Geographic tongue or benign migratory glossitis is a condition of an unknown cause characterized by chronic lesions that slowly migrate across the surface of the tongue. The condition’s characteristic wavefronts suggest that it can be modeled as a reaction–diffusion system. Here, we present a model for geographic tongue pattern evolution using reaction–diffusion equations applied to portions of spheroids and paraboloids that approximate a tongue shape. We demonstrate that the observed patterns of geographic tongue lesions can be explained by propagating reaction–diffusion waves on these variably curved surfaces.

The severity of wildfires is increasing and has driven increases in nighttime fire activity. Enhanced capability to detect the active burning regions of wildfires at night could significantly improve the effectiveness of wildfire management operations. Potassium line emission in the NIR near 770 nm is a signature of active burning. We test the use of multi-band imaging from an aircraft at night to distinguish a wood-burning fire from artificial light sources. We find that a simple ratio of the signals in two broad bands, one including 770 nm, effectively discriminates the fire from artificial light sources. This offers the possibility of nighttime fire detection with high spatial resolution using silicon sensors sensitive in the NIR.

Feedback from star formation may play a key role in energizing the hot, diffuse, X-ray emitting circumgalactic medium (CGM). We observed the diffuse hot gas on the interior of the Orion–Eridanus Superbubble (OES) produced by feedback from the Orion OB association. Using HaloSat, a CubeSat X-ray observatory, we cover the majority of the OES using 11 HaloSat fields, each with a 10° diameter. We find the gas is well described by two thermal plasma components. There are regions of enhanced emission measure (EM) that coincide with the Eridanus X-ray Enhancement and the Orion OB association. Individual field temperatures are statistically consistent with the weighted average of all of the OES fields: a warm temperature kT _w = 0.17 ± 0.02 keV and a hot temperature kT _h = 0.79 ± 0.12 keV. The gas is overpressured in comparison with typical interstellar medium pressures, and the rate of energy injected by Orion OB1 can sufficiently power growth of the superbubble. The gas’s radiative cooling timescale (∼30 Myr) is long in comparison with the rate of hot gas production. The temperatures and EMs of the gas agree with properties of the bulk CGM elsewhere in the Milky Way. If we take the OES as a typical superbubble, these factors together suggest that the hot CGM is energized by star formation activity.

Space-based, photon-counting lidar instruments are effective tools for observing cloud and aerosol layers in the atmosphere. Cloud phases and several different kinds of aerosols are presently identified and typed using sophisticated, fine-tuned classification algorithms that operate using processed lidar data. We present a deep neural network semantic segmentation model that was trained using raw, uncalibrated photon count data and data products from the Cloud/Aerosol Transport System’s (CATS) 1064 nm lidar. Our approach successfully types layers in complex scenes using only raw photon counts, bin altitudes, and ground surface type at 14 to 171 times the spatial resolution of the CATS operational data product. We observe comparable cloud detection and phase determination to the CATS operational algorithm while also exhibiting a 15-point improvement in finding tenuous aerosol layers. Because the model is lightweight, does not rely upon ancillary information, and is optimized to leverage GPU computing, it has the potential to be deployed on-instrument to perform cloud and aerosol typing in real time.

Contained within a galaxy cluster, the Intracluster Medium is comprised of hot, X-ray emitting material between member galaxies. The nearest (∼16.1 Mpc) large galaxy cluster is Virgo. Virgo covers an approximately 12° diameter field, making it challenging to observe in its entirety. The HaloSat satellite utilized a 7° radius field of view to obtain the first full cluster observations since 1994. We then fit the cluster spectra with a plasma emission model of fixed metallicity. Our best fit temperature is kT = 1.39 ± 0.11 keV. We report a cluster luminosity of 5.8 ± 0.7 × 10⁴³ erg s⁻¹ and an emission measure of 5.8 ± 0.6 × 10⁶⁶ cm⁻³.

This study presents a feature-scale cross-validation of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) cloud–aerosol discrimination (CAD) products using 9289 globally collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations acquired between October 2018 and June 2023. The operational ATL09 and a U-Net convolutional neural network (CNN) products are evaluated against CALIPSO on a pixel-by-pixel basis using class-specific metrics. Agreement improves with decreasing along-track comparison-window width, which limits spatial divergence from the ground-track crossing, whereas agreement varies weakly and non-monotonically across the actual 0–10 min inter-satellite time separation. Agreement also depends strongly on the CALIPSO integration size used for layer detection, which affects both feature detectability and effective spatial resolution. Restricting the analysis to ±60° latitude markedly increases aerosol agreement, consistent with reduced influence from high-latitude ambiguities associated with blowing snow, diamond dust, and larger crossing angles, while cloud metrics change more modestly. At a 1-s comparison width and 5-km maximum CALIPSO integration scale, CNN–CALIPSO F1 exceeds ATL09–CALIPSO F1 by 0.027–0.045 globally, with all paired 95% confidence intervals above zero across cloud and aerosol classes under daytime and nighttime conditions; however, some differences within ±60° are not distinguishable from zero. These results show that agreement is governed not only by retrieval algorithms but also by differences in instrument characteristics, effective spatial resolution, and sampling strategy. The proposed collocation framework provides a basis for evaluating current and future machine-learning approaches for spaceborne lidar CAD.