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Abstract This study presents an evaluation of the skill of 12 global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) archive in capturing convective storm parameters over the United States. For the historical reference period 1979–2014, we compare the model-simulated 6-hourly convective available potential energy (CAPE), convective inhibition (CIN), 0–1-km wind shear (S01), and 0–6-km wind shear (S06) to those from two independent reanalysis datasets: ERA5 and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2). To obtain a comprehensive picture, we analyze the parameter distribution, climatological mean, extreme, and thresholded frequency of convective parameters. The analysis reveals significant bias in capturing both magnitude and spatial patterns, which also vary across the seasons. The spatial distribution of means and extremes of the parameters indicates that most models tend to overestimate CAPE, whereas S01 and S06 are underrepresented to varying extents. Additionally, models tend to underestimate extremes in CIN. Comparing the model profiles with rawinsonde profiles indicates that most of the high CAPE models have a warm and moist bias. We also find that the near-surface wind speed is generally underestimated by the models. The intermodel spread is larger for thermodynamic parameters as compared to kinematic parameters. The models generally have a significant positive bias in CAPE over western and eastern regions of the continental United States. More importantly, the bias in the thresholded frequency of all four variables is considerably larger than the bias in the mean, suggesting a nonuniform bias across the distribution. This likely leads to an underrepresentation of favorable severe thunderstorm environments and has the potential to influence dynamical downscaling simulations via initial and boundary conditions. Significance StatementGlobal climate model projections are often used to explore future changes in severe thunderstorm activity. However, climate model outputs often have significant biases, and they can strongly impact the results. In this study, we thoroughly examined biases in convective parameters in 12 models from phase 6 of the Coupled Model Intercomparison Project with respect to two reanalysis datasets. The analysis is performed for North America, covering the period 1979–2014. The study reveals significant biases in convective parameters that differ between models and are tied to the biases in temperature, humidity, and wind profiles. These results provide valuable insight into selecting the right set of models to analyze future changes in severe thunderstorm activity across the North American continent. 

Abstract FrontFinder artificial intelligence (AI) is a novel machine learning algorithm trained to detect cold, warm, stationary, and occluded fronts and drylines. Fronts are associated with many high-impact weather events around the globe. Frontal analysis is still primarily done by human forecasters, often implementing their own rules and criteria for determining front positions. Such techniques result in multiple solutions by different forecasters when given identical sets of data. Numerous studies have attempted to automate frontal analysis through numerical frontal analysis. In recent years, machine learning algorithms have gained more popularity in meteorology due to their ability to learn complex relationships. Our algorithm was able to reproduce three-quarters of forecaster-drawn fronts over CONUS and NOAA’s unified surface analysis domain on independent testing datasets. We applied permutation studies, an explainable artificial intelligence method, to identify the importance of each variable for each front type. The permutation studies showed that the most “important” variables for detecting fronts are consistent with observed processes in the evolution of frontal boundaries. We applied the model to an extratropical cyclone over the central United States to see how the model handles the occlusion process, with results showing that the model can resolve the early stages of occluded fronts wrapping around cyclone centers. While our algorithm is not intended to replace human forecasters, the model can streamline operational workflows by providing efficient frontal boundary identification guidance. FrontFinder has been deployed operationally at NOAA’s Weather Prediction Center. Significance StatementFrontal boundaries drive many high-impact weather events worldwide. Identification and classification of frontal boundaries is necessary to anticipate changing weather conditions; however, frontal analysis is still mainly performed by human forecasters, leaving room for subjective interpretations during the frontal analysis process. We have introduced a novel machine learning method that identifies cold, warm, stationary, and occluded fronts and drylines without the need for high-end computational resources. This algorithm can be used as a tool to expedite the frontal analysis process by ingesting real-time data in operational environments. 

Software developers have difficulty understanding the rationale and intent behind original developers' design decisions. Code histories aim to provide richer contexts for code changes over time, but can introduce a large amount of information to the already cognitively demanding task of code comprehension. Storytelling has shown benefits in communicating complex, time-dependent information, yet programmers are reluctant to write stories for their code changes. We explored the utility of narratives made by generative AI. We conducted a within-subjects study comparing the performance of 16 programmers when recalling code history information from a list-view format versus a comparable AI-generated narrative format. Our study found that when using the story-view, participants were 16\% more successful at recalling code history information, and had 30\% less error when assessing the correctness of their responses. We did not find any significant differences in programmer's perceived mental effort or their attitudes towards reuse when using narrative code stories. 

Background: Software development relies on collaborative problem-solving. Understanding previously addressed problems in software is crucial for developers to identify and repurpose functionalities for new problem-solving contexts. Objective: We explore the barriers programmers encounter during code repurposing and investigate how access to historical context about the original developer's goals may affect this process. Method: We present an exploratory study of 16 programmers who completed two code repurposing tasks in different code bases. Participants completed these tasks both with and without access to the historical information of the original developer's goals. We explore how programmers use analogical reasoning to identify and apply existing software artifacts to new goals. Results: We show that programmers often failed to notice analogies, made false analogies, and underestimated the value of reuse. Even when useful analogies were made, programmers struggled to find the relevant code. We also describe the patterns of how participants utilized code histories. Conclusion: We highlight the barriers programmers face in noticing and applying analogies during code reuse. We suggest design recommendations for future tools to allow lightweight evaluation of code to help programmers identify reuse opportunities. 

Abstract Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that uses a 3D U-Net to produce gridded severe hail nowcasts for up to 40 min in advance. The three U-Net dimensions uniquely incorporate one temporal and two spatial dimensions. Our predictors consist of a combination of output from the National Severe Storms Laboratory Warn-on-Forecast System (WoFS) numerical weather prediction ensemble and remote sensing observations from Vaisala’s National Lightning Detection Network (NLDN). Ground truth for prediction was derived from the maximum expected size of hail calculated from the gridded NEXRAD WSR-88D radar (GridRad) dataset. Our U-Net was evaluated by comparing its test set performance against rigorous hail nowcasting baselines. These baselines included WoFS ensemble Hail and Cloud Growth Model (HAILCAST) and a logistic regression model trained on WoFS 2–5-km updraft helicity. The 3D U-Net outperformed both these baselines for all forecast period time steps. Its predictions yielded a neighborhood maximum critical success index (max CSI) of ∼0.48 and ∼0.30 at forecast minutes 20 and 40, respectively. These max CSIs exceeded the ensemble HAILCAST max CSIs by as much as ∼0.35. The NLDN observations were found to increase the U-Net performance by more than a factor of 4 at some time steps. This system has shown success when nowcasting hail during complex severe weather events, and if used in an operational environment, may prove valuable. 

Programmers typically learn APIs on-the-fly through web examples. Incompatibilities and omissions in copied example code can create barriers for these learners. We present an analysis of example usage barriers programmers faced in a previous study of React.js novices. We show that a small set of errors prevented programmers from using most found code examples. In response, we built REVEAL to detect and repair the common errors we identified in copied code. We describe the formative evaluation of REVEAL and show that REVEAL users were more likely to successfully integrate code examples than participants in the previous study. 

Subtropical oceans contribute significantly to global primary production, but the fate of the picophytoplankton that dominate in these low-nutrient regions is poorly understood. Working in the subtropical Mediterranean, we demonstrate that subduction of water at ocean fronts generates 3D intrusions with uncharacteristically high carbon, chlorophyll, and oxygen that extend below the sunlit photic zone into the dark ocean. These contain fresh picophytoplankton assemblages that resemble the photic-zone regions where the water originated. Intrusions propagate depth-dependent seasonal variations in microbial assemblages into the ocean interior. Strikingly, the intrusions included dominant biomass contributions from nonphotosynthetic bacteria and enrichment of enigmatic heterotrophic bacterial lineages. Thus, the intrusions not only deliver material that differs in composition and nutritional character from sinking detrital particles, but also drive shifts in bacterial community composition, organic matter processing, and interactions between surface and deep communities. Modeling efforts paired with global observations demonstrate that subduction can flux similar magnitudes of particulate organic carbon as sinking export, but is not accounted for in current export estimates and carbon cycle models. Intrusions formed by subduction are a particularly important mechanism for enhancing connectivity between surface and upper mesopelagic ecosystems in stratified subtropical ocean environments that are expanding due to the warming climate. 

Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping”, with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8,958 tornadoes and 7,256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity. 

Abstract Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself.