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Abstract. Winter precipitation forecasts of phase and amount are challenging, especially in Northeast United States where mixed precipitation events from various synoptic systems frequently occur. Yet, there are not enough quality observations of winter precipitation, particularly microphysical properties from falling snow or mixed phase precipitation. During the winters of 2021–2022, 2022–2023, and 2023–2024, the NASA Global Precipitation Measurement (GPM) Ground Validation (GV) program conducted a field campaign at the University of Connecticut (UConn). The goal of this campaign was to observe various phases of winter precipitation and winter storm types to validate the GPM satellite precipitation products. Over the three winters at UConn, a total of 40 instruments were deployed across two observing sites that captured 117 precipitation events, including 19 phase transition events as indicated by the PARSIVEL2. These instruments included scanning and vertically pointing radars, along with suites of in-situ sensors. In addition, an unmanned aircraft system has been deployed in 2023–2024. Here, an overview of the different field deployments, instrumentation, and the datasets collected are presented. To showcase the observations, this article features a wide-ranging set of measurements collected from the instrument suite for the 28 February 2023 storm, during which six to eight inches of snow accumulated at the two different observing sites. Also included is a discussion on how these observations can be combined with other datasets to validate ground-based and remote sensing measurements and highlight important atmospheric processes that impact winter precipitation phase and amount. The datasets collected from this GPM GV field campaign are available at https://doi.org/10.5067/GPMGVUCONN/DATA101 (Cerrai et al., 2025). 

Abstract Global solar photospheric magnetic maps play a critical role in solar and heliospheric physics research. Routine magnetograph measurements of the field occur only along the Sun–Earth line, leaving the far side of the Sun unobserved. Surface flux transport (SFT) models attempt to mitigate this by modeling the surface evolution of the field. While such models have long been established in the community (with several releasing public full-Sun maps), none are open source. The Open-source Flux Transport (OFT) model seeks to fill this gap by providing an open and user-extensible SFT model that also builds on the knowledge of previous models with updated numerical and data acquisition/assimilation methods along with additional user-defined features. In this first of a series of papers on OFT, we introduce its computational core: the High-performance Flux Transport (HipFT) code (https://github.com/predsci/hipft). HipFT implements advection, diffusion, and data assimilation in a modular design that supports a variety of flow models and options. It can compute multiple realizations in a single run across model parameters to create ensembles of maps for uncertainty quantification and is high-performance through the use of multi-CPU and multi-GPU parallelism. HipFT is designed to enable users to write extensions easily, enhancing its flexibility and adaptability. We describe HipFT’s model features, validations of its numerical methods, performance of its parallel and GPU-accelerated code implementation, analysis/postprocessing options, and example use cases. 

Wildland-urban interface (WUI) fires consume fuels, such as vegetation and structural materials, leaving behind ash composed primarily of pyrogenic carbon and metal oxides. However, there is currently limited understanding of the role of WUI fire ash from different sources as a source of paramagnetic species such as environmentally persistent free radicals (EPFRs) and transition metals in the environment. Electron paramagnetic resonance (EPR) was used to detect and quantify paramagnetic species, including organic persistent free radicals and transition metal spins, in fifty-three fire ash and soil samples collected following the North Complex Fire and the Sonoma-Lake-Napa Unit (LNU) Lightning Complex Fire, California, 2020. High concentrations of organic EPFRs (e.g., 1.4 × 1014 to 1.9 × 1017 spins g−1) were detected in the studied WUI fire ash along with other paramagnetic species such as iron and manganese oxides, as well as Fe3+ and Mn2+ ions. The mean concentrations of EPFRs in various ash types decreased following the order: vegetation ash (1.1 × 1017 ± 1.1 × 1017 spins g−1) > structural ash (1.6 × 1016 ± 3.7 × 1016 spins g−1) > vehicle ash (6.4 × 1015 ± 8.6 × 1015 spins g−1) > soil (3.2 × 1015 ± 3.7 × 1015 spins g−1). The mean concentrations of EPFRs decreased with increased combustion completeness indicated by ash color; black (1.1 × 1017 ± 1.1 × 1017 spins g−1) > white (2.5 × 1016 ± 4.4 × 1016 spins g−1) > gray (1.8 × 1016 ± 2.4 × 1016 spins g−1). In contrast, the relative amounts of reduced Mn2+ ions increased with increased combustion completeness. Thus, WUI fire ash is an important global source of EPFRs and reduced metal species (e.g., Mn2+). Further research is needed to underpin the formation, transformation, and environmental and human health impacts of these paramagnetic species in light of the projected increased frequency, size, and severity of WUI fires. 

The Sun’s corona is its tenuous outer atmosphere of hot plasma, which is difficult to observe. Most models of the corona extrapolate its magnetic field from that measured on the photosphere (the Sun’s optical surface) over a full 27-day solar rotational period, providing a time-stationary approximation. We present a model of the corona that evolves continuously in time, by assimilating photospheric magnetic field observations as they become available. This approach reproduces dynamical features that do not appear in time-stationary models. We used the model to predict coronal structure during the total solar eclipse of 8 April 2024 near the maximum of the solar activity cycle. There is better agreement between the model predictions and eclipse observations in coronal regions located above recently assimilated photospheric data. 

Abstract A plethora of coronal models, from empirical to more complex magnetohydrodynamic (MHD) ones, are being used for reconstructing the coronal magnetic field topology and estimating the open magnetic flux. However, no individual solution fully agrees with coronal hole observations and in situ measurements of open flux at 1 au, as there is a strong deficit between the model and observations contributing to the known problem of the missing open flux. In this paper, we investigate the possible origin of the discrepancy between modeled and observed magnetic field topology by assessing the effect on the simulation output by the choice of the input boundary conditions and the simulation setup, including the choice of numerical schemes and the parameter initialization. In the frame of this work, we considered four potential field source surface-based models and one fully MHD model, different types of global magnetic field maps, and model initiation parameters. After assessing the model outputs using a variety of metrics, we conclude that they are highly comparable regardless of the differences set at initiation. When comparing all models to coronal hole boundaries extracted by extreme-ultraviolet filtergrams, we find that they do not compare well. This mismatch between observed and modeled regions of the open field is a candidate contributing to the open flux problem. 

Abstract The disease burden from Legionella spp. infections has been increasing in many industrialized countries and, despite decades of scientific advances, ranks amongst the highest for waterborne diseases. We review here several key research areas from a multidisciplinary perspective and list critical research needs to address some of the challenges of Legionella spp. management in engineered environments. These include: (i) a consideration of Legionella species diversity and cooccurrence, beyond Legionella pneumophila only; (ii) an assessment of their environmental prevalence and clinical relevance, and how that may affect legislation, management, and intervention prioritization; (iii) a consideration of Legionella spp. sources, their definition and prioritization; (iv) the factors affecting Legionnaires’ disease seasonality, how they link to sources, Legionella spp. proliferation and ecology, and how these may be affected by climate change; (v) the challenge of saving energy in buildings while controlling Legionella spp. with high water temperatures and chemical disinfection; and (vi) the ecological interactions of Legionella spp. with other microbes, and their potential as a biological control strategy. Ultimately, we call for increased interdisciplinary collaboration between multiple research domains, as well as transdisciplinary engagement and collaboration across government, industry, and science as the way toward controlling and reducing Legionella-derived infections. 

Abstract BackgroundNumerous studies show that active and engaging classrooms help students learn and persist in college, but adoption of new teaching practices has been slow. Professional development programs encourage instructors to implement new teaching methods and change the status quo in STEM undergraduate teaching, and structured observations of classrooms can be used in multiple ways to describe and assess this instruction. We addressed the challenge of measuring instructional change with observational protocols, data that often do not lend themselves easily to statistical comparisons. Challenges using observational data in comparative research designs include lack of descriptive utility for holistic measures and problems related to construct representation, non-normal distributions and Type-I error inflation for segmented measures. ResultsWe grouped 790 mathematics classes from 74 instructors using Latent Profile Analysis (a statistical clustering technique) and found four reliable categories of classes. Based on this grouping we proposed a simple proportional measure we called Proportion Non-Didactic Lecture (PND). The measure aggregated the proportions of interactive to lecture classes for each instructor. We tested the PND and a measure derived from the Reformed Teaching Observation Protocol (RTOP) with data from a professional development study. The PND worked in simple hypothesis tests but lacked some statistical power due to possible ceiling effects. However, the PND provided effective descriptions of changes in instructional approaches from pre to post. In tandem with examining the proportional measure, we also examined the RTOP-Sum, an existing outcome measure used in comparison studies. The measure is based on the aggregated items in a holistic observational protocol. As an aggregate measure we found it to be highly reliable, correlated highly with the PND, and had more statistical power than the PND. However, the RTOP measure did not provide the thick descriptions of teaching afforded by the PND. ConclusionsFindings suggest that useful dependent measures can be derived from both segmented and holistic observational measures. Both have strengths and weaknesses: measures from segmented data are best at describing changes in teaching, while measures derived from the RTOP have more statistical power. Determining the validity of these measures is important for future use of observational data in comparative studies. 

Abstract To address Objective II of the National Space Weather Strategy and Action Plan “Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts” and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang–Sheeley–Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.