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El Ni\~no-Southern Oscillation (ENSO) is the most prominent interannual climate variability in the tropics and exhibits diverse features in spatiotemporal patterns. In this paper, a simple multiscale intermediate coupled stochastic model is…
Mesoscale eddies are of utmost importance in understanding ocean dynamics and the transport of heat, salt, and nutrients. Accurate representation of these eddies in ocean models is essential for improving model predictions. However,…
Stochastic wind sea is an intermediate small-scale physical process responsible for the state of the atmospheric boundary layer and the water upper layer, having dynamics of all scales. To describe behavior of this system, one could use the…
Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in…
This paper presents a method for estimating significant wave height (Hs) from sparse S_pectral P_oint using a T_ransformer-based approach (SPT). Based on empirical observations that only a minority of spectral points with strong power…
Variations in sea-level, based on tide gauge data (GSLTG) and on combining tide gauges and satellite data (GSLl) are subjected to singular spectrum analysis (SSA), to determine their trends and periodic or quasi-periodic components. GLSTG…
Like elsewhere in the deep-sea, life in the deep Mediterranean depends on turbulent exchange across the stable vertical density stratification for supply of nutrients and oxygen. Commonly modelled, turbulent exchange is inversely…
A fundamental challenge in ocean dynamics is disentangling balanced motions and internal waves. Extracting internal tidal (IT) imprints from surface data is a central part of this challenge. Traditional harmonic analysis can fail under…
Using the APEX-12m telescope, continuum maps at 350~$\mu$m of eight gas-dust clouds from the southern hemisphere are obtained. Clouds are associated with the regions of massive star and star cluster formation and have dense cores. Core…
We use the Global Historical Climatology Network--daily database to calculate trends in sea-level atmospheric pressures, their variance and the variance of their day-to-day differences in nine regions of the world. Changes in pressure…
We propose a nonlinear ocean forecasting technique based on a combination of genetic algorithms and empirical orthogonal function (EOF) analysis. The method is used to forecast the space-time variability of the sea surface temperature (SST)…
We present an end-to-end deep learning framework for short-term forecasting of global sea surface dynamics based on sparse satellite altimetry data. Building on two state-of-the-art architectures: U-Net and 4DVarNet, originally developed…
Surface runoff shapes planetary landscapes, but global hydrological models often lack the resolution and flexibility to simulate dynamic surface water bodies beyond Earth. Recent studies of Mars have revealed abundant geological and…
We present a framework for forecasting significant wave height on the Southwestern Atlantic Ocean using the long short-term memory algorithm (LSTM), trained with the ERA5 database available through Copernicus Climate Data Store (CDS)…
Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size $B$, and the step size or learning rate $\eta$. For small $B$ and large $\eta$,…
We analyze the equilibrium spatial distribution of surface temperatures of patterned surfaces. The surface is exposed to a constant external heat flux and has a fixed internal temperature that is coupled to the outside heat fluxes by finite…
Oceanic eddy kinetic energy (EKE) is a key quantity for measuring the intensity of mesoscale eddies and for parameterizing eddy effects in ocean climate models. Three decades of satellite altimetry observations allow a global assessment of…
We introduce a nonstationary spatio-temporal statistical model for gridded data on the sphere. The model specifies a computationally convenient covariance structure that depends on heterogeneous geography. Widely used statistical models on…
A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is a critical tool for measuring precise and accurate distances across a large redshift range and constraining cosmological parameters. We present an improved model…
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional…