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

Wide-swath satellite altimetry and novel subsurface temperature observations improve predictions in a dynamic western boundary current: System optimization and performance

Improving ocean bottom pressure fields using space gravity data in state estimation

Using microwave imager observations to improve the ocean temperature in a coupled NWP system

Knowledge of the ocean temperature is vital to the accurate use of satellite radiances in weather forecasting and it helps improve the quality of forecasts for the ocean and atmosphere. A recent upgrade to the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) has implemented outer‐loop coupling between the atmosphere and the ocean. The current work uses this method to pass ocean skin‐temperature estimates derived from satellite‐borne microwave imagers to the ocean data assimilation (DA) system; the updated ocean state is then passed back to the atmospheric DA system. It is shown that these skin temperature estimates improve the fit of the short‐range forecast ocean temperature profiles to Array for Real‐Time Geostrophic Oceanography (ARGO) float observations, resulting in up to 4% improvements in the Eastern Tropical Pacific. Improvements in the forecast skin temperature are also recorded indirectly through improvements in the fit of significant wave height observations and infrared channels sensitive to the surface on‐board geostationary satellites. In terms of the atmospheric forecast verification, including the mean fields, the most significant changes are apparent in the medium range (2 to 6 days). Statistically significant improvements are seen in the Tropics for relative humidity at the surface and in the Southern Hemisphere for temperature, vector winds and geopotential height.

Influence of local and remote climate processes on driving the salinification followed by freshening phases in the eastern Arabian Sea

Sea surface salinity (SSS) modulates upper-ocean stratification, air–sea coupling, and freshwater redistribution in the North Indian Ocean, yet its interannual variability in the eastern Arabian Sea (EAS) remains insufficiently quantified. This study evaluates satellite-derived SSS from the Soil Moisture and Ocean Salinity, Soil Moisture Active Passive, and European Space Agency-Climate Change Initiative (CCI) products using high-precision shipborne measurements across the EAS. Validation results show that CCI SSS exhibits strong agreement with in situ observations and best reproduces the spatiotemporal variability of the in situ gridded dataset (Willmott skill score 0.81). CCI also exhibit relatively lower spatial root-mean-square error (0.93 PSU) compared to other Satellite derived datasets. The analysis of interannual SSS variability using the CCI dataset during 2010–2023 reveals two contrasting regimes: a salinification phase during 2015–2019 (+0.5 PSU) followed by a pronounced freshening phase during 2020–2023 (−0.5 PSU). Analysis of precipitation, ocean circulation, and climate indices indicates that reduced rainfall and weak horizontal advection dominated the salinification phase. In contrast, the freshening phase was driven by enhanced precipitation and intensified freshwater transport from the Bay of Bengal. This period coincides with the rare triple-dip La Niña (2020–2022), which strengthened coastal Kelvin waves and the East India Coastal Current, facilitating transport of low-salinity waters through the “River-in-the-Sea”(RIS) pathway. These results highlight the sensitivity of EAS salinity to coupled local and remote freshwater processes and demonstrate the importance of multi-sensor satellite SSS products for monitoring climate-driven hydrological variability.

Satellite Altimetry Signal Processing for Eddy Kinetic Energy Estimation in the Indonesian Seas: Toward Intelligent Ocean Monitoring

Indonesia hosts one of the most dynamically complex marine systems due to the interaction of the Indonesian Throughflow, monsoonal circulation, and complex bathymetry. These processes generate strong mesoscale variability that can be quantified using Eddy Kinetic Energy (EKE). This study proposes a satellite-altimetry-based framework for estimating EKE across the Indonesian Seas using Jason-3 observations integrated with Sea Level Anomaly (SLA) products from the Copernicus Marine Environment Monitoring Service (CMEMS). Geostrophic velocity anomalies were derived from SLA gradients and subsequently used to calculate EKE. Several preprocessing steps, including geophysical corrections, noise filtering, and anomaly normalization, were applied to improve data quality. An intelligent monitoring framework was developed to support automated data ingestion, EKE computation, and anomaly detection. Results indicate pronounced spatial variability of EKE, with elevated values observed in the Makassar Strait, Banda Sea, and southern Java waters. Seasonal analysis reveals stronger EKE during the southeast monsoon, while interannual variability demonstrates links with ENSO and the Indian Ocean Dipole. The proposed framework highlights the potential of integrating satellite altimetry and intelligent monitoring systems for operational ocean observation, environmental assessment, and coastal resilience applications.

The consistent in-situ global gridded temperature and salinity dataset, CORA OA 1960-2024