Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1807.01973

Surface Mixing by Geostrophic Flows in the Bay of Bengal

Nihar Paul·Jai Sukhatme
View original source ↗Read source document ↗Export metadata ↓

Abstract

Mixing in the Bay of Bengal, driven by altimetry derived daily geostrophic surface currents, is studied on subseasonal timescales. Hovm{\"o}ller and wavenumber-frequency diagrams with power spectra confirm the multiscale nature of the flow. Advection of bands immediately brings out the chaotic nature of mixing in the Bay via repeated straining and filamentation of the tracer field. A principal finding is that mixing is local, i.e., of the scale of the eddies, and does not span the entire basin. Indeed, Finite Time Lyapunov Exponent (FTLE), Relative Dispersion (RD) and Finite Size Lyapunov Exponents (FSLE) maps in all seasons are patchy with minima scattered through the interior of the Bay. Non-uniform stirring of the Bay is reflected in long tailed histograms of FTLEs, that become more stretched for longer time intervals. Quantitatively, advection for a week shows the mean FTLE lies near 0.15-0.16 $day^{-1}$, while extremes reach almost 0.5 $day^{-1}$. Averaged over the Bay, RD initially grows exponentially, this is followed by a power-law at scales between approximately 100 and 250 $km$, which finally transitions to an eddy-diffusive regime. These findings are confirmed by FSLEs; in addition, below 250 $km$, a scale dependent diffusion coefficient is extracted, while above 250 $km$, eddy-diffusivities range from $6 \times 10^3$ - $10^4$ $m^2/s$. Finally, with satellite salinity data, these Lagrangian tools are used in the analysis a single post-monsoonal fresh water mixing event. Here, FTLEs and FSLEs allow the identification of transport barriers, and elucidate how eddies help preserve the identity of fresh water.

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Nihar Paul, Jai Sukhatme. 2018-07-05. Surface Mixing by Geostrophic Flows in the Bay of Bengal. https://doi.org/10.1016/j.dsr2.2019.104684

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↗