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Fourth-and sixth-grade students with and without learning disabilities wrote essays about a controversial topic after receiving either a general persuasion goal or an elaborated goal that included subgoals based on elements of argumentative discourse. Students in the elaborated goal condition produced more persuasive essays that were responsive to alternative standpoints than students in the general goal condition. Students with learning disabilities wrote poorer quality and less elaborated arguments than students without disabilities. Measures derived from the structure of students' argumentative strategies were highly predictive of essay quality, and they accounted for the effects of goal condition, grade, and disability status. Nearly all students used the argument from consequences strategy to defend their standpoint. The implications for argumentative writing are discussed.

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

The quad text set framework can assist content teachers in building students' background knowledge, increasing their reading volume, and incorporating complex texts into instruction.A call for continued efforts to improve literacy outcomes for adolescents is standard fare, but exactly what adolescents should read, how much, and how are less clear. As former middle and high school teachers and current university-based literacy researchers, we take the stance that increasing the amount of challenging texts that middle and high school students read has the potential to improve literacy outcomes. However, we know that teachers are often unsure about how to link texts to other curricular objectives. We present a text set framework that allows teachers to plan instruction that meets disciplinary goals while also providing opportunities for students to build their background knowledge through reading. Starting With What We KnowWe started by considering the literature behind the use of text sets. We then thought through factors that impact both comprehension and instruction, including the effects of reading volume and difficulty, and how the use of text sets may help or hinder these challenges. We also considered the actual knowledge and motivational demands on adolescent readers tasked with learning content through high-volume work with texts. Figure 1 presents a visual depiction of the stressors that we saw that influence adolescent reading in school, potentially affecting both attitudes and achievement. We describe these factors to provide background for our decisions. Together, these research strands help teachers consider both students' knowledge and their thinking processes during reading, keeping teachers' attention squarely on what students need to know and do to learn from text.Finally, we developed an approach to text selection and sequencing that puts theory into classroom practice. We worked with teachers to develop texts sets and observed the implementation of our new framework in middle and high school content area classrooms.

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.