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Electron density irregularities in the ionosphere can give rise to scintillations, affecting radio wave phase and amplitude. While scintillations in the cusp and polar cap regions are commonly associated with mesoscale density inhomogeneities and/or shearing, the auroral regions exhibit a strong correlation between scintillation and density structures generated by electron precipitation (arcs). We aim to examine the impact of electron precipitation on the formation of scintillation‐producing density structures using a high‐resolution physics‐based plasma model, the "Geospace Environment Model of Ion‐Neutral Interactions," coupled with a radio propagation model, the "Satellite‐beacon Ionospheric‐scintillation Global Model of the upper Atmosphere." Specifically, we explore the effects of varying spatial and temporal characteristics of the precipitation, including electron total energy flux and their characteristic energies, obtained from the all‐sky‐imagers and Poker Flat Incoherent Scatter Radar observations, on auroral scintillation. To capture small‐scale structures, we incorporate a power‐law turbulence spectrum that induces short wavelength features sensitive to scintillation. Finally, we compare our simulated scintillation results with satellite‐observed scintillations, along with spectral comparisons. Key Points: A physics‐based plasma model and a radio propagation model are used to examine the effects of precipitation on auroral scintillationPrecipitation fluxes with higher energy, a turbulent noise spectrum, and a faster‐moving arc intensify scintillationThe presence of small‐scale precipitation amplifies both phase and amplitude scintillations [ABSTRACT FROM AUTHOR]

In this work we quantify the representation error of the algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), which estimates global neutral winds and ion drifts given time‐varying plasma densities. SAMI3 (Sami3 is A Model of the Ionosphere) serves as the background climate model and pseudo‐measurements for the EMPIRE observation system. This configuration allows the data assimilation inputs to be self‐consistent between each other and with the validation data. The estimated neutral winds and ion drifts are compared to the Horizontal Wind Model (HWM14) and SAMI3 "truth." For both the quiet period on 25 August 2018 and subs7equent storm on 26 August, the EMPIRE estimation of ion drifts is better at low‐to‐mid geomagnetic latitudes with mean error up to 20 m/s. For the high latitudes (poleward of ±60° magnetic), the mean errors exceed 50 m/s with variances up to 200 m/s, and the relative errors are higher than the "truth." At latitudes of ±87°, the large errors are attributed to a boundary effect. However, the neutral wind mean errors peak at 20 m/s at mid‐latitudes (40°–60° magnetic), with larger uncertainties, then converge to 0 approaching higher latitudes. By conducting this study, we define a method for obtaining the representation error covariance for future use of EMPIRE with SAMI3 as background. Plain Language Summary: Data assimilation in the field of space weather is a technique that reduces the gap between background model and instrument measurements to estimate the current or future conditions. Data assimilation needs an estimate of the possible errors, to optimize the calculation of atmospheric variables. The model error and measurement errors can be provided by the model data source and instrument. However, another error source, called the representation error, plays an important role. The representation error is due to simplifying reality to a mathematical model that can be run by a computer. In this study, we compute the representation error for a specific data assimilation algorithm (EMPIRE). To achieve this, we use a model‐simulated reality as the truth. We import information from background models to the data assimilation algorithm, then compare the algorithm outputs to the simulated truth of neutral winds and ion drifts. We conclude that the error for ion drifts from the data assimilation is small at the low‐to‐mid latitudes, but much larger at high latitudes. For neutral wind, the error is larger at mid‐latitudes. Key Points: We analyze the representation error in a data assimilation algorithm for a quiet and a storm day in August 2018 with synthetic dataBy validating the ion drift outputs with the self‐consistent input source, EMPIRE yields smaller errors at low‐to‐mid latitudes up to ±60°EMPIRE neutral winds have lower errors at low and high latitudes, and at southern mid‐latitudes differ from the quiet to storm day [ABSTRACT FROM AUTHOR]

We present a binary hypothesis test for detecting clear sky in auroral all‐sky images based on single‐wavelength keograms. The coefficient of variation c, the ratio of the sample standard deviation to the mean over elevation angle along the meridian, is the test statistic. After image‐correcting keograms and excluding dark sky intervals, detection performance is compared to true conditions as determined by Advanced Very High Resolution Radiometer satellite imagery. The cloud mask, an index of cloud cover, is selected at the corresponding nearest time and location to the site of a meridian spectrograph at Poker Flat Research Range. With training data from 2014 to 2016, theoretical Rayleigh distributions fit to the histograms show a decision threshold of 0.40 could yield an accuracy of about 80%. Separately, we numerically compute the false alarm and missed detection statistics of the greenline 557.7 nm emission and of the redline 630.0 nm emission. We find a threshold of 0.25 for the greenline c maximizes the percent of events correctly identified at 76%. Applied to testing data from 2015 to 2017, the 0.25 threshold yields an accuracy of 68%. Diffuse aurora can have coefficient of variation around 0.2 to 0.5, which would be included by the numerical minimum, but partly excluded by the theoretical model obtained. Numerical results are a few percent worse for the redline emission. Plain Language Summary: Clouds in the sky are a problem for scientists trying to view space beyond. For upper atmospheric scientists, clouds can obscure or scatter auroral light in scientific auroral cameras, making it hard to identify, locate, and track auroral shapes. This paper shows a way to simply and automatically detect clouds using a north‐to‐south line scan of a single color of light from the sky over time, known as a keogram. We compute the ratio of the variation in pixel intensity to the average pixel intensity, for each north‐to‐south scan. Excluding dark sky periods, a large ratio means that the sky is clear, and a small ratio that the sky is cloudy. We find the method works with about a 70% correct rate using red or green auroral light. With this method we can eliminate data during cloudy conditions for any auroral studies that require clear sky conditions. Key Points: Auroral keogram coefficient of variation is used to decide if the sky is cloudy or clear, verified with NOAA satellite data from 2014 to 2017The coefficient of variation distinguishes clear from cloudy auroral conditions with as much as 75% accuracyThis method is intended for subsequent automation of auroral all‐sky image analysis [ABSTRACT FROM AUTHOR]