Tag - Numerical weather prediction

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

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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

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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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Atmospheric composition: No

Climate: No

Computing services and tools: No

Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: Yes

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Category: Forecasting

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

This lesson covers key processes in ice and mixed-phase clouds and precipitation, and parametrization uncertainties.

Level: Others

Certification course: No

What you'll learn:

An overview of key microphysical processes for ice and mixed-phase cloud and precipitation in the atmosphere.
How to recognise the important ice and mixed-phase microphysical processes that need to be parameterised in a numerical weather prediction model.
The complexities of ice and mixed-phase microphysical processes, and uncertainties in their parameterisation.

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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

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Category: Numerical Weather Prediction

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

Explore the key microphysical and warm-phase processes of cloud and precipitation parametrisation and their use in NWP.

Level: Others

Certification course: No

What you'll learn:

Basic concepts for the design of a cloud and precipitation microphysics parameterisation.
Key microphysical processes for warm phase cloud and precipitation in the atmosphere.
Which warm phase microphysical processes need to be parameterised in a numerical weather prediction model.

Estimated duration: 30 minutes

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Climate: No

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Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

Land surface: introduction to cold processes

Learn about the unique role of snow in forecasting, from short-range to seasonal time scales.

Level: Fundamentals

Certification course: No

What you'll learn:

The main physical properties of snow
The role of snow in the climate system at different time scales
Seasonal regimes in high latitude (cold) regions
How snow models are integrated into NWP and climate models

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Climate: No

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Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

Parametrisation of diabatic processes - case studies (convection)

Four case studies exploring the conditions that cause deep convection, considering predictability and forecast errors.

Level: Others

Certification course: No

What you'll learn:

The upper and lower-level flow conditions that favour deep convection
Translating weather maps in situations of deep convection
How to identify convective features and areas with potential predictability and forecast errors

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Climate: No

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Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

Introduction to the parametrization of sub-grid processes

Learn how sub-grid-scale processes (not explicitly simulated in NWP), are parameterised and how challenges are overcome.

Level: Fundamentals

Certification course: No

What you'll learn:

What parameterisation is and why it is needed
Methods and strategies for parameterisation
The role of parameterisation schemes in forecasting products
Examples of parameterisation, and sources of uncertainty

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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

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Category: Numerical Weather Prediction

An introduction to Data Assimilation

Learn about data assimilation and how it is used to define ‘optimal' initial conditions for NWP at ECMWF.

Level: Fundamentals

Certification course: No

What you'll learn:

What is data assimilation and how it fits into numerical weather prediction
Which sources of observations are used by ECMWF in its data assimilation process
How data assimilation methods work, including 4D- VAR
Recent improvements in data assimilation, and future challenges

Estimated duration: 40 minutes

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Atmospheric composition: No

Climate: No

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Data applications: No

Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

Satellite observations and their role in NWP

Learn about the role of satellite observations and measurements, and how these are assimilated and monitored for NWP.

Level: Fundamentals

Certification course: No

What you'll learn:

Different data sources and the role of satellite observations in NWP
Types of satellites, what they measure, and how measurements are taken
How satellite observations are monitored and assimilated

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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

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Category: Numerical Weather Prediction

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

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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

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Category: Numerical Weather Prediction

An introduction to single-column modelling

How SCM is used to investigate the physical processes of a global model in isolation, its applications and limitations.

Level: Others

Certification course: No

What you'll learn:

What single column models are and how they are applied
How to run a single column model, including data requirements
Practical examples of SCMs
Advantages and disadvantages of SCMs

Estimated duration: 30 minutes

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Climate: No

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Forecasting: No

Machine learning: No

Numerical weather prediction: No

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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

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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

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Category: Numerical Weather Prediction

Parametrisation of diabatic processes - Convection in the context of large-scale circulation

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

Level: Others

Certification course: No

What you'll learn:

The importance of convection in global energy and water balance.
Meteorological phenomena generated by convection.
About buoyancy and the parcel or plume method
About large scale effects of convection
About convectively coupled waves

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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

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Category: Numerical Weather Prediction

Parametrization of diabatic processes - 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.

Level: Others

Certification course: No

What you'll learn:

What parameterisation schemes for convection are, and the main aims.
The three main parameterisation schemes used for convection:
- Those based on moisture budgets
- Adjustment schemes
- Mass-flux schemes
The convection scheme used in ECMWF's Integrated Forecasting System (IFS)

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Atmospheric composition: No

Climate: No

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Forecasting: No

Machine learning: No

Numerical weather prediction: No

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Category: Numerical Weather Prediction

Introduction to Cloud Parametrisation

An introduction to the basic concepts for the design of a cloud and precipitation microphysics parametrisation.

Certification course: No

What you'll learn:

Aspects of a cloud and precipitation parameterisation
Processes affecting clouds
Components and examples of microphysics parameterisation schemes

Estimated duration: 15 minutes

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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

Numerical weather prediction

Courses tagged with "Numerical weather prediction"