Machine Learning for Climate Modeling and Prediction

INTERNATIONAL TRAINING ON MACHINE LEARNING FOR CLIMATE MODELING AND PREDICTION

COURSE BACKGROUND

The accelerating pace of climate change and the growing demand for accurate, high-resolution climate predictions necessitate innovative approaches beyond traditional climate modeling. Machine learning (ML), a powerful subset of artificial intelligence, offers unprecedented capabilities to analyze vast and complex climate datasets, identify subtle patterns, and improve the accuracy and efficiency of climate models. From forecasting extreme weather events to downscaling global climate projections and optimizing renewable energy systems, ML is transforming climate science.

ECAS Institute offers this “Machine Learning for Climate Modeling and Prediction” course to equip professionals and researchers with the cutting-edge skills required to leverage AI in addressing urgent climate challenges. This program will bridge the gap between machine learning techniques and their practical application in environmental and climate sciences. Participants will gain hands-on experience with relevant algorithms and tools, enabling them to contribute to more robust climate understanding, prediction, and ultimately, effective climate action.

COURSE OBJECTIVES OF THE TRAINING

Upon successful completion of this course, participants will be able to:

Understand the fundamental concepts of machine learning relevant to climate science.
Apply unsupervised learning techniques (e.g., clustering, dimensionality reduction) to climate datasets.
Utilize supervised learning algorithms (e.g., regression, classification) for climate prediction and analysis.
Explore the application of deep learning models in complex climate phenomena.
Evaluate the strengths and limitations of machine learning in enhancing traditional climate models.
Develop data-driven models for forecasting extreme weather events and long-term climate patterns.
Critically assess the quality and interpretability of ML-driven climate predictions.

WHAT YOU WILL LEARN

This course will enable you to apply cutting-edge machine learning techniques to real-world climate data, improving modeling capabilities and predictive accuracy. You will learn:

Foundational machine learning concepts, including supervised, unsupervised, and deep learning.
How to prepare and process diverse climate datasets for ML applications.
The application of various ML algorithms (e.g., regression, classification, clustering, neural networks) to climate variables like temperature, precipitation, and atmospheric pressure.
Techniques for improving the accuracy and efficiency of climate models using ML.
Methods for predicting extreme weather events and identifying climate trends.
How to use ML for downscaling global climate models to provide regional projections.
The principles of physics-informed machine learning and integrating physical constraints into ML models.
Best practices for evaluating, validating, and interpreting ML models in climate science.

DURATION AND PROGRAM

This is a structured training course designed to provide comprehensive insights and practical skills in applying machine learning for climate modeling and prediction. The program will combine theoretical explanations with hands-on exercises, practical demonstrations, and case studies drawn from real-world climate data. Participants will gain experience using relevant software and tools. The detailed program schedule, including specific session timings and learning activities, will be communicated upon registration.

TARGET PARTICIPANTS

This course is designed for professionals, researchers, and students interested in leveraging advanced computational methods to address climate challenges. It is particularly beneficial for:

Climate scientists and atmospheric researchers
Environmental data scientists and analysts
Meteorologists and weather forecasters
Geoscientists and hydrologists
Data scientists and machine learning engineers seeking to apply their skills in environmental domains.
Professionals involved in climate risk assessment, policy, and adaptation.
Graduate students and academics in environmental science, data science, or related fields.

TRAINING MODULES

The course content is structured to provide a comprehensive understanding of machine learning applications in climate science, from foundational concepts to advanced predictive modeling:

No	Module	Details
1.	Introduction to Machine Learning for Climate Science	This module provides an overview of the intersection of machine learning and climate science, highlighting the challenges and opportunities. Key Topics: Introduction to AI and ML in Climate Modeling Why ML is crucial for climate data Challenges in climate data (sparsity, bias, spatio-temporal complexity) Overview of ML paradigms: Supervised, Unsupervised, Deep Learning
2.	Data Preprocessing and Exploratory Data Analysis for Climate Data	This module focuses on preparing climate datasets for machine learning applications, including handling diverse data types and formats. Key Topics: Climate data sources and types (satellite, weather stations, model outputs) Data cleaning, normalization, and feature engineering Handling spatio-temporal data Exploratory data analysis for climate variables
3.	Unsupervised Learning for Climate Data Analysis	This module introduces techniques for finding patterns and reducing dimensionality in climate datasets without explicit labels. Key Topics: Clustering algorithms (e.g., K-Means, DBSCAN) for identifying climate regimes Dimensionality reduction (e.g., Principal Component Analysis – PCA, Singular Value Decomposition – SVD) for complex climate variables Applications in anomaly detection and regional pattern identification
4.	Supervised Learning for Climate Prediction (Regression)	This module covers techniques for predicting continuous climate variables, such as temperature or precipitation levels. Key Topics: Linear Regression and Multiple Linear Regression Model evaluation metrics for regression (e.g., RMSE, R-squared) Time series forecasting methods (e.g., ARIMA, Prophet) for climate variables Case studies: Temperature forecasting, streamflow prediction
5.	Supervised Learning for Climate Prediction (Classification)	This module focuses on techniques for predicting categorical outcomes, such as the likelihood of extreme events or climate classifications. Key Topics: Logistic Regression Decision Trees and Random Forests Support Vector Machines (SVMs) Case studies: Extreme weather event prediction, cloud detection
6.	Deep Learning for Complex Climate Phenomena	This module introduces advanced neural network architectures and their application to complex, non-linear climate dynamics. Key Topics: Introduction to Neural Networks (Feedforward, Multilayer Perceptron) Convolutional Neural Networks (CNNs) for image-based climate data (e.g., satellite imagery) Recurrent Neural Networks (RNNs) and LSTMs for time-series data (e.g., El Niño prediction) Physics-informed machine learning for climate models
7.	Model Evaluation, Interpretability, and Uncertainty	This module covers best practices for assessing the performance, understanding the decisions, and quantifying uncertainty in ML-driven climate models. Key Topics: Advanced model evaluation techniques Explainable AI (XAI) in climate science Uncertainty quantification in ML predictions Addressing data biases and limitations of ML models
8.	Applications and Future Directions	This module explores a broader range of applications and discusses emerging trends and research frontiers in ML for climate science. Key Topics: ML for renewable energy optimization and grid management ML in environmental monitoring and conservation AI for climate policy planning and impact assessment Challenges and opportunities in AI for climate action

TRAINING STYLE

The modules will be taught through PowerPoint presentations, and lectures and will include a case study/field visit, breakout sessions, case studies and other interactive discussion components.

The course will also include a few guest speakers, both in person and via Zoom and other online learning platforms for overseas speakers. This provides useful real-world insights alongside the more theoretical aspects of the course.

The conference faculty shall consist of experienced decision makers, as well as practitioners and representatives from established educational and research institutions active around climate change, engineering and international development. Throughout the course, theoretical presentation of concepts will be moderated and more group discussions and plenary engagements will be optimized. PowerPoint presentations will be made by facilitators and resource persons, to highlight key concepts before embarking on group work.

GENERAL NOTES

Training manuals and additional reference materials are provided to the participants.
Upon successful completion of this course, participants will be issued with a certificate.
We can also do this as a tailor-made course to meet organization-wide needs. Contact us to find out more: info@ecasiafrica.org.
Payment should be sent to our bank account before the start of training and proof of payment sent to: info@ecasiafrica.org.

ABOUT ECAS INSTITUTE

The ECAS Institute designs and delivers independent and targeted training, research, and consulting services. Our work focusses on climate change and resilience building, carbon markets, renewable energy, nature-based solution, biodiversity conservation, agriculture and food systems, We are located in Nairobi Kenya and work across the African region. We have implemented training and research assignments in Kenya, Tanzania, Uganda, South Sudan, Somalia, Malawi, Rwanda, Congo, and South Africa. Globally, we have supported our partners from the UK, Denmark, Italy, Sweden, Germany, and USA.