A Bayesian approach for statistical–physical bulk parameterization of rain microphysics. Part II: Idealized Markov chain Monte Carlo experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates

A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model-observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of > 200 million realistic DSDs from one-dimensional bin microphysical rain shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the 6th and 9th) perform better than the lower-order moments (0th and 3rd) typically predicted by many bulk microphysics schemes

A Culture of Collaboration: Meeting the Instructional Needs of Adolescent English Language Learners

This article details a study that focused on the supports that enabled an English language learner (ELL) facilitator to contribute to a culture of collaboration between the English as a Second Language (ESL) and Language Arts Departments to more effectively meet the instructional needs of ELLs in one culturally and linguistically diverse high school. Findings emphasize the importance of (1) a supportive leadership context for inclusion of ELLs and the ELL facilitator\u27s work, (2) schoolwide supports for ELLs, and (3) collaboration and influence of the literacy team. The article describes the contributions of the ELL facilitator to the culture of collaboration between the ESL and Language Arts Departments, analyzes the structures and organization of the school context that contributed to this collaborative work to meet the instructional needs of ELLs, and discusses the importance of these findings for both research and practice

What Are We Doing to Middle School English Learners? Findings and Recommendations for Change from a Study of California EL Programs

What Are We Doing to Middle School English Learners: Research ReportEXECUTIVE SUMMARYMiddle school students who are English Learners (ELs) quickly run out of time to develop the academic uses of English and the critical skills that will enable them to succeed in the 21st century. What are schools doing during these crucial years to promote ELs' accelerated access to academic language and grade-level, standards-based instruction? How will these students catch up and be able to compete in high school, in college, and on the job market? This study concludes that middle school programs for English Learners in California are failing students and limiting their futures in profound ways. Conducted by researchers in the Quality Teaching for English Learners program at WestEd, the study was funded by the William and Flora Hewlett Foundation. Interviews with 13 school districts with the highest concentration of English Learners in the state and 64 middle schools in those districts found incoherent EL programs across districts and from school to school within districts. The use of below-grade-level materials was found to be widespread in English Learner programs, remediation rather than acceleration was common, and some schools purposely decelerated students' progress through already below-grade-level materials. On California's five-level assessment of English Learners, the California English Language Development Test (CELDT), most students (56 percent) do not progress a single level in a year's time and some even regress (California Department of Education, 2008). School districts in the study identified inadequate teacher preparation for working with English Learners as the primary challenge to these students' academic success. Yet most districts did not provide professional development that would even begin to address teachers' needs. The study also found that schools did not have mechanisms for addressing challenges that they identified. Schools identified teachers of ELs' and EL students' lack of motivation as primary challenges, yet, only six schools reported a focus on student engagement as a support they offered; none reported having a focus on teacher engagement and motivation. Similarly, lack of parental involvement was identified as a major challenge by school interviewees, but only two schools reported having a focus on involving parents. Case studies were developed from classroom observations and interviews in five middle schools that were selected by triangulation of student data (substantially higher than average EL performance on standardized measures), survey responses, and district nominations. These case studies contextualize the study findings— the major challenges schools still face and the promising practices that were found. Practices in one school especially were notable, a small, autonomous district school organized with a focus on targeted grade-level support for students, concerted outreach to parents, and ongoing collegial professional development for teachers

Optimization of Comminution Circuit Throughput and Product Size Distribution by Simulation and Control

The goal of this project is to improve energy efficiency of industrial crushing and grinding operations (comminution). Mathematical models of the comminution process are being used to study methods for optimizing the product size distribution, so that the amount of excessively fine material produced can be minimized. This will save energy by reducing the amount of material that is ground below the target size, and will also reduce the quantity of materials wasted as slimes that are too fine to be useful. This will be accomplished by: (1) modeling alternative circuit arrangements to determine methods for minimizing overgrinding, and (2) determining whether new technologies, such as high-pressure roll crushing, can be used to alter particle breakage behavior to minimize fines production. In the sixth quarter of this project, work was centered on analyzing the considerable plant data gathered during the first year of the project. Modeling is being carried out of the hydrocyclone portion of the grinding circuit, since this has been identified as the primary source of overgrinding and inefficiency

Sentence Stems That Support Reading Comprehension

Sentence stems are widely used by teachers, but what do we know about developing sentence stems and using them effectively? Sentence stems are intended to facilitate students’ participation in academic conversations and writing and support students to develop the language expected in school, but sometimes the stems do not provide the support intended. The authors explain how to develop supportive sentence stems

Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involving three spatial dimensions, time, and a continuous range of droplet sizes. Utilizing the compact latent representations from Variational Autoencoders (VAEs), we produce novel and intuitive visualizations for the organization of droplet sizes and their evolution over time beyond what is possible with clustering techniques. This greatly improves interpretation and allows us to examine aerosol-cloud interactions by contrasting simulations with different aerosol concentrations. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times.Comment: 4 pages, 3 figures, accepted at NeurIPS 2023 (Machine Learning and the Physical Sciences Workshop

Use of Polarimetric Radar Measurements to Constrain Simulated Convective Cell Evolution: A Pilot Study with Lagrangian Tracking

To probe the potential value of a radar-driven field campaign to constrain simulation of isolated convection subject to a strong aerosol perturbation, convective cells observed by the operational KHGX weather radar in the vicinity of Houston, Texas, are examined individually and statistically. Cells observed in a single case study of onshore flow conditions during July 2013 are first examined and compared with cells in a regional model simulation. Observed and simulated cells are objectively identified and tracked from observed or calculated positive specific differential phase (K(sub DP)) above the melting level, which is related to the presence of supercooled liquid water. Several observed and simulated cells are subjectively selected for further examination. Below the melting level, we compare sequential cross sections of retrieved and simulated raindrop size distribution parameters. Above the melting level, we examine time series of KDP and radar differential reflectivity (Z(sub DR)) statistics from observations and calculated from simulated supercooled rain properties, alongside simulated vertical wind and supercooled rain mixing ratio statistics. Results indicate that the operational weather radar measurements offer multiple constraints on the properties of simulated convective cells, with substantial value added from derived K(sub DP) and retrieved rain properties. The value of collocated three-dimensional lightning mapping array measurements, which are relatively rare in the continental US, supports the choice of Houston as a suitable location for future field studies to improve the simulation and understanding of convective updraft physics. However, rapid evolution of cells between routine volume scans motivates consideration of adaptive scan strategies or radar imaging technologies to amend operational weather radar capabilities. A 3-year climatology of isolated cell tracks, prepared using a more efficient algorithm, yields additional relevant information. Isolated cells are found within the KHGX domain on roughly 40 % of days year-round, with greatest concentration in the northwest quadrant, but roughly 5-fold more cells occur during June through September. During this enhanced occurrence period, the cells initiate following a strong diurnal cycle that peaks in the early afternoon, typically follow a south-to-north flow, and dissipate within 1 h, consistent with the case study examples. Statistics indicate that 150 isolated cells initiate and dissipate within 70 km of the KHGX radar during the enhanced occurrence period annually, and roughly 10 times as many within 200 km, suitable for multi-instrument Lagrangian observation strategies. In addition to ancillary meteorological and aerosol measurements, robust vertical wind speed retrievals would add substantial value to a radar-driven field campaign

Contributions and silence in academic talk: exploring learner experiences of dialogic interaction

The benefits of dialogic interaction which engenders academic talk are greater understanding of concepts and ultimately higher educational standards. However, recent research suggests students, both home and international, face certain challenges in contributing to dialogic interaction in a higher education context. This article reports on a study which explored learner experiences of dialogic interaction and reasons for contributing or remaining silent. Data were gathered from a one-semester postgraduate module at a UK university through interviews, audio recordings of sessions, stimulated recall sessions and course assignments. Results suggest that sociocultural factors such as confidence in language, confidence in knowledge, previous educational experiences, and expectations of roles influenced the learners’ willingness to contribute to the academic talk

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth\u27s atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods