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The ECMWF Extended range forecasts: Introduction

Format: Interactive modules (eLearning)

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.

Analysis of September 2020 European heatwave using ERA5 climate reanalysis data from C3S

Analysis of September 2020 European heatwave using ERA5 climate reanalysis data from C3S

Format: Jupyter notebooks

Analyse a heatwave with ERA5 data from the C3S Climate Data Store. Compare temperatures with climatology. Plot figures.

Discover Anemoi: Introduction to Anemoi

Discover Anemoi: Introduction to Anemoi

Format: Videos
Get started with Anemoi, ECMWF's open-source framework for machine learning weather forecasting. In this introductory...
EarthKit: How to get data with EarthKit

EarthKit: How to get data with EarthKit

Format: Videos
Playlist: EarthKit
Part 3 of the EarthKit series. James Varnell explains how to access data using EarthKit.
EarthKit: How to install EarthKit

EarthKit: How to install EarthKit

Format: Videos
Playlist: EarthKit
Part 2 of the EarthKit series. James Varnell takes you through how to install EarthKit
EarthKit: Processing data with EarthKit

EarthKit: Processing data with EarthKit

Format: Videos
Playlist: EarthKit
Part 4 of the EarthKit series. James Varnell explains how EarthKit can be used to process data and plot charts
EarthKit: Visualising data with EarthKit

EarthKit: Visualising data with EarthKit

Format: Videos
Playlist: EarthKit
Part 5 of the EarthKit series. James Varnell explains how to use earthkit-plots to make visualisations
EarthKit: What is EarthKit?

EarthKit: What is EarthKit?

Format: Videos
Playlist: EarthKit
Part 1 of the EarthKit series. Introducting EarthKit, James Varnell takes you through what EarthKit is and what is can...

Ensemble Forecasting: Sources of forecast uncertainty (introduction)

Format: Interactive modules (eLearning)

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.