Tag - ensembles | ECMWF eLearning

The ECMWF sub-seasonal (extended range) forecasts: Introduction

Learn about sources of predictability, seasonal forecast skill and the ECMWF sub-seasonal forecasting system.

Level: Fundamentals

Certification course: No

What you'll learn:

An overview of ECMWF forecasts at medium, sub-seasonal and seasonal ranges
The basis of sub-seasonal forecasts
The ECMWF sub-seasonal ensemble forecasting system
Evaluation and performance of sub-seasonal forecasts

Full description:

Sub-seasonal (or extended range) forecasts provide outlooks up to 46 days. This lesson examines sources of predictability, seasonal forecast skill and the ECMWF sub-seasonal forecasting system.

Estimated duration: 40 minutes

Hide last updated info: No

Hide number of enrollments: No

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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: Forecasting

Ensemble Forecasting: Sources of forecast uncertainty (introduction)

Learn about sources of error in NWP, how they are quantified, and how ensembles are evaluated.

Level: Fundamentals

Certification course: No

What you'll learn:

Sources of forecast uncertainty
How ensembles quantify forecast uncertainty
How to extract information from the ensemble and evaluate performance
Communicating uncertainty

Full description:

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.

Estimated duration: 40 minutes

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: Yes

Click to enter this course

Category: Forecasting

Forecast Jumpiness: An introduction

Learn about the ways in which forecast jumpiness can appear and how it can be mitigated.

Level: Fundamentals

Certification course: No

What you'll learn:

What is forecast jumpiness?
Examples of jumpiness
Jumpiness in the context of ensembles
How dynamic sensitivities relate to jumpiness

Full description:

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.

Estimated duration: 30 minutes

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Hide number of enrollments: No

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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: Yes

Click to enter this course

Category: Forecasting

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

Learn how EFI, SOT and Model Climate are built and provide forecast guidance for extreme, or severe weather events.

Level: Fundamentals

Certification course: No

What you'll learn:

How the Extreme Forecast Index (EFI) and the Shift Of Tail (SOT) index and Model Climate (M-climate) are built, and how they relate to one another
Limitations of these methods
Case studies

Prerequisites:

Experience in meteorology and forecasting
Basic statistics (probability distributions)

Full description:

This lesson introduces the Extreme Forecast Index (EFI), which provides specialised guidance for identifying anomalous, extreme, or severe weather events. You will learn how the EFI, the Shift of Tails (SOT), and Model Climate are constructed, and how they relate to one another.

Estimated duration: 25 minutes

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: Yes

Machine learning: No

Numerical weather prediction: No

Click to enter this course

Category: Forecasting

Representing model uncertainty with stochastic physics

Explore sources of uncertainty in NWP and how this is represented in the IFS using stochastic physics.

Level: Others

Certification course: No

What you'll learn:

Sources of model uncertainty
How model uncertainty is represented in ensemble forecasts, and in particular in the IFS
How process-level model uncertainty is accounted for, including the Stochastically Perturbed Parameterisation Tendencies scheme

Hide last updated info: No

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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: Numerical Weather Prediction

Sources of Uncertainty

Learn about uncertainties and chaotic behaviour in NWP, why ensembles are needed and how they are used at ECMWF.

Level: Fundamentals

Certification course: No

What you'll learn:

Sources of forecast uncertainty and chaotic behaviour
Ensembles as a tool for capturing uncertainty and probabilistic prediction
ECMWF model setup, initial conditions

Estimated duration: 40 minutes

Hide last updated info: No

Hide number of enrollments: No

Hide full description label: No

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: Numerical Weather Prediction

ensembles

Courses tagged with "ensembles"