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Abstract Agriculture is the dominant source of anthropogenic nitrous oxide (N₂O) –a greenhouse gas and a stratospheric ozone depleting substance. The US Corn Belt is a large global N₂O source, but there remain large uncertainties regarding its source attribution and biogeochemical pathways. Here, we interpret high frequency stable N₂O isotope observations from a very tall tower to improve our understanding of regional source attribution. We detected significant seasonal variability in δ¹⁵N_bulk(6.47–7.33‰) and the isotope site preference (δ¹⁵N^SP = δ¹⁵N^α–δ¹⁵N^β, 18.22–25.19‰) indicating a predominance of denitrification during the growing period but of nitrification during the snowmelt period. Isotope mixing models and atmospheric inversions both indicate that indirect emissions contribute substantially (>35%) to total N₂O emissions. Despite the relatively large uncertainties, the upper bound of bottom‐up indirect emission estimates are at the lower bound of the isotopic constraint, implying significant discrepancies that require further investigation. 

In this paper, we make a case for the importance of teaching secondary school level algebra to students with learning disabilities (LD). Furthermore, we illustrate how they struggle and present best-practices on how they are best supported. We demonstrate effective ways of how teachers can show students with LD how to solve challenging algebra problems. In particular, we depict how educators can help learners with LD show their work on paper in ways that support their thinking processes as they engage with challenging algebra problems. 

Monomeric boroles have been gaining attention as reagents for the synthesis of heterocycles due to their ability to insert atoms into the BC 4 ring in a single step. Although unique boron frameworks can be accessed via this methodology, the products feature aryl substitution on the carbon centers as steric bulk is required to preclude borole dimerization. This work demonstrates that insertion chemistry is possible with Diels–Alder dimeric boroles and that such reactivity is not exclusive to monomeric boroles with bulky groups. With 1-phenyl-2,3,4,5-tetramethylborole dimer, the formal 1,1-insertion of a nitrene and sulfur generate the six-membered aromatic 1,2-azaborine and 1,2-thiaborine, respectively. The isolation of the 1,2-thiaborine enabled the synthesis of an η 6 -chromium complex. Benzophenone and diphenylketene readily insert a CO unit to generate BOC 5 seven-membered rings confirming dimeric boroles can serve as monomeric synthons in 1,2-insertion reactions. An epoxide did not furnish the anticipated eight-membered BOC 6 ring, instead provided a bicyclic system with a BOC 3 ring. The insertion chemistry was demonstrated with two other borole dimers featuring different substitution with diphenylketene as a substrate. This work elevates borole insertion chemistry to a new level to access products that do not require bulky substitution. 

Abstract The Super Dual Auroral Radar Network (SuperDARN) is a network of High Frequency (HF) radars that are typically used for monitoring plasma convection in the Earth's ionosphere. A majority of SuperDARN backscatter can broadly be divided into three categories: (a) ionospheric scatter due to reflections from plasma irregularities in the E and F regions of the ionosphere, (b) ground scatter caused by reflections from the ground/sea surface following reflection in the ionosphere, and (c) backscatter from meteor trails left by meteoroids as they enter the Earth's atmosphere. Due to the complex nature of HF propagation and mid‐latitude electrodynamics, it is often not straightforward to distinguish between different modes of backscatter observed by SuperDARN. In this study, we present a new two‐stage machine learning algorithm for identifying different backscatter modes in SuperDARN data. In the first stage, a neural network that “mimics” ray‐tracing is used to predict the probability of ionospheric and ground scatter occurring at a given location along with parameters like the elevation angles, reflection heights etc. The inputs to the network include parameters that control HF propagation, such as signal frequency, season, UT time, and geomagnetic activity levels. In the second stage, the output probabilities from the neural network and actual SuperDARN data are clustered together to determine the category of the backscatter. Our model can distinguish between meteor scatter, 1/2 hop E‐/F‐region ionospheric as well as ground/sea scatter. We validate our model by comparing predicted elevation angles with those measured at a SuperDARN radar. 

Abstract Sudden enhancement in high‐frequency absorption is a well‐known impact of solar flare‐driven Short‐Wave Fadeout (SWF). Less understood, is a perturbation of the radio wave frequency as it traverses the ionosphere in the early stages of SWF, also known as the Doppler flash. Investigations have suggested two possible sources that might contribute to it’s manifestation: first, enhancements of plasma density in the D‐and lower E‐regions; second, the lowering of the F‐region reflection point. Our recent work investigated a solar flare event using first principles modeling and Super Dual Auroral Radar Network (SuperDARN) HF radar observations and found that change in the F‐region refractive index is the primary driver of the Doppler flash. This study analyzes multiple solar flare events observed across different SuperDARN HF radars to determine how flare characteristics, properties of the traveling radio wave, and geophysical conditions impact the Doppler flash. In addition, we use incoherent scatter radar data and first‐principles modeling to investigate physical mechanisms that drive the lowering of the F‐region reflection points. We found, (a) on average, the change in E‐ and F‐region refractive index is the primary driver of the Doppler flash, (b) solar zenith angle, ray’s elevation angle, operating frequency, and location of the solar flare on the solar disk can alter the ionospheric regions of maximum contribution to the Doppler flash, (c) increased ionospheric Hall and Pedersen conductance causes a reduction of the daytime eastward electric field, and consequently reduces the vertical ion‐drift in the lower and middle latitude ionosphere, which results in lowering of the F‐region ray reflection point. 

Abstract Over‐the‐Horizon communication is strongly dependent on the state of the ionosphere, which is susceptible to solar flares. Trans‐ionospheric high frequency (HF, 3–30 MHz) signals can experience strong attenuation following a solar flare that lasts typically for an hour, commonly referred to as shortwave fadeout (SWF). In this study, we examine the role of dispersion relation and collision frequency formulations on the estimation of SWF in riometer observations using a new physics‐based model framework. The new framework first uses modified solar irradiance models incorporating high‐resolution solar flux data from the GOES satellite X‐ray sensors as input to compute the enhanced ionization produced during a flare event. The framework then uses different dispersion relation and collision frequency formulations to estimate the enhanced HF absorption. The modeled HF absorption is compared with riometer data to determine which formulation best reproduces the observations. We find the Appleton‐Hartree dispersion relation in combination with the averaged collision frequency profile reproduces riometer observations with an average skill score of 0.4, representing 40% better forecast ability than the existing D‐region Absorption Prediction model. Our modeling results also indicate that electron temperature plays an important role in controlling HF absorption. We suggest that adoption of the Appleton‐Hartree dispersion relation in combination with the averaged collision frequency be considered for improved forecasting of ionospheric absorption following solar flares. 

Abstract Trans‐ionospheric high frequency (HF: 3–30 MHz) signals experience strong attenuation following a solar flare‐driven sudden ionospheric disturbance (SID). Solar flare‐driven HF absorption, referred to as short‐wave fadeout, is a well‐known impact of SIDs, but the initial Doppler frequency shift phenomena, also known as “Doppler flash” in the traveling radio wave is not well understood. This study seeks to advance our understanding of the initial impacts of solar flare‐driven SID using a physics‐based whole atmosphere model for a specific solar flare event. First, we demonstrate that the Doppler flash phenomenon observed by Super Dual Auroral Radar Network (SuperDARN) radars can be successfully reproduced using first‐principles based modeling. The output from the simulation is validated against SuperDARN line‐of‐sight Doppler velocity measurements. We then examine which region of the ionosphere, D, E, or F, makes the largest contribution to the Doppler flash. We also consider the relative contribution of change in refractive index through the ionospheric layers versus lowered reflection height. We find: (a) the model is able to reproduce radar observations with an root‐median‐squared‐error and a mean percentage error (δ) of 3.72 m/s and 0.67%, respectively; (b) the F‐region is the most significant contributor to the total Doppler flash (∼48%), 30% of which is contributed by the change in F‐region's refractive index, while the other ∼18% is due to change in ray reflection height. Our analysis shows lowering of the F‐region's ray reflection point is a secondary driver compared to the change in refractive index.