Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2512.10535

Convective and non-convective nature of Balearic meteotsunamis:a 50-year historical review

Agustí Jansà·Diego S. Carrió·Camilo Melo·Romualdo Romero·Baptiste Mourre·Víctor Homar
View original source ↗Read source document ↗Export metadata ↓

Abstract

Meteotsunamis, locally known as rissaga in the Balearic Islands, are significant sea-level oscillations induced by atmospheric disturbances, with amplitudes and frequencies comparable to seismic tsunamis. The port of Ciutadella (Menorca), is a recognized hotspot for meteotsunami occurrence. This study presents a 50-year analysis of 191 meteotsunami events recorded in Ciutadella between 1975 and 2025, focusing on the distinction between the convective and non-convective nature of the pressure disturbances that triggered the meteotsunami event and their implications for predictability and risk assessment. Events were classified using historical records, in-situ barographic and sea-level observations, satellite imagery, and lightning data. Results show that non-convective meteotsunamis are typically triggered by internal gravity waves, producing relatively regular and moderate pressure fluctuations. In contrast, convective events are associated with abrupt pressure jumps and tend to produce the most extreme sea-level oscillations, exceeding 3-4 meters in some cases, such as the catastrophic events of June 1984 and June 2006. Forecasting skill varied by event type: A full-realistic high-resolution ocean-atmosphere modelling system (BRIFS) performed better for convective cases, whereas a targeted reduced-physics method (TRAM) was more accurate for non-convective scenarios. This study provides the most extensive meteotsunami dataset for the Balearic Islands to date and offers a novel framework for understanding the contrasting dynamics and characteristics of both types of events. The results support the development of improved early-warning systems and more effective coastal risk mitigation strategies in vulnerable regions.

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Agustí Jansà, Diego S. Carrió, Camilo Melo, Romualdo Romero, Baptiste Mourre, Víctor Homar. 2025-12-11. Convective and non-convective nature of Balearic meteotsunamis:a 50-year historical review. https://arxiv.org/abs/2512.10535

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↗