Search for: All records

Robust agricultural yields require consistent flower production throughout fluctuating environmental conditions. Floral primordia are produced in the inflorescence meristem, which contains a pool of continuously dividing stem cells. Daughter cells of these divisions either retain stem cell identity or are pushed to the SAM periphery, where they become competent to develop into floral primordia after receiving the appropriate signal. Thus, flower production is inherently linked to regulation of the stem cell pool. The plant hormone auxin promotes flower development throughout its early phases and has been shown to interact with the molecular pathways regulating stem cell maintenance. Here, we will summarize how auxin signaling contributes to stem cell maintenance and promotes flower development through the early phases of initiation, outgrowth, and floral fate establishment. Recent advances in this area suggest that auxin may serve as a signal that integrates stem cell maintenance and new flower production.

 

Abstract Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm. Significance Statement It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms. 

The hostile learning environment and academic disruptions that result from high school violence underscore the need for prevention education. Technology can facilitate the dissemination of educational content, prevention tools, and resources to students. We describe the three-phase iterative process that engaged high school students, administrators and staff, and parents to develop and refine the school safety mobile application (app), uSafeHSTM. During the three-phase development process focus groups and surveys were administered with students, school administrators and staff, and guardians at 13 high schools. Pilot data was collected from seven New England public and private high schools. Optimizing mobile app technology is a promising method of reaching high school students and delivering student support resources that are customizable by each school and safety tools not currently available for this population. 

Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local-to-global scales. Here we provide a quantitative assessment of the three-dimensional spatial distribution of soil N across the conterminous United States (CONUS) using a digital soil mapping (DSM) approach. We used a random forest-regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm) at 5 km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau and Southeast NEON domains. We also compared our soil N predictions with satellite-derived gross primary production (GPP) and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three-dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.

This dataset includes all covariates used for modeling soil Nitrogen, the training data, and the modeling output. The output represents raster files at 5km resolution of soil N at different depths and associated model uncertainty.

Main reference:

Smith EM, Guevara M, Tarin T, Pouyat R, Vargas R. Spatial variability and uncertainty of soil nitrogen across the conterminous United States (in review). Ecosphere.

 

Abstract. During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions. 

The Center for Cybersecurity Education and Research at Old Dominion University has expanded its use of high impact practices in the university’s undergraduate cybersecurity degree program. Strategies developed to promote student learning included learning communities, undergraduate research, a robust internship program, service learning, and electronic portfolios. This paper reviews the literature on these practices, highlights the way that they were implemented in our cybersecurity program, and discussions of some challenges we encountered with each practice. Recommendations for other cybersecurity programs seeking to expand the use of high impact practices are provided. 

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models. 

During the 2015 Plains Elevated Convection at Night (PECAN) field campaign, several nocturnal low-level jets (NLLJs) were observed with integrated boundary layer profiling systems at multiple sites. This paper gives an overview of selected PECAN NLLJ cases and presents a comparison of high-resolution observations with numerical simulations using the Weather Research and Forecasting (WRF) Model. Analyses suggest that simulated NLLJs typically form earlier than the observed NLLJs. They are stronger than the observed counterparts early in the event, but weaker than the observed NLLJs later in the night. However, sudden variations in the boundary layer winds, height of the NLLJ maximum and core region, and potential temperature fields are well captured by the WRF Model. Simulated three-dimensional fields are used for a more focused analysis of PECAN NLLJ cases. While previous studies often related changes in the thermal structure of the nocturnal boundary layer and sudden mixing events to local features, we hypothesize that NLLJ spatial evolution plays an important role in such events. The NLLJ is shown to have heterogeneous depth, wind speed, and wind direction. This study offers detailed documentation of the heterogeneous NLLJ moving down the slope of the Great Plains overnight. As the NLLJ evolves, westerly advection becomes significant. Buoyancy-related mechanisms are proposed to explain NLLJ heterogeneity and down-slope motion. Spatial and temporal heterogeneity of the NLLJ is suggested as a source of the often observed and simulated updrafts during PECAN cases and as a possible mechanism for nocturnal convection initiation. The spatial and temporal characteristics of the NLLJ are interconnected and should not be treated independently.