SearcharxivSearch

arXiv · 1902.04325

The emission of energetic electrons from the complex streamer corona adjacent to leader stepping

Abstract

We here propose a model to capture the complexity of the streamer corona adjacent to leader stepping and relate it to the production of energetic electrons serving as a source of X-rays and $\gamma$-rays, manifesting in terrestrial gamma-ray flashes (TGFs). During its stepping, the leader tip is accompanied by a corona consisting of multitudinous streamers perturbing the air in its vicinity and leaving residual charge behind. We explore the relative importance of air perturbations and preionization on the production of energetic run-away electrons by 2.5D cylindrical Monte Carlo particle simulations of streamers in ambient fields of 16 kV cm$^{-1}$ and 50 kV cm$^{-1}$ at ground pressure. We explore preionization levels between $10^{10}$ m$^{-3}$ and $10^{13}$ m$^{-3}$, channel widths between 0.5 and 1.5 times the original streamer widths and air perturbation levels between 0\% and 50\% of ambient air. We observe that streamers in preionized and perturbed air accelerate more efficiently than in non-ionized and uniform air with air perturbation dominating the streamer acceleration. We find that in unperturbed air preionization levels of $10^{11}$ m$^{-3}$ are sufficient to explain run-away electron rates measured in conjunction with terrestrial gamma-ray flashes. In perturbed air, the production rate of runaway electrons varies from $10^{10}$ s$^{-1}$ to $10^{17}$ s$^{-1}$ with maximum electron energies from some hundreds of eV up to some hundreds of keV in fields above and below the breakdown strength. In the presented simulations the number of runaway electrons matches with the number of energetic electrons measured in alignment with the observations of terrestrial gamma-ray flashes. Conclusively, the complexity of the streamer zone ahead of leader tips allows explaining the emission of energetic electrons and photons from streamer discharges.

Explore related subjects

Keep this discovery

BibTeXRIS

C. Köhn, O. Chanrion, K. Nishikawa, L. Babich, T. Neubert. 2019-02-12. The emission of energetic electrons from the complex streamer corona adjacent to leader stepping. https://doi.org/10.1088/1361-6595/ab6e57

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph