AI-Driven Ocean Prediction, Climate Forecasting, and Marine Intelligence

The aim of this workshop is to provide participants with practical knowledge of how artificial intelligence and open-source ocean data tools can be used for ocean prediction, climate forecasting, and marine intelligence.

• To introduce participants to the fundamentals of ocean prediction, climate forecasting, and marine intelligence.
• To help participants understand key marine and climate variables such as sea surface temperature, salinity, wind, waves, rainfall, pressure, and ocean currents.
• To provide practical exposure to ocean and climate datasets from sources such as NOAA, NASA, Copernicus Marine, ERA5, buoy data, satellite data, and Argo floats.
• To train participants in handling NetCDF, time-series, and geospatial ocean datasets.
• To enable participants to preprocess, clean, visualize, and interpret marine and climate data using open-source tools.

📅 Day 1: Ocean & Climate Data Foundations

• Role of ocean prediction in climate resilience, disaster preparedness, fisheries, shipping, and coastal planning
• Key marine and climate variables: sea surface temperature, salinity, wind, waves, rainfall, pressure, and ocean currents
• Introduction to satellite, buoy, Argo float, NOAA, NASA, Copernicus Marine, and ERA5 datasets
• Basics of NetCDF, time-series, and geospatial ocean data
• Data preprocessing, visualization, and pattern identification for marine intelligence

🛠️ Hands-on 1: Ocean & Climate Dataset Exploration

• Participants will explore real/sample marine datasets, understand variables, coordinates, and time dimensions, and visualize ocean-climate patterns.

🛠️ Hands-on 2: Marine Data Visualization & Preprocessing

• Participants will clean data, handle missing values, plot trends, and create basic maps or time-series visualizations for selected ocean variables.

📅 Day 2: AI Models for Ocean Prediction & Climate Forecasting

• AI workflow for ocean and climate prediction
• Feature selection for marine forecasting variables
• Machine learning models for sea surface temperature, wave height, wind speed, rainfall, and ocean-current prediction
• Time-series forecasting using ML and deep learning approaches
• Model evaluation using error metrics and prediction visualization
• Challenges: uncertainty, data gaps, bias, and model reliability in climate forecasting

🛠️ Hands-on 1: Ocean Variable Prediction Using AI Models

• Participants will build a basic machine learning model to predict a marine or climate variable such as sea surface temperature, wave height, wind speed, or rainfall.

🛠️ Hands-on 2: Climate Time-Series Forecasting Notebook

• Participants will create a simple forecasting workflow, compare predicted and actual values, and interpret model performance.

📅 Day 3: Marine Intelligence, Risk Mapping & Decision Support

• AI for marine intelligence, coastal risk, cyclone monitoring, flood prediction, and early warning systems
• Marine heatwave detection, anomaly identification, and climate impact assessment
• Applications in fisheries, biodiversity, shipping, offshore operations, and coastal management
• Explainable AI for interpreting ocean and climate models
• Digital twins, physics-informed AI, generative AI, and future trends in ocean forecasting
• Real-world deployment challenges in marine and climate intelligence systems

🛠️ Hands-on 1: Marine Risk Mapping & Climate Impact Visualization

• Participants will create simple risk maps or anomaly visualizations such as sea surface temperature anomalies, high-wave zones, or marine heatwave indicators.

🛠️ Hands-on 2: Mini Project: AI-Based Ocean or Climate Forecasting

• Participants will complete a guided mini project by selecting a dataset, preprocessing it, building a prediction or visualization workflow, and interpreting results for real-world marine decision-making.

🧰 Tools Used in the Workshop

• Google Colab, Python, Pandas, Xarray, Scikit-learn

• PhD scholars, research scholars, and postgraduate students from science, engineering, environmental science, climate science, oceanography, marine science, and related fields.
• Researchers, academicians, faculty members, and industry professionals interested in AI applications for ocean and climate systems.
• Professionals working in climate analytics, environmental monitoring, disaster management, coastal planning, fisheries, shipping, offshore operations, or sustainability.
• Students and learners interested in AI, machine learning, climate forecasting, ocean data analysis, and marine intelligence.
• Basic knowledge of Python, data analysis, or machine learning will be helpful but is not mandatory.
• Prior experience with oceanography or climate science is useful, but beginners with interest in the topic can also participate.

• Understand the role of AI in ocean prediction, climate forecasting, and marine intelligence.
• Identify and interpret key marine and climate variables such as sea surface temperature, salinity, wind, waves, rainfall, pressure, and ocean currents.
• Explore ocean and climate datasets from sources such as NOAA, NASA, Copernicus Marine, ERA5, satellite data, buoy data, and Argo floats.
• Work with NetCDF, time-series, and geospatial ocean data using open-source tools.
• Preprocess, clean, and visualize marine datasets for ocean and climate analysis.
• Use Python, Pandas, NumPy, Matplotlib, Xarray, NetCDF4, and Scikit-learn for hands-on ocean data workflows.