eLearning - online resources

The Extreme Forecast Index (EFI) and the Shift Of Tail (SOT) index

The EFI provides specialised forecast guidance for anomalous, extreme, or severe weather events. In this lesson you will learn how EFI, SOT and M-climate are built.

Ensemble Forecasting: Sources of forecast uncertainty (introduction)

Ensembles are run to account for uncertainties in initial conditions. This lesson explores the sources of error in NWP, how they are quantified, and how ensembles are evaluated.

The ECMWF Extended range forecasts: Introduction

Extended range forecasts provide outlooks up to 46 days. This lesson examines sources of predictability, seasonal forecast skill and the ECMWF extended range forecasting system.

Forecast Jumpiness: An introduction

There are times when consecutive forecasts can 'jump' significantly. This lesson will discuss the ways in which forecast jumpiness can appear and how it can be mitigated.

Cloud and precipitation parametrization 2: ice and mixed-phase microphysics

This lesson describes the key microphysical processes for ice and mixed-phase cloud and precipitation in the atmosphere, and the uncertainties in parametrization.

Numerical Weather Prediction: Parametrization of diabatic processes - Convection 1: Convection in the context of large-scale circulation

This lesson will take you through what convection is and the phenomena it causes.

Numerical Weather Prediction: Parametrization of diabatic processes - Convection 2: The mass-flux approach and the IFS scheme

This lesson looks at the three classes of parametrization schemes and the main characteristics of the IFS scheme.

Cloud and precipitation parametrization 1: overview and warm-phase microphysics

This lesson describes the key microphysical processes of cloud and precipitation parametrization with a focus on warm-phase processes and how these are used in NWP.

This lesson will take you through the basic concepts for the design of a cloud and precipitation microphysics parametrization

An introduction to Data Assimilation

Data assimilation is used in NWP to define ‘optimal' initial conditions for numerical forecasts. In this lesson you will define data assimilation and explore how it is used at ECMWF.

Sources of Uncertainty

All numerical weather prediction models have sources of uncertainty. In this lesson you will learn about these uncertainties and chaotic behaviour, why ensemble prediction is needed, and about ECMWF model set up.

Overview of satellite observations and their role in Numerical Weather Prediction (NWP)

This module will teach you about data sources, the role of satellite observations, satellite data measurements, assimilation, and monitoring of satellite observations.

Using stochastic physics to represent model uncertainty

All numerical weather prediction models have uncertainty. This lesson will explore why there is uncertainty and sources of it and how model uncertainty is represented in the IFS using stochastic physics.

Land surface: introduction to cold processes

Snow’s specific properties impact forecast ranges from a few days to seasonal and climate. In this lesson, you will learn about the role of snow at different time scales.

Numerical Weather Prediction: Parametrization of diabatic processes - case studies (convection)

This lesson contains four case studies which explore the conditions that cause and how to identify deep convection including predictability and forecast errors.

An introduction to single-column modelling

The SCM is a tool to investigate the physical processes of a global model in isolation. This lesson will introduce what a SCM is, its applications and limitations.

Using ECMWF computing facilities: the batch system

This lesson will focus on ECGATE - ECMWF's server dedicated to the users' work. You will learn how to run tasks in batch, submit, query and cancel jobs, correct common errors and check the accounting database.

MARS ECMWF's meteorological archive

The Meteorological Archival and Retrieval System (MARS) enables users to access ECMWF’s data. This lesson will teach you how to create a customised data retrieval.

Metview for the Single-Column Model (SCM)

This lesson will provide a quick overview of Metview's main features and enable you to use Metview to analyse and edit input data for the single-column model, run the model and visualise its output.

Exploring meteorological data through OGC web services

Web services are used to visualise geographical data. This lesson describes web services, data standards and outlines what OGC and INSPIRE are.

MARS – advanced retrievals, data manipulation and computations

The Meteorological Archival and Retrieval System (MARS) enables users to access ECMWF’s data. This lesson will look at MARS requests and explore its compute capability.

ecCodes: Decoding with GRIB tools

ecCodes is software developed by ECMWF to decode and encode in WMO GRIB and BUFR formats. This lesson will teach you how to inspect GRIB messages using GRIB tools.

Introduction to BUFR decoding with ecCodes

ecCodes is software developed by ECMWF to decode and encode in WMO GRIB and BUFR formats. This lesson will introduce you to the BUFR format for decoding of BUFR data.

Introduction to Metview

Metview is a powerful meteorological workstation application that enables you to access, process and visualise meteorological data. In this lesson you will learn how to use MetView.

A starter guide to ecFlow

ecFlow is a workflow package that enables users to run programmes behind a firewall. During this lesson you will learn ecFlow vocabulary and how to run a simple suite.

ecCodes – Manipulating GRIB data with tools and API

ecCodes is software developed by ECMWF to decode and encode in WMO GRIB and BUFR formats. This lesson focuses on handling GRIB data with ecCodes tools.

ECMWF compiling Environment - working on ECGATE and High Performance Computing Facility (HPCF)

In this lesson you will learn compiling and linking on ECGATE and HPCF, the basics of make and makefiles and simple debugging and optimisation.

MOOC MLWC - 1. ML in Weather & Climate

This course includes 6 modules and is as introduction to the main topics, from the processing of observations to data assimilation, forecasting and post-processing.

MOOC MLWC - 2. Concepts of Machine Learning

This course includes 5 modules and focuses on the key concepts of Machine Learning.

MOOC MLWC - 3. Practical ML applications in Weather & Climate

This course includes 6 hands-on modules on the main topics of numerical weather and climate prediction.

Climate Data Store and Toolbox

This lesson provides an introduction to the CDS and Toolbox. Users will learn how to search the CDS, how to download data and how the toolbox can be used.

Hands on Case Study

This lesson guides users through an adaptation case study of an (imaginary) olive farmer in Spain. The lesson contains clips that show how to use and adapt scripts in the toolbox.

Introduction to the CDS API

This lesson provides a hands-on introduction to downloading data using the CDS API in Python. The lesson provides a number of short tutorial videos.

Toolbox Advanced Applications

In this lesson you will get to know more functionalities available in the toolbox, you will learn to work with climate projections, and you will build applications.

Climate Data Discovery – Introduction

This lesson provides an introduction to the different sources of climate data and guides you to find the data you need.

Climate Data Discovery – Advanced

This lesson provides details on the various data sources, and strategies to find the data needed: Processing steps, choosing projections, scenarios, ensembles, variables etc. The lesson is a follow-up of “Climate Data Discovery – Introduction”.

Data Resources - Introduction

This lessen provides an overview of the various types of climate data resources, and teaches what Essential Climate Variables are. It will indicate the main advantages and disadvantages of the various data sources.

Data Resources - Observations

This lesson provides training on observations data. The different types of measurements are explained, the types of observing systems and the measurement uncertainty are explained.

Data Resources - Reanalyses

This lesson teaches users the basics of climate reanalysis. The lesson explains how reanalyses are made, an overview of global reanalyses datasets, and their strengths and limitations.

Data Resources - Climate Models

This lesson explains how climate models work and how the quality of climate models can be evaluated. Differences between climate projections, predictions and scenarios are explained.

Bias Correction and Downscaling

This lesson teaches about downscaling and bias correction methods. An exercise for bias correction is included.

Using climate models for climate scenarios

This lesson teaches how to use climate models in the development of national climate scenarios. Examples are provided for The Netherlands, Switzerland and the U.K.

Uncertainty, Robustness and Confidence

This lesson teaches about sources of uncertainty in climate projections, what robust signals are, and when we can be confident in a change.

Climate Projections

This ‘hands-on’ lesson covers climate projections, differences between climate models, and how to choose from climate projection data.

Develop your own climate services case study

The aim of this lesson is to provide an introduction into Climate Services, and a seven step approach to develop a case study, illustrated with a practical example.

Sectoral Application – Agriculture

This lesson covers how climate change impacts the agriculture sector. Responses of different crop types to climate change is explained. Adaptation measures are introduced and how CDS data can be used for this. Examples are given from the SIS Global Agriculture.

Sectoral Application – Energy

This lesson covers how climate change will affect the energy sector. An overview is provided of energy-related data and indicators available from the CDS, with an explanation of how these can be used in applications.

Sectoral Application - Heat Health

In this lesson you will learn how to use the Climate Data Store (CDS) for applications in the health sector. The lesson focuses on urban heat. The lessons shows data and indicators from SIS-European Health.

Sectoral Application - Heat Health – Advanced

This lesson provides further background information about urban heat and health, in the context of CDS applications in the health sector.

Sectoral Application – Tourism

This lesson provides an introduction to C3S applications in the tourism sector. It identifies C3S data and tools useful for tourism stakeholders in supporting climate change adaptation.