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Gaussian Process Regression for Ocean Temperature Anomaly Detection

This project explores the detection of climate anomalies using Gaussian Process Regression (GPR) on real satellite-derived oceanographic data. The goal is to model the expected behavior of a key variable (tdrop, representing the temperature drop across the ocean's cool skin layer) and identify regions or times where observations deviate significantly from this model.

Project Overview

  • Data: NASA satellite reanalysis data from the MERRA-2 dataset, available at Source: Kaggle – OCEAN DATA / CLIMATE CHANGE / NASA
  • Model: Gaussian Process Regression (scikit-learn)
  • Target: tdrop (temperature drop across cool layer)
  • Features:
    • tbar – average temperature of the interface layer
    • tskinice – skin temperature over sea ice
    • rainocn – ocean rainfall
    • delts – surface skin temperature change
  • Training: August 1st, 2018
  • Testing & Anomaly Detection: August 1st, 2021

Requirements:

Python 3.8+ NumPy, pandas, matplotlib, xarray scikit-learn joblib

How to run it:

Train the model (optional, will be auto-triggered if missing):

  • Run train_model.ipynb or
  • Call train_and_save_gpr_model() from train_model.py Run the analysis:
  • Open analyze_results.ipynb
  • or run the script python analyze_results.py

Repository Structure

├── train_model.ipynb           # Notebook to train GPR model
├── analyze_results.ipynb       # Notebook to evaluate predictions and detect anomalies
└── README.md                   # You are here!