Tag - Machine learning | ECMWF eLearning

Machine learning

Courses tagged with "Machine learning"

Machine Learning for Earth Systems Modelling

Foundations and New Frontiers

Online training course 1
From foundations to hand-on applications!

March 2026
Online, self-paced

Level: Fundamentals

Certification course: Yes

Full description:

Overview

Discover how machine learning is transforming extreme weather and climate simulations. Under the Destination Earth (DestinE) initiative, ECMWF is developing a new series of three online courses on Machine Learning for Earth Systems Modelling.

The courses offer a structured learning pathway, from foundations and context to advanced architectures and real-world applications, showing how machine learning (ML) complements physical modelling and supports next-generation digital twins of the Earth.

This first training course of the series introduces the foundations of machine learning in Earth system modelling, connecting ML concepts with physical modelling and real applications. Participants explore how AI is used within the DestinE initiative, including the digital twins and next-generation AI simulation systems. The course also addresses ethics, regulation, and future skills, bringing together expert perspectives to prepare learners for advanced ML applications.

Registration Start Date: 11 February, 2026
Registration End Date: April 2026
Course Start Date: March 2026
Course End Date: May 2026

Objectives of the training

After this course, you will be able to:

Objectives

Target audience

This course series targets researchers, practitioners, or technical specialists from meteorological services, climate centres, academic institutes, or industries.

Course 1 specifically targets a broad audience of high-level information users and policy/decision-makers from both European public and private sectors, academia, and industry. This course will also be of interest to a technical audience of non-ML meteorologists who want to be onboarded into the world of ML.

Prerequisites

Basic understanding of meteorology or climate systems.

Familiarity with statistics and data interpretation.

No prior ML or coding experience required.

Course structure and content

The course consists of a series of modules that combine a range of learning activities and applied examples to support a progressive understanding of ML in Earth System Sciences (ESS). The modules guide learners from core concepts to practical applications, illustrating how ML methods are developed, evaluated, and integrated into weather and climate workflows.

Learning materials include video lectures with visual explanations, interactive webinars, panel discussions featuring experts, and hands-on Python notebooks providing practical demonstrations using open climate datasets and models. Additional resources include curated readings and DestinE documentation, podcasts, auto-graded quizzes to check understanding, and forum discussions that encourage reflection and exchange after selected modules.

Modules

Course contributors

Logos

This course was created by ECMWF under the DestinE initiative and contracted to Wageningen University (WU) in collaboration with the Karlsruhe Institute of Technology (KIT) and Wageningen Research (WR). Scientific content is provided by European experts from ECMWF, academia, national meteorological services, and private industry (contributor details will be published in the following update).

Hide last updated info: Yes

Hide number of enrollments: Yes

Hide full description label: Yes

Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

MLWC MOOC 3: Applications of Machine Learning in Weather and Climate

Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.

Certification course: No

What you'll learn:

The application of ML methods to a range of problems in weather and climate
The details of practical code examples which can be used to apply to your own problems

Prerequisites:

Please complete MOOC MLWC - 2. Concepts of Machine Learning first, or ensure you are familiar with the topics covered there. Intermediate proficiency with Python, knowledge of statistics and experience in weather/climate is assumed.

Full description:

In this third tier of our MOOC on Machine Learning (ML) in Weather and Climate, we focus on the implementation of ML in weather and climate problems.

This six-module course gives code examples and explainers in topics including:

Satellite precipitation removal
Environmental modelling
Nowcasting
Data assimilation
Downscaling
Ocean modelling
Operational meteorology

This course is aimed at those with a technical/weather background and experience in Python. Ideally you will have already taken Tiers 1 and 2 of the MOOC on ML in Weather and Climate, or already be familiar with the concepts there.

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Estimated duration: 25 hours

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: Yes

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

MLWC MOOC 2: Concepts of Machine Learning in Weather and Climate

Five modules covering decision trees, deep learning, uncertainty and generative models, and physics-guided approaches.

Level: Fundamentals

Certification course: No

What you'll learn:

The main concepts of data retrieval using CliMetLab
Theory and application of regression and decision trees
Theory underlying deep neural networks, and software tools to apply them to practical problems
Uncertainty quantification using Bayesian and generative approaches
Physics-constrained approaches

By the end of this course, participants should have a sound understanding of the theory underpinning key ML approaches, and how these can be applied in practice.

Prerequisites:

Please complete MOOC MLWC - 1. ML in Weather & Climate first, or ensure you are familiar with the topics covered there. Basic proficiency with Python, knowledge of statistics and experience in weather/climate is assumed.

Full description:

In this second tier of our MOOC on Machine Learning (ML) in Weather and Climate, we go deeper into the theory underpinning ML, and the tools that are used to implement it, using Python case studies in weather and climate.

This five-module course covers regression trees and random forests, explores workflows in ML, and dives deeper into the ingredients of deep neural networks, including architectures, training, tuning and evaluating models. We will also explore approaches to quantify uncertainty, including generative ML models and Bayesian neural networks. The last module covers physics-guided ML approaches with applications to post-processing.

This course is aimed at those with a technical/weather background, some experience in Python, and a solid understanding of statistics. Ideally you will have already taken Tier 1 of this MOOC or already be familiar with the concepts there.

This course features a mixture of interactive modules, webinar recordings, quizzes, podcasts, and contains a number of detailed code examples.

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Estimated duration: 14 hours

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: Yes

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning

MLWC MOOC 1: Introduction to Machine Learning in Weather and Climate

Six modules introducing the main topics in machine learning in the context of weather and climate.

Level: Fundamentals

Certification course: No

What you'll learn:

An overview of machine learning in weather and climate
Unpack, at a conceptual level, key concepts and topics in ML
Applications and recent advances in the field

Prerequisites:

A basic knowledge of weather and climate, statistics and computing.

Full description:

In this first tier of our MOOC on Machine Learning (ML) in Weather and Climate, we give a broad overview of the key concepts ML and its applications in recent years to topics from forecasting and data assimilation to post-processing, observations and computing. This course is aimed as an introduction, which is accessible to those with an interest in ML, weather and climate, but without necessarily requiring a very technical background.

This course features a mixture of interactive modules, webinar recordings, quizzes, podcasts and code examples.

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Estimated duration: 14 hours

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: Yes

Computing services and tools: Yes

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Machine Learning