The "middle corona" is a critical transition between the highly disparate physical regimes of the lower and outer solar corona. Nonetheless, it remains poorly understood due to the difficulty of observing this faint region (1.5-3 R☉). New observations from the GOES Solar Ultraviolet Imager in August and September 2018 provide the first comprehensive look at this region's characteristics and long-term evolution in extreme ultraviolet (EUV). Our analysis shows that the dominant emission mechanism here is resonant scattering rather than collisional excitation, consistent with recent model predictions. Our observations highlight that solar wind structures in the heliosphere originate from complex dynamics manifesting in the middle corona that do not occur at lower heights. These data emphasize that low-coronal phenomena can be strongly influenced by inflows from above, not only by photospheric motion, a factor largely overlooked in current models of coronal evolution. This study reveals the full kinematic profile of the initiation of several coronal mass ejections, filling a crucial observational gap that has hindered understanding of the origins of solar eruptions. These new data uniquely demonstrate how EUV observations of the middle corona provide strong new constraints on models seeking to unify the corona and heliosphere. Our EUV Observations and the Middle CoronaThe solar corona is the primary driver of almost all plasma dynamics throughout the solar system 1 . However, the precise nature of the connection between the corona and the heliosphere remains surprisingly poorly understood 2 . Recent solar and heliospheric observations taken by Parker Solar Probe, well within Mercury's orbit, revealed a highly structured environment shaped by flows and ejecta interacting with the corona's complex magnetic field 3,4,5,6 . The influence of these flows on the heliosphere and structural evolution

The four Solar Ultraviolet Imagers (SUVI) on board the Geostationary Operational Environmental Satellite (GOES)‐16 and GOES‐17 and the upcoming GOES‐T and GOES‐U weather satellites serve as National Oceanic and Atmospheric Administration's operational solar coronal imagers. These four identically designed solar Extreme UltraViolet instruments are similar in design and capability to the Solar Dynamics Observatory‐Atmospheric Imaging Assembly suite of solar telescopes, and are planned to operationally span two solar cycles or more, from 2017 through 2040. We present the concept of operations for the SUVI instruments, operational requirements, and constraints. The reader is also introduced to the instrument design, testing, and performance characteristics. Finally, the various data products are described along with their potential utility to the operational user or researcher.

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In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using extreme ultraviolet and Hα images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012), a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm are best highlighted in: comparison of outputs to hand-drawn maps; response to short-term variability; and tracking long-term changes on the Sun. Our work indicates that the next generation of solar image classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.

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The four Solar UltraViolet Imagers on board the GOES-16 and GOES-17 and the upcoming GOES-T and GOES-U weather satellites serve as NOAA's operational solar coronal imagers. These four identically designed solar EUV instruments are similar in design and capability to the SDO-AIA suite of solar telescopes, and are planned to operationally span two solar cycles or more, from 2017 through 2040. We present the concept of operations for the SUVI instruments, operational requirements, and constraints. The reader is also introduced to the instrument design, testing, and performance characteristics. Finally, the various data products are described along with their potential utility to the operational user or researcher.

We have mapped cold atomic gas in 21cm line HI self-absorption (HISA) at arcminute resolution over more than 90% of the Milky Way's disk. To probe the formation of H2 clouds, we have compared our HISA distribution with CO J=1-0 line emission. Few HISA features in the outer Galaxy have CO at the same position and velocity, while most inner-Galaxy HISA has overlapping CO. But many apparent inner-Galaxy HISA-CO associations can be explained as chance superpositions, so most inner-Galaxy HISA may also be CO-free. Since standard equilibrium cloud models cannot explain the very cold HI in many HISA features without molecules being present, these clouds may instead have significant CO-dark H2.Comment: 2 pages, 1 figure, to appear in proceedings of IAU Symposium 315, "From Interstellar Clouds to Star-Forming Galaxies: Universal Processes?

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