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Willem Deconinck

The aim of this session is to understand how numerical precision can be

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title	Advanced Numerical Methods

Multiexcerpt

MultiExcerptName	NMtime

Time

Monday

Tuesday

Wednesday

Thursday

Friday

9.15

Introductions

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title	Algorithms for semi-implicit integrations of nonhydrostatic PDEs of atmospheric dynamics (1)

The aim of this set of lectures is to systematically build theoretical foundations for Numerical Weather Prediction at nonhydrostatic resolutions. In the first part of the lecture, we will discuss a suite of all-scale nonhydrostatic PDEs, including the anelastic, the pseudo-incompressible and the fully compressible Euler equations of atmospheric dynamics. First we will introduce the three sets of nonhydrostatic governing equations written in a physically intuitive Cartesian vector form, in abstraction from the model geometry and the coordinate frame adopted. Then, we will combine the three sets into a single set recast in a form of the conservation laws consistent with the problem geometry and the unified solution procedure. In the second part of the lecture, we will build and document the common numerical algorithm for integrating the generalised set of the governing PDEs put forward in the first part of the lecture. Then, we will compare soundproof and compressible solutions and demonstrate the efficacy of this unified numerical framework for two idealised flow problems relevant to weather and climate.

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

explain the form, properties and role of alternative systems of nonhydrostatic PDEs for all scale atmospheric dynamics;
explain the importance and key aspects of continuous mappings employed in all-scale atmospheric models;
explain the difference between the explicit and semi-implicit algorithms for integrating nonhydrostatic PDEs, the importance of consistent numerical approximations, and the fundamental role of transport and elliptic solvers.

Piotr Smolarkiewicz

see first lecture for handout

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title	The semi-Lagrangian, semi-implicit technique of the ECMWF model

The aim of this session is to describe the numerical technique used in the ECMWF model for integrating the transport equations of the hydrostatic primitive equation set. We will present an overview of the semi-Lagrangian method and how it is combined with semi-implicit time-stepping to provide a stable and accurate formulation for the ECMWF Integrated Forecasting System (IFS).

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

describe the fundamental concepts of semi-Lagrangian advection schemes, their strengths and weaknesses

describe semi-implicit time-stepping and its use in IFS

explain the important role these two techniques play for the efficiency of the current IFS system

explain the impact that future super-computing architectures may have in the applicability of the semi-Lagrangian technique in high resolution non-hydrostatic global NWP systems.

Michail Diamantakis

SISL_2017.pptx

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title	Discontinuous higher order discretization methods

The aim of this session is to learn about recent developments in discontinuous higher order spatial discretization methods, such as the Discontinuous Galerkin method (DG), and the Spectral Difference method (SD). These methods are of interest because they can be used on unstructured meshes and facilitate optimal parallel efficiency. We will present an overview of higher order grid point methods for discretizing partial differential equations (PDE's) with compact stencil support, and illustrate a practical implementation.

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

ell what are the advantages offered by discontinuous higher order methods
describe how to solve PDE's with discontinuous methods
identify the key elements that contribute to a PDE solver

Willem Deconinck

Disc_HO_Methods.pdf

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title	Massively parallel computing for NWP and climate

The aim of this session is to understand the main issues and challenges in parallel computing, and how parallel computers are programmed today.

By the end of this session you should be able to

explain the difference between shared and distributed memory
describe the key architectural features of a supercomputer
describe the purpose of OpenMP and MPI on today’s supercomputers
identify the reasons for the use of accelerator technology

Andreas Müller

AMueller-ParallelComputing.pdf

10.

35

45

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title	Numerics + Discretization in NWP today

Using the 30-year history of ECMWF's Integrated Forecasting System (IFS) as an example, thelecture is an introduction to the development and current state-of-the-art of global numerical weather prediction (NWP), as well as to the challenges faced in the future. It is intended to provide

an overview and context for the topics covered in more detail during the course.

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

describe the development of global NWP, the current-state-of-the-art, and future challenges

identify relevant areas of research in numerical methods for Earth-System Modelling

put into context every subsequent lecture and its purpose

Nils Wedi

Lecture_1_wedi.pptx

Animation 1 (Plumb-McEwan laboratory experiment):

View file

name	QBOoriginal_short.gif.mp4
height	250

Animation 2 (DNS simulation of laboratory experiment):

Image Added

Animation 3 (equatorial stratosphere):

Image Added

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title	Algorithms for semi-implicit integrations of nonhydrostatic PDEs of atmospheric dynamics (2)

The aim of this set of lectures is to systematically build theoretical foundations for Numerical Weather Prediction at nonhydrostatic resolutions. In the first part of the lecture, we will discuss a suite of all-scale nonhydrostatic PDEs, including the anelastic, the pseudo-incompressible and the fully compressible Euler equations of atmospheric dynamics. First we will introduce the three sets of nonhydrostatic governing equations written in a physically intuitive Cartesian vector form, in abstraction from the model geometry and the coordinate frame adopted. Then, we will combine the three sets into a single set recast in a form of the conservation laws consistent with the problem geometry and the unified solution procedure. In the second part of the lecture, we will build and document the common numerical algorithm for integrating the generalised set of the governing PDEs put forward in the first part of the lecture. Then, we will compare soundproof and compressible solutions and demonstrate the efficacy of this unified numerical framework for two idealised flow problems relevant to weather and climate.

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

explain the form, properties and role of alternative systems of nonhydrostatic PDEs for all scale atmospheric dynamics;
explain the importance and key aspects of continuous mappings employed in all-scale atmospheric models;
explain the difference between the explicit and semi-implicit algorithms for integrating nonhydrostatic PDEs, the importance of consistent numerical approximations, and the fundamental role of transport and elliptic solvers.

Piotr Smolarkiewicz

see first lecture for handout

Practical Session

Willem Deconinck, Christian Kühnlein

Expand

title

Discontinuous higher order discretization methods

The aim of this session is to learn about recent developments in discontinuous higher order spatial discretization methods, such as the Discontinuous Galerkin method (DG), and the Spectral Difference method (SD). These methods are of interest because they can be used on unstructured meshes and facilitate optimal parallel efficiency. We will present an overview of higher order grid point methods for discretizing partial differential equations (PDE's) with compact stencil support, and illustrate a practical implementation.

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

ell what are the advantages offered by discontinuous higher order methods
describe how to solve PDE's with discontinuous methods
identify the key elements that contribute to a PDE solver

Expand

title	Reduced Precision Computing for Earth System Modelling

Operational and research activities at ECMWF now/in the future

In this lecture we will give you a brief history of ECMWF and present the main areas of NWP research that is currently being carried out in the centre. We then look at current research challenges and present some of the latest developments that will soon become operational.

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

List the main research areas at ECMWF and describe the latest model developments.

Sarah Keeley and Erland Källén

ECMWF-Past-FutureNM_2017fin.pptx

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title	Reduced Precision Computing for Earth System Modelling

The aim of this session is to understand how numerical precision can be traded against computational performance in Earth System modelling. It will be discussed how a reduction in numerical precision will influence model quality and how the minimal level of precision that will still allow simulations at high accuracy can be identified. We will give an overview about existing hardware options to adjust numerical precision to the need of the application.

By the end of this session you should be able to

describe how rounding errors will impact model simulations that show chaotic dynamics
describe the connection between numerical precision, computational performance and predictability
recall how a trade off between precision and performance can be realised in Earth System modelling today and in the future

Peter Düben

peter_dueben.pdf

11.

45

55

Expand

title	Introduction to semi-implicit integrations of nonhydrostatic PDEs of atmospheric dynamics

The aim of this set of lectures is to systematically build theoretical foundations for Numerical Weather Prediction at nonhydrostatic resolutions. In the first part of the lecture, we will discuss a suite of all-scale nonhydrostatic PDEs, including the anelastic, the pseudo-incompressible and the fully compressible Euler equations of atmospheric dynamics. First we will introduce the three sets of nonhydrostatic governing equations written in a physically intuitive Cartesian vector form, in abstraction from the model geometry and the coordinate frame adopted. Then, we will combine the three sets into a single set recast in a form of the conservation laws consistent with the problem geometry and the unified solution procedure. In the second part of the lecture, we will build and document the common numerical algorithm for integrating the generalised set of the governing PDEs put forward in the first part of the lecture. Then, we will compare soundproof and compressible solutions and demonstrate the efficacy of this unified numerical framework for two idealised flow problems relevant to weather and climate.

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

explain the form, properties and role of alternative systems of nonhydrostatic PDEs for all scale atmospheric dynamics;
explain the importance and key aspects of continuous mappings employed in all-scale atmospheric models;
explain the difference between the explicit and semi-implicit algorithms for integrating nonhydrostatic PDEs, the importance of consistent numerical approximations, and the fundamental role of transport and elliptic solvers.

Piotr Smolarkiewicz

Course2017_smolar.pptx

Practical Session (elliptic solvers)

Andreas Müller, Willem Deconinck, Christian Kühnlein

Tuesday-Exercises-Handout.pdf

Practical Session

Willem Deconinck, Christian Kühnlein

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title

Operational and research activities at ECMWF now/in the futureIn this lecture we will give you a brief history of ECMWF and present the main areas of NWP research that is currently being carried out in the centre. We then look at current research challenges and present some of the latest developments that will soon become operational

Discontinuous higher order discretization methods

The aim of this session is to learn about recent developments in discontinuous higher order spatial discretization methods, such as the Discontinuous Galerkin method (DG), and the Spectral Difference method (SD). These methods are of interest because they can be used on unstructured meshes and facilitate optimal parallel efficiency. We will present an overview of higher order grid point methods for discretizing partial differential equations (PDE's) with compact stencil support, and illustrate a practical implementation.

By the end of the

lecture

session you should be able to:

List the main research areas at ECMWF and describe the latest model developments.

ell what are the advantages offered by discontinuous higher order methods
describe how to solve PDE's with discontinuous methods
identify the key elements that contribute to a PDE solver

Willem Deconinck

See first lecture for handout

Sarah Keeley and Erland Källén

Course wrap up and Certificates

14.00

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title	The spectral transform method

The success of the spectral transform method in global NWP in comparison to alternative methods has been overwhelming, with many operational forecast centres (including ECMWF) having madethe spectral transform their method of choice. The lecture will introduce the basic elements of the spectral transform, explain why it has been successful and describe recent developments such as

the fast Legendre transform.

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

explain what the spectral transform method is, how it is applied, and describe the latest developments at ECMWF.

give reasons why it is successful for global NWP and climate.

identify potential disadvantages of the method.

Nils Wedi

Lecture_2_wedi.pptx

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title	Eulerian time-stepping schemes for NWP and climate

The aim of this session is to describe Eulerian based numerical techniques for integrating the equation sets encountered in NWP models. We will present an overview of different time-stepping techniques and discuss the advantages and disadvantages of each approach.

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

obtain a good understanding of the minimum theoretical properties required by time-stepping schemes
describe differences, strengths-weaknesses of different time-stepping approaches such as split-explicit time-stepping, Runge-Kutta time-stepping

describe the basic features of different time-stepping schemes used in other weather forecasting models such as WRF, ICON

Michail Diamantakis

tstepping2017.pptx

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title	Hydrostatic/Non-hydrostatic dynamics, resolved/permitted convection and interfacing to physical parameterizations

During this presentation, we will discuss two of the questions faced by numerical weather prediction scientists as forecast models reach horizontal resolutions of 6 to 2 km:

Do we need to abandon the primitive equations for a non-hydrostatic system of equations?
Do we still need a deep convection parametrisation?
and we will show what answers to these questions are given by very high resolution simulations of the IFS.

By the end of the presentation, you should be able to:

discuss the limits of the hydrostatic approximation for numerical weather prediction
explain the dilemma of parametrizing deep convection versus permitting explicit deep convection at resolution in the grey zone of convection

Sylvie Malardel

PDC_grey.pdf

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title	Introduction to element based computing, finite volume and finite element methods

The aim of two lectures is to introduce basis of finite volume and continuous finite element discretisations and relate them to corresponding data structures and mesh generation techniques. The main focus will be on unstructured meshes and their application to global and local atmospheric models. Flexibility, communication overheads, memory requirements and user friendliness of such meshes with be contrasted with those of structured meshes. The most commonly used mesh generation techniques will be highlighted, together with mesh manipulation techniques employed in mesh adaption approaches and will be followed by a discussion of alternative geometrical representations of orography. An example of unstructured meshes’ implementation to non-hydrostatic and hydrostatic atmospheric solvers will provide an illustration of their potential and challenges.

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

understand applicability, advantages and disadvantages of selected mesh generation techniques for a given type of application.
appreciate importance of data structures in relation to atmospheric models and mesh generation.
gain awareness of issues related to flexible mesh generation and adaption.

Joanna Szmelter

JoannaSzmelter2017.ppt

15.30

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title	Vertical discretisation

The goal of this session is to provide an overview of the use of generalised curvilinear coordinates in atmospheric numerical models.

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

describe some important aspects of the formulation and implementation of the governing equations in generalised coordinates
describe various vertical coordinates employed in atmospheric models
indicate the use of generalised coordinates to employ moving mesh adaptivity

Christian Kühnlein

hristian

kuehnlein_EC_TC2017.pdf

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title	Mesh adaptivity using continuous mappings

The goal of this session is to provide an overview of the use of generalised curvilinear coordinates in atmospheric numerical models.

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

describe some important aspects of the formulation and implementation of the governing equations in generalised coordinates
describe various vertical coordinates employed in atmospheric models
indicate the use of generalised coordinates to employ moving mesh adaptivity

C

Christian Kühnlein

See first lecture for handout

Expand

title	Hydrostatic/Non-hydrostatic dynamics, resolved/permitted convection and interfacing to physical parameterizations

During this presentation, we will discuss two of the questions faced by numerical weather prediction scientists as forecast models reach horizontal resolutions of 6 to 2 km:

Do we need to abandon the primitive equations for a non-hydrostatic system of equations?
Do we still need a deep convection parametrisation?
and we will show what answers to these questions are given by very high resolution simulations of the IFS.

By the end of the presentation, you should be able to:

discuss the limits of the hydrostatic approximation for numerical weather prediction
explain the dilemma of parametrizing deep convection versus permitting explicit deep convection at resolution in the grey zone of convection

Sylvie Malardel

resolution.pdf

Expand

title	Mesh generation

The aim of two lectures is to introduce basis of finite volume and continuous finite element discretisations and relate them to corresponding data structures and mesh generation techniques. The main focus will be on unstructured meshes and their application to global and local atmospheric models. Flexibility, communication overheads, memory requirements and user friendliness of such meshes with be contrasted with those of structured meshes. The most commonly used mesh generation techniques will be highlighted, together with mesh manipulation techniques employed in mesh adaption approaches and will be followed by a discussion of alternative geometrical representations of orography. An example of unstructured meshes’ implementation to non-hydrostatic and hydrostatic atmospheric solvers will provide an illustration of their potential and challenges.

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

understand applicability, advantages and disadvantages of selected mesh generation techniques for a given type of application.
appreciate importance of data structures in relation to atmospheric models and mesh generation.
gain awareness of issues related to flexible mesh generation and adaption.

Joanna Szmelter

See first lecture for handout

Expand

title	Parametrization of sub-grid scale processes

Multiexcerpt

MultiExcerptName	PAtime

Time

Monday

Tuesday

Wednesday

Thursday

Friday

9.15

Introduction to the course

Erland Källén / Students

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title	Clouds (2)

This session describes the representation of subgrid-scale variability of humidity, cloud and precipitation and how this can be parametrized in atmospheric models.

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

• recognise the reasons for representing the subgrid variability of humidity and cloud in an atmospheric model

• explain how the key quantity of cloud fraction is related to subgrid heterogeneity assumptions

• describe the different types of subgrid cloud parametrization schemes.

Richard Forbes

TC2017_Forbes_L2_cloud_coldphase.pptx

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title	Land Surface (2):

Snow

This session will have two

mains

main components:

An overview of the role of snow in the climate system from observations, models and forecasts

.Description

; with a description of the current representation of snow in the ECMWF model.
An overview of the role of vegetation in NWP with a description of the evolution of vegetation representation in the ECMWF model, supported by some evaluation examples

and ongoing developments

.

By the end of the session, the students should be able:

Identify the main processes associated with snow and vegetation in

the climate system

NWP
Describe the main components

of the

related to snow and vegetation scheme in the ECMWF land surface model

Souhail Boussetta

TC2017_PA_Surf_partII.pptx

Expand

title	Land Surface (3): Surface Energy, Water Cycle

By the end of the session, the students should be able:

relate flux and storage
recognise land surface predictors and land diagnostic quantities

Gianpaolo Balsamo

new_surf2.pptx

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title	Parametrization and Data Assimilation

This three-hour lecture will start by explaining the role and main ingredients of data assimilation in general. The widely used framework of variational data assimilation will then be gradually introduced. The challenges associated with the necessary inclusion of physical parametrizations in the data assimilation process will be highlighted. The concept of adjoint model as well as the techniques to derive it will be introduced. The importance of the linearity constraint in 4D-Var and the methods to address it will be detailed. The set of linearized physical parametrizations used at ECMWF will then be briefly presented. Finally, various examples of the use of physical parametrizations in variational data assimilation and its impact on weather forecast quality will be given.

By the end of the session, the students should be able:

• to name the main ingredients of a data assimilation system.

• to tell why physical parametrizations are needed in data assimilation.

• to identify the role of the adjoint code in 4D-Var.

• to recognize the importance of the regularization of the linearized code.

Philippe Lopez

see first lecture for handouts

10.45

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title	Radiation (1)

This module aims to introduce the fundamentals of radiative transfer theory and its role within the global atmospheric circulation. The lectures will also cover the techniques of numerical modelling of the radiative transfer equations in global-circulation models with a particular focus on the code in use in the ECMWF Integrated Forecasting System.

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

• Identify the key processes controlling the atmospheric radiative balance

• Recognize the role of the radiative transfer in the Earth energy balance

• Estimate the impact of changes in the radiative parameterizations on climate

Additional outcomes:

• Develop skills in data analysis and numerical modelling

Robin Hogan
hogan_ecmwf_radiation_lecture1.pptx

Expand

title	Convection (1)

Convection affects all atmospheric scales. Therefore, the convection session aims to provide a deeper understanding of the atmospheric general circulation and its interaction with convective heating and vertical transports. The notions and techniques acquired during the course should be useful for developers of convective parametrizations, forecasters and for analysing ouput from high-resolution convection resolving models.

By the end of the session you should become familiarised with

• the interaction between the large-scale circulation and the convection including radiative-convective equilibrium and convectively-coupled large-scale waves

• the notion of convective adjustment and the mass flux concept in particular

• the basic concepts behind the ECMWF convection parametrization and some useful numerical tricks

• forecasting convection including convective systems and the diurnal cycle

• diagnose forecast errors related to convection.

Peter Bechtold

CONVECTION_T1_2017.ppt

Expand

title	Radiation (3)

This module aims to introduce the fundamentals of radiative transfer theory and its role within the global atmospheric circulation. The lectures will also cover the techniques of numerical modelling of the radiative transfer equations in global-circulation models with a particular focus on the code in use in the ECMWF Integrated Forecasting System.

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

• Identify the key processes controlling the atmospheric radiative balance

• Recognize the role of the radiative transfer in the Earth energy balance

• Estimate the impact of changes in the radiative parameterizations on climate

Additional outcomes:

• Develop skills in data analysis and numerical modelling

Robin Hogan

Alessio Bozzo

AB_TC_Radiation_Lecture3a.pptx

Expand

title	Convection (3)

Convection affects all atmospheric scales. Therefore, the convection session aims to provide a deeper understanding of the atmospheric general circulation and its interaction with convective heating and vertical transports. The notions and techniques acquired during the course should be useful for developers of convective parametrizations, forecasters and for analysing ouput from high-resolution convection resolving models.

By the end of the session you should become familiarised with

• the interaction between the large-scale circulation and the convection including radiative-convective equilibrium and convectively-coupled large-scale waves

• the notion of convective adjustment and the mass flux concept in particular

• the basic concepts behind the ECMWF convection parametrization and some useful numerical tricks

• forecasting convection including convective systems and the diurnal cycle

• diagnose forecast errors related to convection.

Peter Bechtold
CONVECTION_T3_2017.ppt

Expand

title	Numerics of Parameterization

This short lecture is an introduction to the questions of time splitting and process splitting in a numerical weather prediction model and to the problems resulting from the interaction of different numerical solvers inside the same model.

After this introduction, you should

• be fully aware that each parametrisation is only a small part of a much larger system, usually one term in the full system of equations which needs to be solved by the forecast model,

• remember, when working on your own parametrisation(s), that parametrisations are also subject to the constraints imposed by numerical analysis and algorithmic, as is the solver in the dynamical core.

Sylvie Malardel

Alessio Bozzo

PDC_2017.pdf

11.55

Expand

title	Boundary Layer (1)

This session gives a theoretical introduction of the planetary boundary layer, including its definition, classification, notions about turbulence within the boundary layer, differences between clear and cloudy boundary layers, and equations used to describe the mean state in a numerical model.

Expected outcomes:

• understand what is the boundary layer, its characteristics and why it is important to study it and represent it correctly in numerical models

• understand the difference between the various boundary layer types

Irina Sandu
pbl1_is_2017.ppt

Expand

title	Radiation (2)

This module aims to introduce the fundamentals of radiative transfer theory and its role within the global atmospheric circulation. The lectures will also cover the techniques of numerical modelling of the radiative transfer equations in global-circulation models with a particular focus on the code in use in the ECMWF Integrated Forecasting System.

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

• Identify the key processes controlling the atmospheric radiative balance

• Recognize the role of the radiative transfer in the Earth energy balance

• Estimate the impact of changes in the radiative parameterizations on climate

Additional outcomes:

• Develop skills in data analysis and numerical modelling

Robin Hogan

hogan_ecmwf_radiation_lecture2.pptx

Expand

title	Convection (2)

Convection affects all atmospheric scales. Therefore, the convection session aims to provide a deeper understanding of the atmospheric general circulation and its interaction with convective heating and vertical transports. The notions and techniques acquired during the course should be useful for developers of convective parametrizations, forecasters and for analysing ouput from high-resolution convection resolving models.

By the end of the session you should become familiarised with

• the interaction between the large-scale circulation and the convection including radiative-convective equilibrium and convectively-coupled large-scale waves

• the notion of convective adjustment and the mass flux concept in particular

• the basic concepts behind the ECMWF convection parametrization and some useful numerical tricks

• forecasting convection including convective systems and the diurnal cycle

• diagnose forecast errors related to convection.

Peter Bechtold
CONVECTION_T2_2017.ppt

Expand

title	Clouds (3)

Building on the previous two Cloud sessions, the practical implementation of a cloud parametrization is described, using the ECMWF global model as an example appropriate for global weather forecasting.

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

• explain the key sources and sinks of cloud and precipitation required in a parametrization

• describe the main components of the ECMWF stratiform cloud parametrization

• recognise the limitations of approximating complex processes.

Richard Forbes

TC2017_Forbes_L3_cloud_subgrid.pptx

Expand

title	Model Evaluation: Clouds and Boundary Layer

This session will give an overview of techniques and data sources used for the verification of the boundary layer scheme. We will use examples from the IFS to explore how verification methods can help to identify systematic errors in the model's boundary layer parameterization, and guide future model development.

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

• Identify data sources and products suitable for BL verification

• Recognize the strengths and limitations of the verification strategies discussed

• Choose a suitable verification method to investigate model errors in boundary layer height, transport and cloudiness.

Maike Ahlgrimm

CldPblVeri2017.ppt

14.00

Expand

title	Clouds (1)

This session gives a brief overview of cloud parametrization issues and an understanding of the basic microphysics of liquid, ice and mixed phase cloud and precipitation processes.

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

• recall the basic concepts for the design of a cloud parametrization

• describe the key microphysical processes in the atmosphere

• recognize the important microphysical processes that need to be parametrized in a global NWP model.

Richard Forbes

TC2017_Forbes_L1_cloud_warmphase.pptx

Expand

title	Boundary Layer (2)

This session focuses on representation of the surface layer, i.e. the layer between the surface and the first model level. More particularly, it explains how the surface fluxes are parametrized, and it gives insights on the representation of the surfaces roughness lengths which are one of the crucial aspects of the formulation of the surface fluxes.

Expected outcomes:

• be aware of the difficulties related to the representation of the surface layer in a numerical model

• understand how the surface fluxes are parametrized

Irina Sandu

pbl2_is_2017.pptx

Expand

title	Boundary Layer (3)

This session explains the different approaches used in numerical models to parametrize the turbulent mixing taking place at the subgrid scale, above the surface layer. Various turbulence closures are presented before describing closure currently used in the ECMWF model.

Expected outcomes:

• understand what a turbulence closure is and what are the types of closures encountered in numerical models

• have an overview of the parameterization of turbulent mixing in the ECMWF model

Irina Sandu

pbl3_is_2017.ppt

Expand

title	Parametrization and Data Assimilation

This three-hour lecture will start by explaining the role and main ingredients of data assimilation in general. The widely used framework of variational data assimilation will then be gradually introduced. The challenges associated with the necessary inclusion of physical parametrizations in the data assimilation process will be highlighted. The concept of adjoint model as well as the techniques to derive it will be introduced. The importance of the linearity constraint in 4D-Var and the methods to address it will be detailed. The set of linearized physical parametrizations used at ECMWF will then be briefly presented. Finally, various examples of the use of physical parametrizations in variational data assimilation and its impact on weather forecast quality will be given.

By the end of the session, the students should be able:

• to name the main ingredients of a data assimilation system.

• to tell why physical parametrizations are needed in data assimilation.

• to identify the role of the adjoint code in 4D-Var.

• to recognize the importance of the regularization of the linearized code.

Philippe Lopez

TC_PA_lopez_2017_main.ppt
TC_PA_lopez_2017_ex.ppt

Expand

title	Parameterization of Sub-grid Orography

On the basis of simple gravity wave theory, the concepts of sub-grib turbulent form drag, flow blocking, and gravity wave excitation will be introduced. The ECMWF formulations will be described, and the impact will be discussed.

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

• Describe the relevant physical mechanisms related to sub-grid orography that have impact on flow in the atmosphere.

• Describe the impact of sub-grid orography.

Anton Beljaars

subgrid_orography_2017.ppt

15.30

Expand

title	Land Surface (1): Introduction

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

recognise land elements relevant to weather,
review land modelling strategies to heterogeneity

Gianpaolo Balsamo

new_surf1.pptx

Introduction to the Single Column Model

Filip Vana

SCM_intro.pdf

Introduction to Metview and SCM interface

Iain Russell

2016-03-21-Metview-SCM-Overview.pptx

Radiation exercises

Alessio Bozzo and Robin Hogan

Land Surface exercises

Gianpaolo Balsamo and Souhail Boussetta

Boundary Layer & Cloud exercises

Irina Sandu, Maike Ahlgrimm and Richard Forbes

Moist Processes Exercises

Richard Forbes and Peter Bechtold

16.40

Moist Processes Games

Richard Forbes and Peter Bechtold

Radiation exercises

Alessio Bozzo and Robin Hogan

Land Surface exercises

Gianpaolo Balsamo and Souhail Boussetta

Boundary Layer & Cloud exercises

Irina Sandu, Maike Ahlgrimm and Richard Forbes

Course wrap up and certificates

Sebastien Massart

Expand

title	Data Assimilation

Multiexcerpt

MultiExcerptName	DAtime

Time

Monday

Tuesday

Wednesday

Thursday

Friday

9.15

Expand

title	Introduction. Operational and research activities at ECMWF now/in the future

In this lecture we will give you a brief history of ECMWF and present the main areas of NWP research that is currently being carried out in the centre. We then look at current research challenges and present some of the latest developments that will soon become operational.

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

List the main research areas at ECMWF and describe the latest model developments.

Erland Källén, Sarah Keeley

Expand

title	Assimilation Algorithms: (2) 3D-Var

This lecture will present the 3D-Var assimilation algorithm. This algorithm is based in the formulation of a cost function to minimize. Minimization methods will be presented together with some information on how to improve their efficiency.

By the end of the lecture the participants should be able to:

Recognize the 3D-Var cost function
Explain the various terms of the cost function
Question the efficiency of methods designed to find the mimimum of the cost function

Sebastien Massart

TC_lecture_2.pdf

Expand

title	Reanalysis

The aim of this session is to understand how data assimilation can improve our knowledge of past weather over long time-scales. We will present recent advances that help capture changes over time in observing system networks, and project this variation in information content into uncertainty estimates of the reanalysis products. We will also discuss the applications of reanalysis, which generally put weather events into the climate context.

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

explain what are the goals of data assimilation in a reanalysis data assimilation system
list the key aspects that require particular attention in reanalysis, as compared to numerical weather prediction
describe the most common problems in reanalysis products

Patrick Laloyaux
Laloyaux_Reanalysis_2017.pptx

Expand

title	Bias Correction

In this lecture the variational bias correction scheme (VarBC) as used at ECMWF is explained. VarBC replaced the tedious job of estimating observation bias off-line for each satellite instrument or in-situ network by an automatic self-adaptive system. This is achieved by making the bias estimation an integral part of the ECMWF variational data assimilation system, where now both the initial model state and observation bias estimates are updated simultaneously.

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

many observations are biased, and that the characteristics of bias varies widely between types of instruments
separation between model bias and observation bias is often difficult
the success of an adaptive system implicitly relies on a redundancy in the underlying observing system.

Niels Bormann

expand

Bormann_2017_TC_BiasCorrection.pptx

Expand

title	Land Data

Analysis System - screen level parameters and snow

Assimilation

The aim of these sessions is to understand the role of land surface data assimilation on medium range weather forecasts.

We will give an overview of the different approaches used to assimilate land surface data and to initialise model variables in NWP. We will present the current observing systems and describe the land data assimilation structure within ECMWF system.

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

identify the different observations used for snow and soil moisture data assimilation
define land surface data assimilation approaches used for NWP
describe the role of land surface data assimilation on medium-range weather forecasts

Patricia de Rosnay

deRosnay_TC_NWP_DA_2017.pdf

10.45

Expand

title

Land Data Assimilation - Soil moisture

Overview of Assimilation Methods

The

aim of these sessions is to understand the role of land surface data assimilation on medium range weather forecasts.

goal of the ECMWF Earth System data assimilation is to provide an accurate and physically coherent description of the state of the atmosphere, ocean, sea ice and land surface as an initial point for our forecasts.

This requires blending in a statistically optimal way information from a huge variety of observations and our prior knowledge about the physical laws of the Earth system, which is encapsulated in our models.

In this lecture we will lay the general conceptual framework on how to achieve this from a Bayesian perspective. We will then highlight the approximations and hypotheses which are required to make the assimilation problem computationally tractable and which underlie the practical data assimilation algorithms which will be described in detail in this training course

We will give an overview of the different approaches used to assimilate land surface data and to initialise model variables in NWP. We will present the current observing systems and describe the land data assimilation structure within ECMWF system

.

By the end of

the session

Expand

title	Overview of Assimilation Methods

Massimo Bonavita

lecture you should be able to:

identify the different observations used for snow and soil moisture data assimilation
define land surface data assimilation approaches used for NWP
describe the role of land surface data assimilation on medium-range weather forecasts

Patricia de Rosnay

10.35

understand the basics of how a geophysical data assimilation system works;
understand the main approximations and hypotheses which are required to build practical data assimilation algorithms for large geophysical systems

Massimo Bonavita

DataAssim_Overview_Bonavita_2017_1.pptx

Expand

title	Assimilation Algorithms: (3) 4D-Var

Sebastien Massart

TC_lecture_3.pdf

Expand

title	Data Assimilation Diagnostics: Forecast Sensitivity

Cristina Lupu

FSOI_DALecture_CLupu.pptx

Expand

title	Quality Control of observations

A single observation can under some conditions undermine the quality of a global analyses. The lecture will go through methods used to make the analysis more robust against oulier or wrong observations, with focus on variational quality control.

Elias Holm

Holm_VarQC_lecture.pdf

Expand

title	Tangent Linear and Adjoints

The goal of this lecture is to familiarise the student with the notion of tangent linear and adjoint models, and their use in variational data assimilation. A general overview of the current use of tangent linear and adjoint models in the ECMWF system will also be provided. Theoretical definitions and practical examples of tangent liner and adjoint models will be given. The student will be invited to work some simple tangent linear and adjoint derivations together with the instructor. A brief introduction to automatic differentiation software will also be given./

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

define what tangent linear and adjoint models are
derive tangent linear and adjoint equations for a simple nonlinear equation
describe the use of tangent linear and adjoint codes within the ECMWF's 4D-VAR system.

Angela Benedetti

11.45

Training_course_2017_TLAD.pptx

11.55

Expand

title	Conventional and actively sensed observations

The aim of this lecture is to

By the end of the lecture the participants should be able to:

Lars Isaksen

Expand

title	Assimilation Algorithms: (4) Ensemble Kalman filters

The aim of this lecture is to introduce the concept of the EnKF in the context of atmospheric data assimilation. Strengths and weaknesses of the algorithm will be discussed and results of the ECMWF implementation will be presented

This lecture will introduce how observations are an essential part of the data assimilation system.

It will focus on in situ (also called conventional) observations, from surface stations, drifters, aircraft and radiosondes. They are important both for direct use in the data assimilation system and for diagnostics. Radiosonde and surface observations also help to control the biases in the assimilation system. However they are diverse and hey can be complex, so close attention to quality control, observation uncertainty and (in some cases) bias correction is needed to optimise their use.

The lecture will also introduce the actively sensed satellite observations used for data assimilation at ECMWF: radio occultation data, scatterometer winds, and altimeter wind/significant wave height.

By the end of the lecture the

participants

student should be able to:

• Describe the basic EnKF algorithm and its connections with the Kalman Filter;

• Discuss some of the advantages and the limitations of EnKF algorithms with respect to more established variational algorithms;

• Be aware of recent developments in hybrid variational-EnKF data assimilation

Massimo Bonavita

Expand

title	Parameterization and Data Assimilation

This one-hour lecture will identify the challenges associated with the use of physical parametrizations in the context of four-dimensional variational data assimilation (4D-Var). The importance of the linearity constraint in 4D-Var and the methods to address it will be detailed. The set of linearized physical parametrizations used at ECMWF will be briefly presented. Examples of the use of physical parametrizations in variational data assimilation and its impact on forecast quality will be given.

understand how in situ and actively sensed observations are used in data assimilation, including bias aspects and observation uncertainty aspects.

appreciate the diverse and complex range of in situ observations used in modern NWP.
understand how radio occultation data, scatterometer winds and altimeter data are used in data assimilation.

Lars Isaksen

LI_DA_TC_2017_Insitu_actively_sensed_Observations.pptx

Expand

title	Assimilation Algorithms: (4) Ensemble Kalman filters

The aim of this lecture is to introduce the concept of the EnKF in the context of atmospheric data assimilation. Strengths and weaknesses of the algorithm will be discussed and results of the ECMWF implementation will be presented.

By the end of the lecture

,

the

students

participants should be able

:

to tell why physical parametrizations are needed in data assimilation.
to recognize the importance of the regularization of the linearized code

to:

• Describe the basic EnKF algorithm and its connections with the Kalman Filter;

• Discuss some of the advantages and the limitations of EnKF algorithms with respect to more established variational algorithms;

• Be aware of recent developments in hybrid variational-EnKF data assimilation

Massimo Bonavita

Bonavita_ENKF_TC2017.pptx

Expand

title	Parameterization and Data Assimilation

This one-hour lecture will identify the challenges associated with the use of physical parametrizations in the context of four-dimensional variational data assimilation (4D-Var). The importance of the linearity constraint in 4D-Var and the methods to address it will be detailed. The set of linearized physical parametrizations used at ECMWF will be briefly presented. Examples of the use of physical parametrizations in variational data assimilation and its impact on forecast quality will be given

Philippe Lopez

Expand

title	Model error

In this lecture, the impact of model error on variational data assimilation will be presented. This lecture will introduce weak-constraint 4D-Var as a way to account for model error in the data assimilation process. Several examples of results from simplified implementations in the IFS will be shown

.

By the end of the lecture, the

participants

students should be able

to

:

describe the impact of model error on the data assimilation process,
explain the difficulties in properly accounting for model error in data assimilation.

Patrick Laloyaux

to tell why physical parametrizations are needed in data assimilation.
to recognize the importance of the regularization of the linearized code

Philippe Lopez

TC_DA_lopez_2017_main.ppt

Practical Session: Tangent Linear and Adjoints14.00

Expand

title

Analysis of Radiance Observations

The primary purpose of this lecture is explore the implications of the fact that satellites can only measure radiation at the top of the atmosphere and do not measure the geophysical variables we require for NWP (e.g. temperature, humidity and wind). The link between the atmospheric variables and the measured radiances is the radiative transfer equation - the key elements of which are discussed. It is shown how - with careful frequency selection - satellite measurements can be made for which the relationship to geophysical variables is greatly simplified. Despite these simplifications, it is shown that the extraction of detailed profile information from downward looking radiance measurements is a formally ill posed inverse problem.

Data assimilation is introduced as the solution to this inverse problem, where background information and satellite observations are combined to produce a best or optimal estimate of the atmospheric state. The main elements of the assimilation scheme (such as the chain of observation operators for radiances) and its key statistical inputs are examined. In particular it is shown that incorrect specification of observation errors (R) and background errors (B) can severely limit the successful exploitation of satellite data.

By the end of this lecture you will:

understand exactly what a satellite actually measures (radiance)
appreciate the complex relationship between what is measured and what we wish to know for NWP
how information is extracted from satellite measurements in data assimilation

Tony McNally

Expand

title	Assimilation Algorithms: (5) Hybrid Data Assimilation methods

The aim of this lecture is to

By the end of the lecture the participants should be able to:

Massimo Bonavita

Practical Session with OOPS

Marcin Chrust

Sebastien Massart

Patrick Laloyaux

Expand

title	Data Assimilation of Atmospheric Composition

At ECMWF atmospheric composition data are assimilated into the IFS as part of the MACC-II project. On a global scale, atmospheric composition represents the full state of the global atmosphere covering phenomena such as desert dust plumes, long-range transport of atmospheric pollutants or ash plumes from volcanic eruptions, but also variations and long-term changes in the background concentrations of greenhouse gases.

The aim of this lecture is to give an overview of the work that is carried out at ECMWF regarding the assimilation of atmospheric composition data, and to address why this is of interest and which special challenges are faced when assimilating atmospheric composition data.

By the end of the session you should:

have some understanding of the work carried out at ECMWF to assimilate data of atmospheric composition

Antje Inness

Expand

title	Coupled Data Assimilation: opportunities and challenges

The aim of this lecture is to

By the end of the lecture the participants should be able to:

Phil Browne

15.30

Expand

title	Assimilation Algorithms (1): Basic Concepts

Sebastien Massart

Followed by drinks reception and poster session

Expand

title	Background error modeling in data assimilation

The background error is central to the performance of the analysis system and tells how much confidence to put in the best available forecast which is to be updated with new observations. The lecture will review how background errors are estimated and represented for current variational algorithms.

Massimo Bonavita

Practical Session with OOPS continued

Expand

title	Ocean Data Assimilation

This lecture provides an overview of a typical ocean data assimilation system for initialization and re-analyses application. The lecture uses as an example the ECMWF ocean data assimilation system, which is based the NEMOVAR (3Dvar FGAT). This will be used to discuss design of the assimilation cycle, formulation of error covariances, observations assimilated and evaluation procedure, among others.

By the end of the lecture students should be able to:

describe the different components involved in a an ocean data assimilation system
list the commonalities and and differences between ocean and atmosphere data assimilation
describe the basics of the physical ocean observing system
explain the essential multivariate relationships between ocean variables
identify the limitations of the existing systems.

Hao Zuo

Question/answer session
Elias Holm, Lars Isaksen, Tony McNally, Mike Fisher

Course evaluation 16:-16:30

Sarah Keeley

Expand

title	Satellite Data Assimilation (EUMETSAT/ECMWF))

Multiexcerpt

MultiExcerptName	SATtime

TimeMondayTuesdayWednesdayThursdayFriday9:30 -10:45Meet the students

The infrared spectrum- measurement, modelling and

information content

Tony McNally

GPS Radio Occulation: Extended applications

Sean Healy

Satellites for environmental

monitoring and forecasting

Antje Inness

Model error

In this lecture, the impact of model error on variational data assimilation will be presented. This lecture will introduce weak-constraint 4D-Var as a way to account for model error in the data assimilation process. Several examples of results from simplified implementations in the IFS will be shown.

By the end of the lecture the participants should be able to:

describe the impact of model error on the data assimilation process,
explain the difficulties in properly accounting for model error in data assimilation.

Patrick Laloyaux

Weak_Constraint.pptx

Practical Session: Tangent Linear and Adjoints

Training_course_2017_AD_handson.pptx

14.00

Expand

title	Analysis of Radiance Observations

The primary purpose of this lecture is explore the implications of the fact that satellites can only measure radiation at the top of the atmosphere and do not measure the geophysical variables we require for NWP (e.g. temperature, humidity and wind). The link between the atmospheric variables and the measured radiances is the radiative transfer equation - the key elements of which are discussed. It is shown how - with careful frequency selection - satellite measurements can be made for which the relationship to geophysical variables is greatly simplified. Despite these simplifications, it is shown that the extraction of detailed profile information from downward looking radiance measurements is a formally ill posed inverse problem.

Data assimilation is introduced as the solution to this inverse problem, where background information and satellite observations are combined to produce a best or optimal estimate of the atmospheric state. The main elements of the assimilation scheme (such as the chain of observation operators for radiances) and its key statistical inputs are examined. In particular it is shown that incorrect specification of observation errors (R) and background errors (B) can severely limit the successful exploitation of satellite data.

By the end of this lecture you will:

understand exactly what a satellite actually measures (radiance)
appreciate the complex relationship between what is measured and what we wish to know for NWP
how information is extracted from satellite measurements in data assimilation

Tony McNally

DA_TC_satellite.ppt

Expand

title	Assimilation Algorithms: (5) Hybrid Data Assimilation methods

The aim of this lecture is to

By the end of the lecture the participants should be able to:

Massimo Bonavita

Bonavita_EDA_HYBRID_DA_TC2017.pptx

Practical Session with OOPS

Marcin Chrust

Sebastien Massart

Patrick Laloyaux

Expand

title	Data Assimilation of Atmospheric Composition

At ECMWF atmospheric composition data are assimilated into the IFS as part of the MACC-II project. On a global scale, atmospheric composition represents the full state of the global atmosphere covering phenomena such as desert dust plumes, long-range transport of atmospheric pollutants or ash plumes from volcanic eruptions, but also variations and long-term changes in the background concentrations of greenhouse gases.

The aim of this lecture is to give an overview of the work that is carried out at ECMWF regarding the assimilation of atmospheric composition data, and to address why this is of interest and which special challenges are faced when assimilating atmospheric composition data.

By the end of the session you should:

have some understanding of the work carried out at ECMWF to assimilate data of atmospheric composition

Antje Inness

Inness_envi2017.ppt.pptx

Expand

title	Coupled Data Assimilation: opportunities and challenges

At ECMWF we are striving to move towards an Earth System approach to our data assimilation techniques. We currently have models not only of the atmosphere, but of the ocean, the land surface, sea ice, waves, and atmospheric composition. These systems interact with each other in different ways and all need to be initialised through the incorporation of observational data.

The aim of this lecture is to recognise the benefits and challenges associated with data assimilation in coupled models.

By the end of the lecture the participants should be able to:

Recall the challenges associated with variational data assimilation in systems with different timescales and computer codes.
Describe the benefits of having more consitently balanced coupled systems from coupled data assimilation.
Explain the differences between weakly and strongly coupled data assimilation approaches.
Discuss the various methods that are in use at ECWMF and explain the planned developments of the systems.

Phil Browne

coupled_da_presentation.pdf

15.30

Expand

title	Assimilation Algorithms (1): Basic Concepts

This lecture will explain the basic concepts of the assimilation algorithms. The terminology used in the next lectures will be introduced. Simple examples will conduce towards the formulation of the optimal minimum-variance analysis. The optimal interpolation method will finally be presented.

By the end of the lecture the participants should be able to:

Recognize the notations used for the rest of the week
Solve the optimal minimum-variance analysis problem
Apply the optimal interpolation method

Sebastien Massart
TC_lecture_1.pdf

Followed by drinks reception and poster session

Expand

title	Background error modeling in data assimilation

The background error is central to the performance of the analysis system and tells how much confidence to put in the best available forecast which is to be updated with new observations. The lecture will review how background errors are estimated and represented for current variational algorithms.

Massimo Bonavita

BGErr_lecture_2017.ppt

Practical Session with OOPS continued

Expand

title	Ocean Data Assimilation

This lecture provides an overview of a typical ocean data assimilation system for initialization and re-analyses application. The lecture uses as an example the ECMWF ocean data assimilation system, which is based the NEMOVAR (3Dvar FGAT). This will be used to discuss design of the assimilation cycle, formulation of error covariances, observations assimilated and evaluation procedure, among others.

By the end of the lecture students should be able to:

describe the different components involved in a an ocean data assimilation system
list the commonalities and and differences between ocean and atmosphere data assimilation
describe the basics of the physical ocean observing system
explain the essential multivariate relationships between ocean variables
identify the limitations of the existing systems.

Hao Zuo
DA_course_2017_ocean_Zuo.pptx

Question/answer session
Elias Holm, Lars Isaksen, Tony McNally, Massimo Bonavita

DataAssim_Final_Discussion_2017.pptx

TC_OOPS_2017_summary.pptx

Course evaluation 16:-16:30

Sarah Keeley

Expand

title	Satellite Data Assimilation (EUMETSAT/ECMWF))

Multiexcerpt

MultiExcerptName	SATtime

Time	Monday	Tuesday	Wednesday	Thursday	Friday
9:30 -10:45	Meet the students	The infrared spectrum- measurement, modelling and information content Tony McNally SAT_TC_The_IR_spectrum_TM.ppt	GPS Radio Occulation: Extended applications Sean Healy gpsro_lecture2_2017_nwpsaf.ppt	Satellites for environmental monitoring and forecasting Antje Inness NWP_SAF_training_Course_April_2017_Inness.pptx	Satellite information on the ocean surface (SCAT) Giovanna De Chiara GDeChiara_surface_obs_2017_1.1.pptx
11:15...12:30	Theoretical background (1) What do satellites measure ? Tony McNally SAT_TC_What_do_Satellites_Measure_TM.ppt	GPS Radio Occulation: Principles and NWP use Sean Healy gpsro_lecture1_2017_nwpsaf_v1.ppt	The detection and assimilation of clouds in infrared radiances Tony McNally SAT_TC_Detection_and_assimilation_clouds_IR.ppt	Background errors for satellite data assimilation Tony McNally SAT_TC_Estimation_and_correction_systematic-error.ppt	Systematic errors, monitoring and auto-alert systems Mohamed Dahoui Dahoui_Satellite_2017.pptx
14:00...15:15	Theoretical background (2) Data assimilation algorithms, Key elements and inputs Tony McNally SAT_TC_Data_assimilation_key_elements_TM.ppt	Satellite information on the land surface Patricia de Rosnay Land_satellite_NWP_SAF_TC_2017.pdf	The detection and assimilation of clouds and rain in microwave radiances Alan Geer sat_microwave_2017_2.pptx	Observation errors for satellite data assimilation Peter Weston ObsErrors_2017.pptx	Current satellite observing network and its future evolution Stephen English CurrentAndFutureSatellite.pptx
15:45...17:00	The microwave spectrum, measurement, modelling and information content Alan Geer sat_microwave_2017_1.pptx	A Practical guide to IR and MW radiative transfer – using the RTTOV model and GUI David Rundle (UK Met Office) SAT_RT_modelling_lecture_JAH_DR.ppt SAT_RT_modelling_practical_JAH_DR.ppt	Wind information from satellites (Atmospheric Motion Vectors) Katie Lean amv_4dtracing_training_KLean_2017.pptx	1DVar theory, simulator + practical session on background and observation errors Tony McNally SAT_TC_Background_errors_TM.ppt SAT_TC_1DVAR_practical.pptx	Question and answer session, course evaluation

Satellite information on the ocean surface (SCAT)

Giovanna De Chiara

Theoretical background (1)

What do satellites measure ?

Tony McNally

GPS Radio Occulation: Principles and NWP use

Sean Healy

The detection and assimilation of clouds in infrared radiances

Tony McNally

Background errors for satellite data assimilation

Tony McNally

Systematic errors, monitoring and auto-alert systems

Mohamed Dahoui

Theoretical background (2)

Data assimilation algorithms, Key elements and inputs

Tony McNally

The detection and assimilation of clouds and rain in microwave radiances

Alan Geer

Observation errors for satellite

data assimilation

Peter Weston

Current satellite observing network and its future evolution

Stephen English

The microwave spectrum,

measurement, modelling and

information content

Alan Geer

Wind information from satellites

(Atmospheric Motion Vectors)

Katie Lean

1DVar theory, simulator + practical

session on background and observation errors

Tony McNally

Expand

title	Predictability and ocean-atmosphere ensembles

Multiexcerpt

MultiExcerptName	PRtime

Time:

Monday

Tuesday

Wednesday

Thursday

Friday

9.15-10.15

Introduction to the course

with Computer Hall tour

Expand

title	Initial Uncertainties (2)

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

Simon Lang

lang_2_2017new.pdf

Expand

title	Diagnostics (1)

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

Mark Rodwell

20170510_TC_PR_Diags_1_01_static.pdf

Expand

title	Diagnostics (2)

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

Mark Rodwell

20170511_TC_PR_Diags_2_03_static.pdf

Expand

title	Initializaton techniques in coupled forecasting

Magdalena Balmaseda

TCPR_Balmaseda_2017_Initialization_b.pdf

10.45

Expand

title	Introduction to Chaos

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

11:15...12:30

14:00...15:15

Satellite information on the land surface

Patricia de Rosnay

15:45...17:00

A Practical guide to IR and MW radiative transfer – using the RTTOV model and GUI

David Rundle (UK Met Office)

Question and answer session,

course evaluation

Expand

title	Predictability and ocean-atmosphere ensembles

Multiexcerpt

MultiExcerptName	PRtime

Time:

Monday

Tuesday

Wednesday

Thursday

Friday

9.15-10.15

Introduction to the course

with Computer Hall tour

Expand

title	Initial uncertainties in the medium-range ENS (2)

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

Expand

title	Ensemble data assimilation

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 presentedRoberto Buizza

.

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

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

Antje Weisheimer

Intro_to_ChaosPres_short_AW2017.pdf

Expand

title	Ensemble verification (

2

1)

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

to measure reliability
understand why resolution and sharpness of a probabilistic forecast matter
compute several of the standard verification metrics used for ensemble forecasts

Martin Leutbecher

v1handout.pdf

Expand

title	Weather regimes

Franco Molteni

TCPR_Molteni_2017_regimes.pdf

Expand

title	Coupled ocean-atmosphere variability - MJO

Franco Molteni

TCPR_Molteni_Vitart_2017_MJO.pdf

Expand

title	The monthly forecast system at ECMWF

Martin Leutbecher

Expand

title	Coupled ocean-atmosphere variability

This lecture provides a broad overview of the role of the ocean on the predictability and prediction of weather and climate. It introduces some basic phenomena needed to to understand the time scales and nature of the ocean-atmosphere coupling.

Magdalena Balmaseda

Expand

title	Initializaton techniques in seasonal forecasting

Magdalena Balmaseda

10.35-1135

Expand

title	Introduction to Chaos

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

Sarah Keeley

ExpandtitleApproaches to ensemble prediction/TIGGE

The aim of this session is to

illustrate the key characteristic of the nine operational global, medium-range ensemble systems. These are the ensembles available also within the TIGGE (Thorpex Interactive Grand Global Ensemble) project data-base. Similarity and differences in the approaches followed to simulate the sources of forecast uncertainties will be discussed, and their relevance for forecast performance will be illustrated

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:

illustrate the main similarities and differences of the 9 TIGGE global ensembles
link the performance differences of TIGGE ensemble to their design
describe the main differences between singular vectors and EDA-based perturbations

Roberto Buizza

Expand

title	Weather regimes

Franco Molteni

Expand

title	Coupled ocean-atmosphere variability - MJO

Frederic Vitart

Expand

title	The monthly forecast system at ECMWF

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

Frederic Vitart

11.45-12.45 Expand

title	Sources of uncertainty

The aim of this session is to introduce the main sources of uncertainty that lead to forecast errors. The weather prediction problem will be

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

Magdalena Balmaseda

TCPR_Vitart_2017.2.pdf

11.55

Expand

title	Sources of uncertainty

The aim of this session is to introduce the main sources of uncertainty that lead to forecast errors. The weather prediction problem will be discussed, and stated it in terms of an appropriate probability density function (PDF). The concept of ensemble prediction based on a finite number of integration will be introduced, and the reason why it is to be the only feasible method to predict the PDF beyond the range of linear growth will be illustrated.

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

explain which are the main sources of forecast error
illustrate why numerical prediction should be stated in probabilistic terms
describe the rationale behind ensemble prediction

Antje Weisheimer

sources_of_uncertainty_AW2017.pdf

Expand

title	Using stochastic physics to represent model error

After this lecture, students will be able to:

explain the physical and practical motivations for using stochastic physics in an ensemble forecast;
describe the two stochastic parameterization schemes used in the IFS ensemble, and their respective purposes;
be able to identify the improvement in forecasting skill from the inclusion of stochastic physics.

Sarah-Jane Lock

StochPhys2017_print.pdf

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title	Clustering techniques and their applications

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

discussed, and stated it in terms of an appropriate probability density function (PDF). The concept of ensemble prediction based on a finite number of integration will be introduced, and the reason why it is to be the only feasible method to predict the PDF beyond the range of linear growth will be illustrated

.

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

explain

which are the main sources of forecast error

illustrate why numerical prediction should be stated in probabilistic terms

describe the rationale behind ensemble prediction

Roberto Buizza

Expand

title	Ensemble verification (1)

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

Martin Leutbecher

Expand

title	Clustering techniques and their applications

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

how the cluster analysis works
use the ECMWF clustering products

Laura Ferranti

TC_clustering_2017.pdf

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title	Coupled land-atmosphere variability

Land surface is a potential source of predictability of weather variability, such as warm or cold spells or precipitation. We will review the way land surface affects the atmospheric conditions, and the criteria that need to be fulfilled to contribute to predictability. A number of land-atmosphere coupling metrics are discussed, as well as a number of studies on the effect of realistic land surface initialization reported in literature.

Bart van den Hurk

Land_surface_predictability_for_training_course.pdf

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title	The seasonal forecast system at ECMWF

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,

By the end of the session

you should be able to:

explain

how the cluster analysis works

use the ECMWF clustering products

Laura Ferranti

Expand

title	Diagnostics (2)

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

Mark Rodwell

Expand

title	The seasonal forecast system at ECMWF

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

Tim Stockdale

2.00-3.00 Expand

title	Sources of predictability beyond the deterministic limit

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.

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

Tim Stockdale

tc2017_seasonal.pdf

2.00

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title	Sources of predictability beyond the deterministic limit

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

Sarah Keeley

Beyond_limit.pdf

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title	Post-processing of ensemble forecasts

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,

After this session

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

Mark Rodwell

Expand

title	Post-processing of ensemble forecasts

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

Tim Stockdale

2.45pm Discussion Session in the Weather Room

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title	Latest forecasts

The latest medium, monthly and seasonal forecasts will be discussed in terms of out look and performance.

This is a combined event with the weekly weather discussion that ECMWF staff attend.

3.30-4.30

Expand

title	Initial uncertainties in the medium-range ENS (1)

The aim of the this lecture is to discuss basic concepts behind initial perturbation techniques.

After the lecture you should be able to:

Understand the difference between singular vectors and breeding (ETKF) vectors
Explain why pure random perturbations do not work

Linus Magnusson

Expand

title	Stratospheric impacts

Andrew Charlton-Perez

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

Sarah Keeley

Expand

title	Using stochastic physics to represent model error

After this lecture, students will be able to:

explain the physical and practical motivations for using stochastic physics in an ensemble forecast;
describe the two stochastic parameterization schemes used in the IFS ensemble, and their respective purposes;
be able to identify the improvement in forecasting skill from the inclusion of stochastic physics.

Sarah-Jane Lock

Expand

title	Diagnostics (1)

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

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

Tim Stockdale

tc2017_calibration.pdf

Expand

title	Stratospheric impacts

Andrew Charlton-Perez

charlton_perez_strat.pdf

Expand

title	Coupled ocean-atmosphere variability

This lecture provides a broad overview of the role of the ocean on the predictability and prediction of weather and climate. It introduces some basic phenomena needed to to understand the time scales and nature of the ocean-atmosphere coupling.

Magdalena Balmaseda

TCPR_Balmaseda_2017_ocean_updated.pdf

2.45pm Discussion Session in the Weather Room

Expand

title	Latest forecasts

The latest medium, monthly and seasonal forecasts will be discussed in terms of out look and performance.

This is a combined event with the weekly weather discussion that ECMWF staff attend.

3.30

Expand

title	Initial uncertainties in the medium-range ENS (2)

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

SImon Lang

lang_1_2017.pdf

Expand

title	Ensemble verification (2)

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

Martin Leutbecher

v2handout.pdf

Linus Magnusson/Sarah Keeley

Practice Session:

Expand

title	Lorenz '95 model

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.

Martin Leutbecher

Practice Session:

Ensemble Verification

Expand

title	Economic Value of Ensembles

Louise Arnal, Sarah Keeley and Sarah-Jane Lock

4.30-5.15

Understanding Ensembles Practical

Computer hall and Weather Room Tours

5.15 ice breaker

Lecture and Practice Session:

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title	Application of ENS: Flood

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.

Fredrik Wetterhall

fred_flooding2017.pdf

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