Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2210.02150

A comparison of stochastic and deterministic downscaling in eddy resolving ocean modelling: the Lakshadweep Sea case study

Georgy I. Shapiro·Jose M. Gonzalez-Ondina·Mohammed Salim·Jiada Tu
View original source ↗Read source document ↗Export metadata ↓

Abstract

This study compares the skills of two numerical models having the same horizontal resolution but based on different principles in representing meso- and submesoscale features of ocean dynamics in the Lakshadweep Sea (North Indian Ocean). The first model, titled LD20-NEMO, is based on solving primitive equations using the NEMO (Nucleus for European Modelling of the Ocean) modelling engine. The second one, titled LD20-SDD, uses a newer Stochastic-Deterministic Downscaling method. Both models have 1/20o resolution and use the outputs from a Global Ocean Physics Analysis and Forecast model at 1/12o resolution available from Copernicus Marine Service (CMEMS). The LD20-NEMO uses only a 2D set of data from CMEMS as lateral boundary conditions. The LD20-SDD consumes the full 3D set of data from CMEMS and exploits the stochastic properties of these data to generate the downscaled field variables at higher resolution than the parent model. The skills of the three models, CMEMS, LD20-NEMO and LD20-SDD are assessed against remotely sensed and in-situ observations for the four-year period 2015-2018. All models show similar skills in reproducing temperature and salinity, however the SDD version performs slightly better than the NEMO version. This difference in resolution is particularly significant in simulation of vorticity and computation of the share of the sea occupied by highly non-linear processes. While the NEMO and SDD model show similar skill, the SDD model is more computationally efficient than the NEMO model by a large margin.

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Georgy I. Shapiro, Jose M. Gonzalez-Ondina, Mohammed Salim, Jiada Tu. 2022-10-05. A comparison of stochastic and deterministic downscaling in eddy resolving ocean modelling: the Lakshadweep Sea case study. https://arxiv.org/abs/2210.02150

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↗