We present a metric for detecting clouds in auroral all-sky images based on single-wavelength keograms made with a collocated meridian spectrograph. The coefficient of variation, the ratio of the sample standard deviation to the sample mean taken over viewing angle, is the metric for cloud detection. After calibrating and flat-field correcting keogram data, then excluding dark sky intervals, the effectiveness of the coefficient of variation as a detector is tested compared to true conditions as determined by Advanced Very High Resolution Radiometer (AVHRR) satellite imagery of cloud cover. 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 (PFRR). We use events that are completely cloud-free or completely cloudy according to AVHRR to compute the false alarm and missed detection statistics for the coefficient of variation of the greenline 557.7 nm emission and of the redline 630.0 nm emission. For training data of the years 2014 and 2016, we find a greenline threshold of 0.51 maximizes the percent of events correctly identified at 75%. When applied to testing data of the years 2015 and 2017, the 0.51 threshold yields an accuracy of 77%. There is a relatively shallow and wide minimum of mislabeled events for thresholds spanning about 0.2 to 0.8. For the same events, the minimum is narrower for the redline, spanning roughly 0.3-0.5, with a threshold of 0.46 maximizing detector accuracy at 78-79%.

This study summarizes paleomagnetic secular variation (PSV) in five published Holocene records from Eastern North America. We have developed 100-year increment time series for the declinations and inclinations for all sites and compared their directional variability. We see evidence of ten correlatable features in both inclination and declination. We focus on the clockwise or counter-clockwise motion of paleomagnetic directions (termed circularity) in these PSV records. We have first calculated the incremental rate and direction of motion (clockwise or counter-clockwise) for each record over the last 4000-8000 years. We have separately looked for discernable looping in individual records. We estimate the loop sizes, durations, and circularity direction. We see the same pattern of circularity in both measurement techniques. There are seven intervals of oscillating circularity and looping in all five sites. Both techniques suggest a distinctive oscillating, teeter-totter like, behavior to PSV circularity that must be due to the pattern of fluid flow in the outer core. This teeter-totter behavior is unbalanced with more time spent in clockwise motion than in counter-clockwise motion. We think the teeter-totter oscillation may be due to torsional oscillation in the outer core fluid flow. The loops have a distribution of sizes and durations with smaller loops being shorter in duration and bigger loops having longer durations. All five PSV records show 5 intervals of ~102 yr significant acceleration in circularity rate and PSV rate combined with change in circularity direction. These features are broadly analogous to historic geomagnetic jerks.

Atmospheric motion vectors (AMVs), known as cloud track winds, have positive impacts on global numerical weather forecasts (NWP). In this study, AMVs retrieved from Fengyun-2G and Fengyun-4A are compared in their data quality and impacts on the typhoon forecasts in order to investigate the differences between the first and second generation of the geostationary meteorological satellites of China. This report conducted data evaluation and assimilation-forecasting experiments on FY-2G and FY-4A atmospheric motion vector (AMVs), respectively. The results show that the AMVs data of FY-4A are of better quality than those of FY-2G and assimilating the AMVs of FY-2G and FY-4A have a neutral to slightly positive impacts on typhoon forecasts, which is quite encouraging for the operational use in the future.

Parameterizations for bottom shear stress are required to predict sediment resuspension from field observations and within numerical models that do not resolve flow within the viscous sublayer. This study assessed three observation-based bottom shear stress (τb) parameterizations, including (1) the sum of surface wave stress and mean current (quadratic) stress (τb= τw +τc); (2) the log-law (τb= τL); and (3) the turbulent kinetic energy (τb= τTKE); using two years of observations from a large shallow lake. For this system, the parameterization τb= τw +τc was sufficient to qualitatively predict resuspension, since bottom currents and surface wave orbitals were the two major processes found to resuspend bottom sediments. However, the τL and τTKE parameterizations also captured the development of a nepheloid layer within the hypolimnion associated with high-frequency internal waves. Reynolds-averaged Navier-Stokes (RANS) equation models parameterize τb as the summation of modeled current-induced bottom stress (τc,m) and modelled surface wave-induced bottom stress (τw,m). The performance of different parameterizations for τc,m and τw,m in RANS models was assessed against the observations. The optimal parameterizations yielded root-mean-square errors of 0.031 and 0.025 Pa, respectively, when τc,m, and τw,m were set using a constant canonical drag coefficient. A RANS-based τL parameterization was developed; however, the grid-averaged modelled dissipation did not always match local observations, leading to O(10) errors in prediction of bottom stress. Turbulence-based parameterizations should be further developed for application to flows with mean shear-free boundary turbulence.

The Moho is the interface between crust and mantle, and accurate location of the Moho is important for both resource exploration and deep earth condition and structural change investigations. The Parker-Oldenburg (P-O) method, is simple and efficient and thus has been extensively applied in the frequency domain and for Moho depth inversion. However, Moho fluctuation simulations using the P-O method are not reliable because of the lack of field geographic data constraints during the inversion process and excessively smoothing of data details caused by using a filter to correct the source data signals. To solve those problems, we propose an improved iteration P-O method with variable density, the iterative process is constrained by geological data in the inversion parameters, and the variable depth of the gravity interface is iterated using an equivalent form of upward continuation in the Fourier domain, which is more stable and convergent than downward continuation term in original P-O method. Synthetic experiments indicate that improved method has the better consistency among the simulations than original method, and our improved method has the smallest RMS of 0.59 km. In a real case, we employed the improved method to invert the Moho depth of the South China Sea, and the RMS between our Moho model and the seismological data is the smallest value of 3.87 km. The synthetic experiments and application of the model to the SCS further prove that our method is practical and efficient.