spectra we can expect from the recently launched Chandra observatory, and contemporaneous HST STIS high-resolution UV measurements of the CXO calibration star Capella (alpha Aur; G8 III + G1 III). I compare STIS spectra of solar-type dwarfs-zeta Dor (F7 V), an active coronal source; and alpha Cen A (G2 V), a near twin of the Sun-to a trace obtained with the SOHO SUMER imaging UV spectrometer. I also compare STIS line profiles of the active coronal dwarf to the corresponding features in the mixed-activity "hybrid-chromosphere" bright giant alpha TrA (K2 II) and the archetype "noncoronal" red giant Arcturus (alpha Boo; K2 III). The latter shows dramatic evidence for a "cool absorber" in its outer atmosphere that is extinguishing the "hot lines" (like Si IV lambda1393 and N V lambda1238) below about 1500 A, probably through absorption in the Si I lambda1525 and C I lambda1240 photoionization continua. The disappearance of coronae across the "Linsky-Haisch" dividing line near K1 III thus apparently is promoted by a dramatic overturning in the outer atmospheric structure, namely the coronae of the red giants seem to lie beneath their extended chromospheres, rather than outside as in the Sun. I then discuss an intriguing long-slit STIS low-resolution observation of an X-ray active late-A dwarf in the nearby Hyades cluster: the spatially resolved UV spectroscopy clearly shows that a previously unseen close companion (a dKe or dMe) likely is responsible for the coronal activity, rather than some unexplained departure of the A dwarf from its expected state of X-ray dormancy. Finally, I describe early results from a joint observing campaign involving SOHO SUMER, TRACE, and the Kitt Peak Infrared Imaging Spectrometer, conducted May 1999, to explore the dynamics of the quiet solar atmosphere through the key "magnetic transition zone" that separates the kinetically dominated deep photosphere from the magnetically dominated coronal regime. Linking spatially and temporally resolved solar properties to aspects of the averaged lineshapes (for example: widths, asymmetries, intensity ratios, and Doppler shifts) is a crucial step in carrying any physical wisdom we develop in the solar setting to the distant stars.

Ayres, Thomas R.↗