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The aim of this session is to understand how we are able to provide forecasts at long time horizons given the chaotic nature of the atmosphere.

After this session you should be able to:

• describe the Lorenz idea of Predictability of the first and second kind
• list examples of the elements of the Earth system that provide predictability on longer timescales
• understand the type of forecast that we are able to provide beyond the deterministic limit

Increasing observation volumes and model complexity, decreasing errors, and a growing desire for uncertainty information, all necessitate developments in our diagnostic tools. The aim of these lectures is to discuss some of these tools, the dynamical insight behind them, and the residual
deficiencies that they are highlighting.

By the end of the lectures you should be aware of:

•   Some of the key weakness of the ECMWF forecast system 
•   Some of the diagnostic tools used to identify and understand these weaknesses

 The aim of this session is to introduce the idea of chaos.  We will discuss the implications this has for numerical weather prediction.

By the end of the session you should be able to:

• describe what limits the predictability of the atmosphere
• understand the need for probabilistic forecasting

Abstract: The lectures introduce methods of ensemble verification. They cover the verification of discrete forecasts (e.g. dry/wet) and continuous scalar forecasts (e.g. temperature). Various scores such as the Brier score and the continuous ranked probability score are introduced.

After the lectures you should be able to

• explain what a reliable probabilistic forecast is and how to measure reliability

• understand why resolution and sharpness of a probabilistic forecast matter

• compute several of the standard verification metrics used for ensemble forecasts

Increasing observation volumes and model complexity, decreasing errors, and a growing desire for uncertainty information, all necessitate developments in our diagnostic tools. The aim of these lectures is to discuss some of these tools, the dynamical insight behind them, and the residual
deficiencies that they are highlighting.

By the end of the lectures you should be aware of:

•   Some of the key weakness of the ECMWF forecast system 
•   Some of the diagnostic tools used to identify and understand these weaknesses

In this session the generation of the perturbed initial condition of the ECMWF ensemble will be presented. We will discuss the ratio behind using singular vectors in the ensemble and how they are calculated. Then it will be explained how the singular vectors are combined with perturbations from the ensemble of data assimilations to construct the perturbations for the ensemble.

By the end of the session you should be able to:

• explain the idea behind using singular vectors in the ensemble

• describe how singular vectors are calculated

• describe the construction of the ensemble perturbations

This lecture gives an overview of ensemble and post-processing and calibration techniques. The presentation is made from the medium-range forecast perspective. The (relative) benefits of calibration and multi-model combination for medium-range forecasting are also discussed.

  By the end of this lecture, you should be able to:

• describe a wide range of possible calibration methods for ensemble systems
• explain which methods are suitable in which circumstances
• discuss the merits of calibration and multi-model combination

The aim of this session is to provide a general overview of monthly forecasting at ECMWF. We will review the main sources of predictability for the sub-seasonal time scale, including the Madden Julian Oscillation, sudden stratospheric warmings (SSWs), land initial conditions and  their simulation by the coupled IFS-NEMO system. The skill of the ECMWF operational monthly forecasts
will also be discussed.

By the end of the session you should be able to: 

•   List the different sources of predictability for extended-range forecasts 
•   Describe the operational system used to produce the ECMWF monthly forecasts 
•   Assess the skill of the monthly forecasting system

The aim of this session is to introduce the ECMWF ensemble of data assimilation (EDA). The rationale and methodology of the EDA will be illustrated, and its use in to simulate initial uncertainties in the ECMWF ensemble prediction system (ENS) will be presented.

By the end of the session you should be able to:

• know what is the ECMWF EDA

• illustrate how the EDA is used to simulate initial uncertainty in ensemble prediction

• understand the main differences between singular vectors and EDA-based perturbations

You get the opportunity to experiment yourself with an ensemble prediction system for a chaotic low-dimensional dynamical system introduced by Edward Lorenz in 1995. Experiments permit to study the role of the initial condition perturbations and the representation of model uncertainties. Various metrics introduced in the ensemble verification lectures will be applied in this session.

After the practice session, you will be able to use the toy model as an educational tool.

During this session you will use metview to produce some standard ensemble visualisations such as:

• stamp maps
• spaghetti plumes
• cluster analysis

This will be used in a real world situation to decide whether to run a flight campaign or not.

During this session you will use metview to produce some standard ensemble visualisations such as:

• stamp maps
• spaghetti plumes
• cluster analysis

This will be used in a real world situation to decide whether to run a flight campaign or not.

You get the opportunity to experiment yourself with an ensemble prediction system for a chaotic low-dimensional dynamical system introduced by Edward Lorenz in 1995. Experiments permit to study the role of the initial condition perturbations and the representation of model uncertainties. Various metrics introduced in the ensemble verification lectures will be applied in this session.

After the practice session, you will be able to use the toy model as an educational tool.

This lecture covers the essentials of building a numerical seasonal forecast system, as exemplified by the present prediction system at ECMWF.

  By the end of this lecture, you should be able to:

• explain the scientific basis of seasonal forecast systems
• describe in outline ECMWF System 4 and its forecast performance
• discuss the critical factors in further improving forecast systems

The aim of this session is to understand the ECMWF clustering products.

By the end of the session you should be able to:

• explain how the cluster analysis works
• use the ECMWF clustering products

Abstract: The lecture is a short introduction to operational hydrological ensemble prediction systems, with focus on flooding. The European Flood Awareness System (EFAS) is described. The lecture also contains a short interactive exercise in decision making under uncertainty using prbabilistic forecasts as an example.

By the end of the session you should be able to:

• Describe the components in hydrological ensemble prediction systems (HEPS).

• Describe the major sources of uncertainty in HEPS and how they can be reduced.

• Explain the difficulties in using probabilistic flood forecasts in decision making.