Subsurface ocean temperature is a fundamental variable for understanding ocean circulation,upper-ocean heat content, stratification, climate variability, air-sea interaction and marine ecosystems. Accurate representation of the vertical ocean temperature is essential for applications such as marine heatwave monitoring, fisheries, and data assimilation, etc. However, direct measurements of subsurface temperature remain sparse because they rely primarily on in-situ observing systems such as ARGO p
The current problem statement proposes the to build a Satellite Embedding-Based Deep Learning Framework to reconstruct depth-wise subsurface temperature from daily surface satellite observations at 0.25°...
Subsurface ocean temperature is a fundamental variable for understanding ocean circulation,upper-ocean heat content, stratification, climate variability, air-sea interaction and marine ecosystems Accurate representation of the vertical ocean temperature is essential for applications such as marine heatwave monitoring, fisheries, and data assimilation, etc However, direct measurements of subsurface temperature remain sparse because they rely primarily on in-situ observing systems such as ARGO p
The current problem statement proposes the to build a Satellite Embedding-Based Deep Learning Framework to reconstruct depth-wise subsurface temperature from daily surface satellite observations at 0.25° spatial resolution for North Indian Ocean (5°N to 30°N and 45°E to 105°E).The objective is to estimate the three-dimensional ocean temperature using only surface satellite observations. The proposed system shall: 1. Develop a preprocessing and harmonization pipeline for multi-source satellite and ocean datasets. 2. Standardize all datasets to: a. Spatial Resolution: 0.25° Ã- 0.25° b. Temporal Resolution: Daily 3. Use surface observations as input variables: a. Sea Surface Temperature (SST) b. Sea Surface Salinity (SSS) c. Sea Surface Height (SSH) / Sea Level Anomaly (SLA) d. Surface