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In magneto-inertial fusion, the ratio of the characteristic fuel length perpendicular to the applied magnetic field R to the α-particle Larmor radius ϱα is a critical parameter setting the scale of electron thermal-conduction loss and charged burn-product confinement. Using a previously developed deep-learning-based Bayesian inference tool, we obtain the magnetic-field fuel-radius product BR∝R/ϱα from an ensemble of 16 magnetized liner inertial fusion (MagLIF) experiments. Observations of the trends in BR are consistent with relative trade-offs between compression and flux loss as well as the impact of mix from 1D resistive radiation magneto-hydrodynamics simulations in all but two experiments, for which 3D effects are hypothesized to play a significant role. Finally, we explain the relationship between BR and the generalized Lawson parameter χ. Our results indicate the ability to improve performance in MagLIF through careful tuning of experimental inputs, while also highlighting key risks from mix and 3D effects that must be mitigated in scaling MagLIF to higher currents with a next-generation driver.

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of $N_{\text {scans}} = 139$ SCXI images across $N_{\text {exp}} = 67$ different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. We also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

The performance of the Hurricane Weather Research and Forecasting (HWRF) Model Rapid Intensification Analog Ensemble (RI-AnEn) is evaluated for real-time forecasts made during the National Oceanic and Atmospheric Administration (NOAA)’s 2018 Hurricane Forecast Improvement Program (HFIP) demonstration. Using a variety of assessment tools (Brier skill score, reliability diagrams, ROC curves, ROC skill scores), RI-AnEn is shown to perform competitively compared to both the deterministic HWRF and current operational probabilistic RI forecast aids. The assessment is extended to include forecasts from the 2017 HFIP demonstration and shows that RI-AnEn is the only model with significant RI forecast skill at all lead times in the Atlantic and eastern Pacific basins. Though RI-AnEn is overconfident in its RI forecasts, it is generally well calibrated for all lead times. Furthermore, significance testing indicates that for the 2017–18 Atlantic and eastern Pacific sample, RI-AnEn is more skillful than HWRF at all lead times and better than most of the other probabilistic guidance at 48 and 72 h. ROC curves reveal that RI-AnEn offers a good combination of sensitivity and specificity, performing comparably to SHIPS-RII at all lead times in both basins. With respect to specific high-impact cases from the 2018 Atlantic season, performance of RI-AnEn ranges from excellent (Hurricane Michael) to poor (Hurricane Florence). The multiyear assessment and results for two high-impact case studies from 2018 indicate that, while promising, RI-AnEn requires further work to refine its performance as well as to accurately situate its effectiveness relative to other RI forecasts aids.

A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt ≈ 0.51 m s−1) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index.

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP&rsquo, s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017&rsquo, s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.

Cross-disciplinary self-studies and peer coaching have separately been shown to offer teacher educators meaningful professional development opportunities. However, the teacher educator literature h...

We assessed the influence of genre-specific discourse knowledge and writing goals on the persuasive writing of 4th and 6th grade students with and without learning disabilities (LD). Students were first interviewed about their knowledge of persuasion and persuasive writing. They then wrote a persuasive essay about a controversial topic after receiving either a general goal to persuade or an elaborated goal that focused on the inclusion of elements of persuasive discourse. Finally, the students were asked to generate ideas to assist a hypothetical student who was struggling to write a persuasive essay. Students in the elaborated goal condition produced higher quality persuasive essays than students in the general goal condition. In addition, students with LD wrote less persuasive essays than students without disabilities. Furthermore, knowledge of persuasion predicted the persuasiveness of students’ essays. However, the number and types of ideas students generated did not predict essay persuasiveness after accounting for the effects of other variables. Finally, the provision of an elaborated goal did not impact the number or type of ideas generated by the students. However, the students’ ideas evidenced considerable sensitivity to possible criticisms that could be leveled by an audience. The implications for argumentative writing are discussed.

An analog ensemble (AnEn) technique is applied to the prediction of tropical cyclone (TC) intensity (i.e., maximum 1-min averaged 10-m wind speed). The AnEn is an inexpensive, naturally calibrated ensemble prediction of TC intensity derived from a training dataset of deterministic Hurricane Weather Research and Forecasting (HWRF; 2015 version) Model forecasts. In this implementation of the AnEn, a set of analog forecasts is generated by searching an HWRF archive for forecasts sharing key features with the current HWRF forecast. The forecast training period spans 2011–15. The similarity of a current forecast with past forecasts is estimated using predictors derived from the HWRF reforecasts that capture thermodynamic and kinematic properties of a TC’s environment and its inner core. Additionally, the value of adding a multimodel intensity consensus forecast as an AnEn predictor is examined. Once analogs are identified, the verifying intensity observations corresponding to each analog HWRF forecast are used to produce the AnEn intensity prediction. In this work, the AnEn is developed for both the eastern Pacific and Atlantic Ocean basins. The AnEn’s performance with respect to mean absolute error (MAE) is compared with the raw HWRF output, the official National Hurricane Center (NHC) forecast, and other top-performing NHC models. Also, probabilistic intensity forecasts are compared with a quantile mapping model based on the HWRF’s intensity forecast. In terms of MAE, the AnEn outperforms HWRF in the eastern Pacific at all lead times examined and up to 24-h lead time in the Atlantic. Also, unlike traditional dynamical ensembles, the AnEn produces an excellent spread–skill relationship.

To increase reading volume and help students access challenging texts, the authors propose a four-dimensional framework for text sets. The quad text set framework is designed around a target text: a challenging content area text, such as a canonical literary work, research article, or historical primary source document. The three remaining dimensions include visual texts (e.g., a video, pictures), informational texts to build students’ background knowledge and vocabulary, and an accessible young adult novel or current events article to help students engage with the topic. Working together, these texts can build students’ background knowledge, make the target text accessible to students, and also allow them to synthesize information across sources. The authors suggest that quad text sets are useful in English, science, and social studies classrooms.

In this study, cycled forecast experiments were performed to assess the ability of different cloud microphysics and cumulus parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model to accurately simulate the evolution of the cloud and moisture fields during the entire life cycle of Hurricane Edouard (2014). The forecast accuracy for each model configuration was evaluated through comparison of observed and simulated Geostationary Operational Environmental Satellite-13 (GOES-13) infrared brightness temperatures and satellite-derived tropical cyclone intensity estimates computed using the advanced Dvorak technique (ADT). Overall, the analysis revealed a large moist bias in the mid- and upper troposphere during the entire forecast period that was at least partially due to a moist bias in the initialization datasets but was also affected by the microphysics and cumulus parameterization schemes. Large differences occurred in the azimuthal brightness temperature distributions, with two of the microphysics schemes producing hurricane eyes that were much larger and clearer than observed, especially for later forecast hours. Comparisons to the forecast 10-m wind speeds showed reasonable agreement (correlations between 0.58 and 0.74) between the surface-based intensities and the ADT intensity estimates inferred via cloud patterns in the upper troposphere. It was also found that model configurations that had the smallest differences between the ADT and surface-based intensities had the most accurate track and intensity forecasts. Last, the cloud microphysics schemes had the largest impact on the forecast accuracy.