An Intelligent Reconstruction Method for Ocean Subsurface Temperature Empowered by isQG

Data Driven Reconstruction of Upper Ocean Profiles for Improved State Estimation in the Philippine Sea

Despite significant advances in observational systems such as the global Argo array of autonomous profiling floats, the spatiotemporal coverage of subsurface ocean observations remains limited compared to the dense data provided by satellite platforms. This study develops a data‐driven framework to reconstruct synthetic profiles of upper ocean temperature and salinity by training a self‐attention‐based neural network with satellite‐derived sea surface height (SSH) and sea surface temperature anomalies, using 17 years of collocated Argo float measurements. Daily synthetic profiles for the upper 650 m of the Philippine Sea were generated for the entirety of 2010 and assimilated into a regional ocean model via 4D‐Var data assimilation. Results show overall improved effectiveness of state estimation when synthetic profiles are utilized. Diagnostic variables like temperature, salinity, SSH, horizontal velocity all show improvement. Synthetic profiles of subsurface temperature had an overall positive impact on SSH analysis and forecast, especially in regions east of the Luzon Strait and the southern domain influenced by the North Equatorial Current. In the vertical range of 150–600 m, the impact of synthetic profiles on various observations was promising, leading to substantial reductions in the analysis and forecast error.

OceanVerse: Evaluable 4D Ocean Element Reconstruction Dataset under Realistic Sparsity

The first oceanic 4D sparse observation reconstruction dataset, named OceanVerse, is presented, providing a novel large-scale dataset that meets the MNAR (Missing Not at Random) condition, supporting more effective model comparison, generalization evaluation and potential advancement of scientific reconstruction architectures.

High-Resolution Downscaling of Urban Land Surface Temperature via Machine Learning

Abstract. Land surface temperature (LST) obtained from satellite observations is a key parameter for understanding Earth surface-atmosphere energy exchange and urban thermal environments. However, the use of existing satellite-derived LST datasets for urban applications is limited by the coarse spatial resolution and the mixed-pixel problem. By integrating both two-dimensional (2D) surface properties and three-dimensional (3D) urban morphological characteristics, this study proposes a machine learning-based framework for high-resolution downscaling of satellite-based urban land surface temperature (SULST). A Random Forest model was developed to generate a 1-m downscaled SULST (DSULST) map. The model demonstrates strong performance, with a Pearson correlation coefficient of 0.89, RMSE of 1.15 K, NRMSE of 0.095, MAE of 0.56 K, and an index of agreement of 0.95. The 1-m DSULST maps reveal substantial sub-pixel thermal heterogeneity that is not captured by conventional 30-m LST data. Fine-scale spatial patterns associated with vegetation, building structures, and roads are clearly resolved in the downscaled 1-m temperature maps. These results highlight a critical limitation of satellite-derived LST in representing intra-urban thermal variability. The findings demonstrate that enhancing the spatial resolution of urban LST is essential for urban applications, including modeling surface energy fluxes, pedestrian-level heat exposure, and energy consumption, all of which benefit from higher spatial resolution.

The Importance of Subsurface Thermal Effects to Indian Ocean Dipole Prediction Revealed by a Skillful Three-Dimensional Deep Learning Model

OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

OceanDepths is introduced, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature, sea surface salinity, and sea surface height products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support comparisons or multi-stage learning.

Bathymetry Prediction With SWOT Gravity Anomaly Using Machine Learning Methods: Paper 2—Model Evaluation and Uncertainty Analysis

Despite millions of ship soundings, bathymetry from satellite derived gravity is still necessary to fill in approximately three quarters of the global oceans. The methods used to carry out this inference have not changed significantly since the 1990s; however new methodology involving Machine Learning (ML) improves the bathymetric predictions considerably. Here we utilize five independent ML models from a workshop at the Technical University of Denmark (DTU). We highlight the benefits achieved by either (a) inclusion of a new highly‐accurate gravity field from the Surface Water and Ocean Topography (SWOT) satellite (∼22% improvement in spatial resolution), or (b) highly flexible ML methods capable of inferring bathymetric regimes not previously possible. By taking advantage of these five independent models we can determine regions of high confidence in our bathymetric inversion as well as regions with challenging conditions. Building on model prediction confidence and features revealed from the dense gravity field obtained by SWOT, we present a global features‐of‐interest map that, if mapped by ship soundings, would yield the largest improvement of the global bathymetry. These features are primarily located in regions with sparse multibeam coverage and associated with large gravity anomalies in the marine gravity field, indicating a potential presence of complex seafloor topography.