SearcharxivSearch

arXiv · 2008.01666

Moored observations of turbulent mixing events in deep Lake Garda (I)

Abstract

Deep water circulation and mixing processes in deep lakes are largely unknown, although they are responsible for the transport of matter, nutrients and pollutants. Such a lack of knowledge cannot be reliably provided by numerical hydrodynamic modelling studies because detailed observations are typically not available to validate them. To overcome some of these deficiencies, a dedicated yearlong mooring comprising 100 high-resolution temperature sensors and a single current meter were located in the deeper half of the 344 m deepest point of the subalpine Lake Garda (Italy). The observations show peaks and calms of turbulent exchange, besides ubiquitous internal wave activity. In late winter, northerly winds activate episodic deep convective overturning, the dense water being subsequently advected along the lake-floor. Besides deep convection, such winds also set-up seiches and inertial waves that are associated with about 100 times larger turbulence dissipation rates than that by semidiurnal internal wave breaking observed in summer. In the lower 60 m above the lake-floor however, the average turbulence dissipation rate is approximately constant in value year-around, being about 10 times larger than open-ocean values, except during deep convection episodes.

Explore related subjects

Keep this discovery

BibTeXRIS

Hans van Haren, Sebastiano Piccolroaz, Marina Amadori, Marco Toffolon, Henk A. Dijkstra. 2020-08-04. Moored observations of turbulent mixing events in deep Lake Garda (I). https://arxiv.org/abs/2008.01666

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