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Quasi-periodic plasmoid formation at the tip of magnetic streamer structures is observed to occur in experiments on the Big Red Ball as well as in simulations of these experiments performed with the extended magnetohydrodynamics code, NIMROD. This plasmoid formation is found to occur on a characteristic time scale dependent on pressure gradients and magnetic curvature in both experiment and simulation. Single mode, or laminar, plasmoids exist when the pressure gradient is modest, but give way to turbulent plasmoid ejection when the system drive is higher, which produces plasmoids of many sizes. However, a critical pressure gradient is also observed, below which plasmoids are never formed. A simple heuristic model of this plasmoid formation process is presented and suggested to be a consequence of a dynamic loss of equilibrium in the high- $$\beta$$ region of the helmet streamer. This model is capable of explaining the periodicity of plasmoids observed in the experiment and simulations, and produces plasmoid periods of 90 minutes when applied to two-dimensional models of solar streamers with a height of $$3R_\odot$$ . This is consistent with the location and frequency at which periodic plasma blobs have been observed to form by Large Angle and Spectrometric Coronograph and Sun Earth Connection Coronal and Heliospheric Investigation instruments. 

Abstract Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s −1 and RMSEs of 2-3 m s −1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced. 

Abstract The distribution of turbulent kinetic energy (TKE) and its budget terms is estimated in simulated tropical cyclones (TCs) of various intensities. Each simulated TC is subject to storm motion, wind shear, and oceanic coupling. Different storm intensities are achieved through different ocean profiles in the model initialization. For each oceanic profile, the atmospheric simulations are performed with and without TKE advection. In all simulations, the TKE is maximized at low levels (i.e., below 1 km) and ∼0.5 km radially inward of the azimuthal‐mean radius of maximum wind speed at 1‐km height. As in a previous study, the axisymmetric TKE decreases with height in the eyewall, but more abruptly in simulations without TKE advection. The largest TKE budget terms are shear generation and dissipation, though variability in vertical turbulent transport and buoyancy production affect the change in the azimuthal‐mean TKE distribution. The general relationships between the TKE budget terms are consistent across different radii, regardless of storm intensity. In terms of the asymmetric distribution in the eyewall, TKE is maximized in the front‐left quadrant where the sea surface temperature (SST) is highest and is minimized in the rear‐right quadrant where the SST is the lowest. In the category‐5 simulation, the height of the TKE maximum varies significantly in the eyewall between quadrants and is between ∼400 m in the rear‐right quadrant and ∼1,000 m in the front‐left quadrant. When TKE advection is included in the simulations, the maximum eyewall TKE values are downwind compared to the simulations without TKE advection.