Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1811.06212

Mapping Observation Project of High-Energy Phenomena during Winter Thunderstorms in Japan

Yuuki Wada·Teruaki Enoto·Yoshihiro Furuta·Kazuhiro Nakazawa·Takayuki Yuasa·Takahiro Matsumoto·Daigo Umemoto·Kazuo makishima·Harufumi Tsuchiya·the GROWTH collaboration
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Gamma-Ray Observation of Winter Thunderclouds (GROWTH) collaboration has been performing observation campaigns of high-energy radiation in coastal areas of Japan Sea. Winter thunderstorms in Japan have unique characteristics such as frequent positive-polarity discharges, large discharge current, and low cloud bases. These features allow us to observe both long-duration gamma-ray bursts and lightning-triggered short-duration bursts at sea level. In 2015, we started a mapping observation project using multiple detectors at several new observation sites. We have developed brand-new portable gamma-ray detectors and deployed in the Kanazawa and Komatsu areas as well as the existing site at Kashiwazaki. During three winter seasons from 2015, we have detected 27 long-duration bursts and 8 short-duration bursts. The improved observation network in Kashiwazaki enables us to discover that the short-duration bursts are attributed to atmospheric photonuclear reactions triggered by a downward terrestrial gamma-ray flash. Collaborating with electric-field and radio-band measurements, we have also revealed a relation between abrupt termination of a long-duration burst and a lightning discharge. We demonstrate that the mapping observation project has been providing us clues to understand high-energy atmospheric phenomena associated with thunderstorm activities.

Explore related subjects

physics.ao-phphysics.ins-det

Keep this discovery

BibTeXRIS

Yuuki Wada, Teruaki Enoto, Yoshihiro Furuta, Kazuhiro Nakazawa, Takayuki Yuasa, Takahiro Matsumoto, Daigo Umemoto, Kazuo makishima, Harufumi Tsuchiya, the GROWTH collaboration. 2018-11-15. Mapping Observation Project of High-Energy Phenomena during Winter Thunderstorms in Japan. https://arxiv.org/abs/1811.06212

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗