Adnet Systems (United States)

The X-IFU instrument for ESA's upcoming flagship mission Athena will use Mo/Au bilayer transition-edge sensors (TES) with Au/Bi X-ray absorbers to form a kilo-pixel microcalorimeter array. Such detectors require careful calibration to maintain their best possible performance, but such calibrations can change over long periods of time when exposed to certain environmental conditions. We have previously reported the effects of humidity and elevated temperature on prototype array characteristics. In that study, storage of a prototype array at 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C for 10 days under vacuum produced an unexpected shift in the TES critical temperature T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{c}$</tex-math></inline-formula> (+13 to +19%) and normal resistance R<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{n}$</tex-math></inline-formula> (+4%). In this work, we continue investigating shifts in array characteristics after long-duration temperature exposures so that safe storage and handling conditions for X-IFU flight arrays can be set. A prototype array exposed to 35 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C under vacuum for 26 days gradually increased in TES normal resistance. The energy scale of a second prototype array was measured before aging and then recovered to within 0.5 eV following a similar aging induced R<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{n}$</tex-math></inline-formula> increase. Aging of the TES was studied directly by applying the same thermal conditions exclusively to Mo/Au bilayers on Si substrate. These measurements allow a better understanding of the cause of the observed shift by decoupling the TES bilayer from the remaining device structures such as the SiNx membrane and the absorber. Four-point resistance measurements were used to readout the bilayer samples at 4.2 K. Bilayers aged in a vacuum generally saw no change or a slight increase in resistance, while bilayers aged in nitrogen gas decreased in resistance. Finally, we conclude with a comparative overview of all test results on prototype arrays and TES bilayers, elaborate on possible mechanisms for the observed shifts in characteristics, and make recommendations for maintaining the long-term stability of our devices.

Abstract The General Coordinates Network (GCN) is NASA’s time-domain and multimessenger alert system. GCN distributes two data products: automated “Notices” and human-generated “Circulars” that report the observations of high-energy and multimessenger astronomical transients. The flexible and nonstructured format of GCN Circulars, comprising more than 40,500 Circulars accumulated over three decades, makes it challenging to manually extract observational information, such as redshift or observed wave bands. In this work, we employ large language models (LLMs) to facilitate the automated parsing of transient reports. We develop a neural topic modeling pipeline with open-source tools for the automatic clustering and summarization of astrophysical topics in the Circulars archive. Using neural topic modeling and contrastive fine-tuning, we classify Circulars based on their observation wave bands and messengers. Additionally, we separate gravitational-wave event clusters and their electromagnetic counterparts from the Circulars archive. Finally, using the open-source Mistral model, we implement a system to automatically extract gamma-ray burst (GRB) redshift information from the Circulars archive, without the need for any training. Evaluation against the manually curated Neil Gehrels Swift Observatory GRB table shows that our simple system, with the help of prompt-tuning, output parsing, and retrieval augmented generation (RAG), can achieve an accuracy of 97.2% for redshift-containing Circulars. Our neural search-enhanced RAG pipeline accurately retrieved 96.8% of redshift Circulars from the manually curated archive. Our study demonstrates the potential of LLMs to automate and enhance astronomical text mining and provides a foundational work for future advances in transient alert analysis.

Abstract. A variety of chemical and dynamical processes in the troposphere and stratosphere affect tropical total column ozone (TCO), the net effect of which may cause changes in surface UV radiation and impact human and ecosystem health. We use dynamical linear modeling to estimate tropical trends in TCO and partial column ozone (PCO) in the troposphere and three stratospheric layers to assess agreement between satellite observational composites and chemistry–climate model (CCM) simulations from two multi-model experiments (CCMI-1 and CCMI-2022). While both model experiments show tropical TCO increases over 2000–2021, CCMI–2022 trends (+2.5 DU) agree slightly better with observations than CCMI-1 (+1.6 DU). However, this overall agreement obscures multiple systematic differences in PCO trends between the models and observations across atmospheric layers. For example, since 2000 tropical tropospheric PCO increased significantly in CCMI-2022 (+1.5 DU) but not in CCMI–1 (+0.3 DU), largely explaining the difference in TCO trends. Also, despite nearly identical stratospheric PCO trends, CCMI-2022 trends are slightly more negative in the lower stratospheric (by ~0.5 DU), compensated by more positive middle/upper stratosphere trends compared to CCMI-1. Crucially, substantial differences exist across observational PCO trends, particularly in the troposphere and middle/upper stratosphere, and these disagreements limit the ability to evaluate CCM fidelity. Furthermore, while the inter-model correlation between late and early 21st century trends is suggestive of a potential emergent constraint on future ozone trends, the spread in observational trends precludes its observational implementation.

Abstract. The boreal forest has experienced the fastest warming of any forested biome in recent decades. While vegetation–climate models predict a northward migration of boreal tree cover, the long-term studies required to test the hypothesis have been confined to regional analyses, general indices of vegetation productivity, and data calibrated to other ecoregions. Here we report a comprehensive test of the magnitude, direction, and significance of changes in the distribution of the boreal forest based on the longest and highest-resolution time-series of calibrated satellite maps of tree cover to date. From 1985 to 2020, boreal tree cover expanded by 0.844 million km2, a 12 % relative increase since 1985, and shifted northward by 0.29° mean and 0.43° median latitude. Gains were concentrated between 64–68° N and exceeded losses at southern margins, despite stable disturbance rates across most latitudes. Forest age distributions reveal that young stands (up to 36 years) now comprise 15.4 % of forest area and hold 1.1–5.9 Pg of aboveground biomass carbon, with the potential to sequester an additional 2.3–3.8 Pg C if allowed to mature. These findings confirm the northward advance of the boreal forest and implicate the future importance of the region's greening to the global carbon budget.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosols in the upper troposphere play an important role in Earth&rsquo;s radiative balance and atmospheric composition. Satellite observations have revealed a recurrent enhancement of aerosol extinction coefficient (AEC) in the upper troposphere and near the tropopause over the Asian summer monsoon (ASM) anticyclone (ASMA) region during July&ndash;August. However, substantial uncertainties remain regarding (i) the influence of ASM dynamics and climate variability on these aerosols, (ii) the extent to which the upper-tropospheric aerosol trends reflect changes in surface pollutant emissions, and (iii) the ability of global models to simulate aerosol amounts, variability, and key controlling processes in the upper-tropospheric ASMA region. Here, we present results from an AeroCom-coordinated global multi-model study addressing these issues. Using simulations from nine models for 2000&ndash;2018, we find large inter-model differences in non-volcanic AEC over the upper-tropospheric ASMA region, with coefficients of variation ranging from 64 % to 86 %. Approximately half of this spread is attributable to differences in transport and wet removal processes, as diagnosed using common tracers, with discrepancies in wet removal contributing about eight times more than those associated with transport. The multi-model ensemble indicates an overall increase in non-volcanic AEC over the past two decades, consistent with rising anthropogenic emissions in Asia, while interannual variability is linked to climate variability as represented by the Multivariate ENSO Index. Through comparison with satellite observations, we further identify persistent model deficiencies, particularly in the representation of volcanic aerosols, and highlight priorities for future coordinated model developments and evaluation.

The changes in global mean precipitation over recent decades are poorly understood despite their fundamental importance to climate change prediction. We provide an observational verification of the physical link between global precipitation and net atmospheric radiative cooling. April 2006 through December 2024, the net atmospheric total (shortwave + longwave) cooling increases at a rate of 0.11 ± 0.31 watts per square meter per decade (Wm−2 dec−1). Trends of longwave cooling and shortwave heating are 0.76 ± 0.48 and 0.64 ± 0.27 Wm−2 dec-1, respectively. The longwave trend is a result of partial cancelation among contributions from increasing surface skin temperature, air temperature, and water vapor. Increasing shortwave absorption is caused by increasing water vapor. The trend of global mean diabatic heating by precipitation is 0.03 ± 0.61 Wm−2 dec−1. These results observationally confirm that an absence of current global precipitation trends is consistent with net atmospheric radiative cooling trends explained by nearly balanced changes between longwave cooling and shortwave heating.

Abstract Using advanced processing techniques, we analyze high-cadence, high-resolution extreme-ultraviolet images and show that, throughout the solar cycle, midlatitude coronal holes (CHs) are made up of ubiquitous and space-filling funnel-shaped structures (or cells) anchored to unipolar magnetic flux concentrations in network lanes. We demonstrate that the coronal cells, previously documented in the magnetically closed regions, and coronal plumes, inside CHs, are a particular manifestation of ubiquitous cells. The cell properties depend on the magnetic field intensity at their footpoint and connectivity in the corona, either closing in opposite-polarity regions (CF-cells) or extending to form open field (OF-cells). The OF-cells reach size scales on the order of supergranules and are characterized by dark lanes delimiting ray-like features both showing, at different levels, persistent jetlike ejections. The cells’ lifetime mirrors that of magnetic flux concentrations revealed by magnetograms, slowly emerging and then disappearing in a matter of a few hours to a few days in a one-to-one correspondence. When a cell forms along the CH boundary, it can alter the CH boundary by shrinking or expanding the CH by an approximately supergranular cell unit. Therefore, coronal cells can contribute to the dynamics of the CH boundary. We contextualize these observations in a “coronal cell” theory potentially able to provide an explanation for fine-scale coronal structures and jetting activity in polar CHs.

This paper proposes a power grid load forecasting method based on a large-scale language model (such as GPT), aiming to address the shortcomings of traditional forecasting methods when facing complex power load data. By designing and optimizing the GPT model based on the Transformer architecture, the study demonstrates its effectiveness and advantages in power grid load forecasting. Compared with traditional models (such as ARIMA, XGBoost, and LSTM), experimental results show that GPT significantly outperforms other baseline models in load forecasting accuracy, especially in reducing prediction errors and improving model generalization capabilities. In addition, GPT can make full use of the time series characteristics in historical data, capture the long-term dependence of power grid load, and adapt to load fluctuations under different times and conditions. By evaluating various performance indicators, such as mean absolute error (MAE) and root mean square error (RMSE), experiments show that this method has high accuracy and computational efficiency in power grid load forecasting tasks, and is particularly suitable for power system dispatching and management that requires real-time prediction and decision support.

Abstract The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) product combines CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites to create a record of earth radiation budget (ERB) and the associated cloud properties. As the Terra and Aqua orbits are no longer maintained at a fixed mean local time, EBAF recently transitioned to the CERES and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on NOAA-20 to avoid introducing a time-dependent bias in the record. To ensure smooth transitions between the Terra, combined Terra and Aqua (Terra+Aqua), and NOAA-20 portions of the record, regional climatological adjustments derived from the overlap period between missions are applied to anchor the entire record to Terra+Aqua. We estimate the random error in global monthly anomalies following the transitions to be &lt;0.15 W m−2 for top-of-atmosphere (TOA) flux and &lt;0.1% for cloud fraction, much smaller than the standard deviation in the corresponding anomalies. As the number of ERB instruments will decrease from four to one in just 10 years, there is a high probability that a data gap in the EBAF record will occur, making it challenging to maintain continuity. We estimate that there is a 33% probability of a data gap in 2028 and a 60% probability in 2035. Bridging a data gap using computed TOA fluxes from one satellite product and one atmospheric reanalysis results in errors that are a factor of 4 larger than those obtained when there is overlap between successive missions.

The Global Space-based Stratospheric Aerosol Climatology (GloSSAC) is essential for understanding and modeling the climatic impacts of stratospheric aerosols. It primarily relies on data from the Stratospheric Aerosol Gas Experiment (SAGE) satellite series, supplemented by the Optical Spectrograph and Infrared Imaging System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). GloSSAC currently provides stratospheric aerosol extinction coefficients and aerosol optical depths at 525 and 1020 nm. With CALIPSO decommissioned and OSIRIS nearing the end of its operational life, SAGE III/ISS will soon become the sole data source for GloSSAC, but it will only be available as long as the International Space Station (ISS) is operational, around 2030. Therefore, incorporating other measurements, such as those from the Ozone Mapping and Profiler Suite limb profiler (OMPS-LP), is critical. OMPS-LP has provided continuous aerosol extinction coefficient measurements since 2012 with a retrieval algorithm developed by NASA (OMPS(NASA)). However, OMPS(NASA) has been shown to overestimate aerosol extinction coefficients, particularly after the 2022 Hunga Tonga eruption, compared to the tomographic retrieval of OMPS developed by the University of Saskatchewan (OMPS(SASK)) and SAGE III/ISS. Our analysis shows that OMPS(NASA) indeed exhibits a consistently high bias (>50%) following large volcanic eruptions and pyrocumulonimbus plumes from intense wildfires, while OMPS(SASK) shows reasonable agreement with SAGE III/ISS between 40 0 S and 40 0 N. This overestimation by OMPS(NASA) leads to an overestimation of the aerosol effective radiative forcing (ERF) and the associated model-simulated global surface temperature response by about a factor of two.