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Aerosols in Jupiter’s stratosphere form intriguing polar hoods, which have been investigated by ultraviolet cameras on Cassini and the Hubble Space Telescope. Transient, concentrated dark ovals of unknown origin have been noted within both the northern and southern polar hoods. However, a systematic comparative study of their properties, which could elucidate the physical processes active at the poles, has not yet been performed due to infrequent observations. Using 26 global maps of Jupiter taken by Hubble between 1994 and 2022, we detected transient ultraviolet-dark ovals with a 48% to 53% frequency of occurrence in the south. We found the southern dark oval to be 4 to 6 times more common than its northern counterpart. The southern feature is an anticyclonic vortex and remains within the auroral oval during most of its lifetime. The oval’s darkness is consistent with a 20 to 50 times increase in haze abundance or an overall upward shift in the stratospheric haze distribution. The anticyclonic vorticity of the dark oval is enhanced relative to its surroundings, which represents a deep extension of the higher-altitude vortices previously reported in the thermosphere and upper stratosphere. The haze enhancement is probably driven by magnetospheric momentum exchange, with enhanced aerosols producing the localized heating detected in previous infrared retrievals. 

Abstract The global energy budget is pivotal to understanding planetary evolution and climate behaviors. Assessing the energy budget of giant planets, particularly those with large seasonal cycles, however, remains a challenge without long-term observations. Evolution models of Saturn cannot explain its estimated Bond albedo and internal heat flux, mainly because previous estimates were based on limited observations. Here, we analyze the long-term observations recorded by the Cassini spacecraft and find notably higher Bond albedo (0.41 ± 0.02) and internal heat flux (2.84 ± 0.20 Wm⁻²) values than previous estimates. Furthermore, Saturn’s global energy budget is not in a steady state and exhibits significant dynamical imbalances. The global radiant energy deficit at the top of the atmosphere, indicative of the planetary cooling of Saturn, reveals remarkable seasonal fluctuations with a magnitude of 16.0 ± 4.2%. Further analysis of the energy budget of the upper atmosphere including the internal heat suggests seasonal energy imbalances at both global and hemispheric scales, contributing to the development of giant convective storms on Saturn. Similar seasonal variabilities of planetary cooling and energy imbalance exist in other giant planets within and beyond the Solar System, a prospect currently overlooked in existing evolutional and atmospheric models. 

Abstract The purpose of this research is to build an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1–10-day range using the U-Net 3+ machine learning model. This paper illustrates the range of model performance resulting from choices made in the modeling process, such as how labels are defined for the model and how input variables are codified for the model. By combining the capabilities of the U-Net 3+ model with a neighborhood loss function, fractions skill score (FSS), we can quantify model success by predictions made both in and around the location of the original fire occurrence label. The model is trained on weather, weather-derived fuel, and topography observational inputs and labels representing fire occurrence. Observational weather, weather-derived fuel, and topography data are sourced from the gridded surface meteorological (gridMET) dataset, a daily, CONUS-wide, high-spatial-resolution dataset of surface meteorological variables. Fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis Fire-Occurrence Database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS, combining data sourced from the reporting systems of federal, state, and local organizations. By exploring the many aspects of the modeling process with the added context of model performance, this work builds understanding around the use of deep learning to predict fire occurrence in CONUS. Significance StatementOur work seeks to explore the limits to which deep learning can predict wildfire occurrence in CONUS with the ultimate goal of providing decision support to those allocating fire resources during high fire seasons. By exploring with what accuracy and lead time we can provide insights to these persons, we hope to reduce loss of life, reduce damage to property, and improve future event preparedness. We compare two models, one trained on all fires in the continental United States and the other on only large lightning fires. We found that a model trained on all fires produced a higher probability of fire. 

Abstract This paper illustrates the lessons learned as we applied the U-Net3+ deep learning model to the task of building an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1–10-day range. Through the lens of model performance, we explore the reasons for performance improvements made possible by the model. Lessons include the importance of labeling, the impact of information loss in input variables, and the role of operational considerations in the modeling process. This work offers lessons learned for other interdisciplinary researchers working at the intersection of deep learning and fire occurrence prediction with an eye toward operationalization. 

Abstract We present and evaluate a deep learning first-guess front-identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provided by the National Weather Service’s Weather Prediction Center, Ocean Prediction Center, Tropical Analysis and Forecast Branch, and Honolulu Forecast Office are treated as ground-truth labels for training the deep learning models. The models are trained using ERA5 data with variables known to be important for distinguishing frontal boundaries, including temperature, equivalent potential temperature, and wind velocity and direction at multiple heights. Using a 250-km neighborhood over the contiguous U.S. domain, our best models achieve critical success index scores of 0.60 for cold fronts, 0.43 for warm fronts, 0.48 for stationary fronts, 0.45 for occluded fronts, and 0.71 using a binary classification system (front/no front), whereas scores over the full unified surface analysis domain were lower. For cold and warm fronts and binary classification, these scores significantly outperform prior baseline methods that utilize 250-km neighborhoods. These first-guess deep learning algorithms can be used by forecasters to locate frontal boundaries more effectively and expedite the frontal analysis process. Significance StatementFronts are boundaries that affect the weather that people experience daily. Currently, forecasters must identify these boundaries through manual analysis. We have developed an automated machine learning method for detecting cold, warm, stationary, and occluded fronts. Our automated method provides forecasters with an additional tool to expedite the frontal analysis process. 

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is ${}^{136}\mathrm{Xe}$, which would double beta decay into ${}^{136}\mathrm{Ba}$. Detecting the single ${}^{136}\mathrm{Ba}$daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon. Published by the American Physical Society2024