Page 1© Crown copyright 2005 Ensemble Forecasting: THORPEX and the future of NWP Richard Swinbank, with thanks to Ken Mylne and David Richardson UTLS International

© Crown copyright 2005 Page 1

Ensemble Forecasting:THORPEX and the future of NWP

Richard Swinbank, with thanks to

Ken Mylne and David Richardson

UTLS International School, Cargese, October 2005


Ensembles - Outline

Why Ensemble forecasts?Ensemble forecasting at the Met OfficeTHORPEX – improving the prediction of high-impact weather

Multi-model ensembles - TIGGE and NAEFSThe future of forecasting

Ensemble Forecasts


Forecast failures

Today’s NWP systems are one of the great scientific achievements of the 20th Century, but…

We've all heard of high-profile forecast failures:

16-17 Oct '87 – still difficult with today’s systems

Dec '99 French storms

Less severe errors are much more common, especially in medium-range forecasts

So what causes errors in forecasts? Analysis Errors Model Errors and Approximations Unresolved Processes


Ensembles Forecasts

Small errors in initial conditions will always amplify and, together with model errors and approximations, limit the useful forecast range.

By running an ensemble of many model forecasts with small differences in initial conditions (and model formulation) we can:

take account of uncertainty sample the distribution of forecast states estimate probabilities


Ensemble forecasting

time

Forecast uncertainty

Climatology

Initial Condition Uncertainty

X

Deterministic Forecast

Analysis


Lorenz Model

Variations in predictability can be illustrated using the Lorenz (1963) model:

X aX aY

Y XZ bX Y

Z XY cZ

Simple non-linear system.

Possible atmospheric analogue: Zonal Flow Blocked Flow


Ensemble Forecasting in the Lorenz Model

1. Predictable - deterministic OK

2. Predictable at first -

probability OK

3. Unpredictable climatology OK


Desirable properties of ensembles

By sampling the initial (and forecast model) uncertainties an ensemble forecast system aims to forecast the PDF (probability density function).

To achieve this we need: All members equally probable RMS spread of members is similar to RMS error of control

forecast

If these criteria are met, the ensemble can be used to estimate probabilities: If 20% of members predict X, then the probability of X is

estimated to be 20%


Rank histograms

For each ensemble forecast rank members by forecast parameter, e.g. temperature at station locations

Identify rank of each verifying observation

Plot histogram of observation ranksIdeal is flatTypically get excessive outliers

Two simple ways of showing all ensemble members together

•Spaghetti Plot

•Postage stamp plot

Visualising Ensemble Forecasts (1)

Visualising Ensemble Forecasts (2)

An EPS meteogram portrays probabilistic information at a particular location

(In this case an ECMWF forecast for Cargèse – how did it work out?)

Ensemble forecasting at the Met Office


Use of ECMWF EPS at Met Office

ECMWF ensemble forecasts are used to assess the most probable outcome before creating the medium-range forecast charts


Probability Forecasts from Ensembles

Probability forecast products available to end-users

assess and manage risk

Post-processing of site-specific forecasts

Applied routinely in offshore-oil operations


First Guess Early Warnings Project

National Severe Weather Warning Service:

Met Office issues Early Warnings up to 5 days ahead - when probability 60% of disruption due to:

Severe Gales Heavy rain Heavy Snow

FGEW System provides forecasters with alerts and guidance from EPS

Probs for regions of UKProb in UK=67%


Short-range Ensembles

ECMWF EPS has transformed the way we do Medium-Range Forecasting

Uncertainty also in short-range: Rapid cyclogenesis often poorly forecast deterministically (e.g.

Dec 1999) Many customers most interested in short-range

Assess ability to estimate uncertainty in local weather QPF Cloud Ceiling, Fog Winds etc


Ensemble for short-range forecastingRegional ensemble over N. Atlantic and Europe (NAE)Nested within global ensemble for LBCsETKF perturbationsStochastic physicsT+72 global, T+36 regional

Met Office Global and Regional EPS, MOGREPS

NAE


ETKF Generation of Perturbations

ObservationsAnalysis (Var)ETKF

Xf1Xf2Xf3…

T+12

• ETKF similar to Error Breeding but with matrix transformation of all perturbations to provide next set

• Perturbations scaled according to analysis uncertainty using observation errors


ETKF in global UM

ETKF set up with global UMProcessing all observations used in data

assimilation12-hour cycle (f/c twice per day)Running in conjunction with stochastic physics to

propagate effectEncouraging growth rate in case studies

(ECMWF use singular vectors of linear model to identify rapidly growing modes)


Stochastic Physics Schemes

Three components to current stochastic physics: Installed in current version:

Stochastic Convective Vorticity (SCV)Random Parameters (RP)

Under test:Stochastic Kinetic Energy Backscatter (SKEB)


Current scheme (SCV+RP) has Substantial impact on surface variables in the short-range (72-h):

PMSL (up to 5 hPa) T2M (up to 9ºC) PREC (up to 40% of control values)

Neutral impact on model climate

Stochastic Physics Summary

•New SKEB scheme has:•Larger impact•Realistic growth rate

Increase in spread for an IC-only ensemble

500 hPa geopotential height

SKEB

RP+SCV


THORPEX

Accelerating improvements in the accuracy of one-day to two weeks high-impact weather

forecasts for the benefit of society, economy and

environment

A photographic collage depicting the societal, economic and ecological impacts of severe weather associated with four Rossby wave-trains that encircled the globe during November 2002.

2005 2014…


What is THORPEX?

THORPEX: a World Weather Research Programme Where THORPEX means “THe Observing System Research and Predictability EXperiment”

THORPEX was established in May 2003 by the Fourteenth World Meteorological Congress as a ten-year international global atmospheric research and development programme under the auspices of the WMO Commission for Atmospheric Sciences (CAS).

THORPEX is a part of the WMO World Weather Research Programme (WWRP)


THORPEX Objectives

To reduce and mitigate natural disasters; To fully realise the societal and economic

benefits of improved weather forecasts, especially in developing and least developed countries.

This is achievable by:

1. Extending the range of skilful weather forecasts to time scales of value in decision-making (up to 14 days) using probabilistic ensemble forecast techniques;

2. Developing accurate and timely weather warnings in a form that can be readily used in decision-making support tools;

3. Assessing the impact of weather forecasts and associated outcomes on the development of mitigation strategies to minimise the impact of natural hazards.


High-impact weather events

The objective is to improve the forecasting of high-impact weather at short- and medium-range, for instance:

Local scale (UK)Boscastle – intense rain and flooding August 2004

Regional scale (Europe)Heatwave in France, August 2003

Global phenomena, such as tropical cyclonesHurricane Katrina, New Orleans, August 2005

Multi-model Ensembles


Multi-model ensembles

Multi-model ensembles combine ensemble forecasts produced from different models (usually different NWP centres).

This gives access to a bigger ensemble size at relatively little extra cost.

In addition, results from DEMETER (seasonal forecasting project) indicate that there is also a benefit from using different forecast models.


Benefits of multi-model ensembles

By better representing the uncertainties within the different modelling systems, a multi-model ensemble gives a much better representation of the probability (risk) of given events occurring

Figures show how well the forecast probability of an event match the actual probability that the situation will occur. For a perfect forecast system the line will lie on the diagonal

Combined multi-model

ECMWF Meteo-France

Met Office

Reliability: 2m temperature above normal, DEMETER seasonal forecasts


Why should multi-model ensembles be better?

Can a poor model add skill? If all aspects of a model are poor, perhaps not, unless its

errors cancel with another.

How can the multi-model be better than the average single model performance? Error cancellation and non-linearity of probabilistic diagnostics

tend to make multi-model results better in practice.

Why not use the best single model instead? Models tend to have different strengths and weaknesses, so

there is no single best model.

(Hagedorn et al, 2005)


Met Office medium-range ensemble

Develop from short range ensemble system (MOGREPS)

Multi-model ensemble, in collaboration with TIGGE partners, including ECMWF and NAEFS.

To be run using UK allocation of resources on ECMWF supercomputer


Medium Range Ensemble Forecast Process

Initial Analysis

Perturbations

Initial Analysis

Perturbations

CreateInitial Conditions

Run Ensemble forecast

TIGGEarchive

Multi-modelEnsemble

Perturbed Initial conditions

Single-model ensemble

Met OfficeECMWF

Combine Ensemble forecasts

Ensemble forecasts from other models

Product generationProductsProducts


TIGGE

THORPEX Interactive Grand Global Ensemble Framework for international collaboration in

development and testing of ensemble prediction systems

Resource for many THORPEX research projects Prediction component of THORPEX Forecast

Demonstration Projects (FDPs) A prototype future Global Interactive Forecast

System Global and regional components


TIGGE

Initially develop database of available ensembles, collected in near-real time

Co-ordinate research using this multi-model ensemble data Compare initial condition methods Compare multi-model and perturbed physics Develop ways to combine ensembles Boundary conditions for regional ensembles Regime-dependence of ensemble configuration (size, resolution,

composition)

Observation targeting (case selection, ETKF sensitive area prediction)

Societal and economic impacts assessment

Close interaction with other THORPEX sub-programmes


TIGGE infrastructure Phase 1

Data collected in near-real time (via internet ftp) at central TIGGE data archives

Can be implemented now at little cost

Can handle current data volumes within available network and storage capabilities

TIGGE Centre A

EPS 1 EPS 2 EPS n

NHMS academic End user

TIGGE Centre B

Predictability science

Real-worldapplications


North American Ensemble Forecast System

USA, Canada, and Mexico have set up NAEFS

This is an operational multi-model ensemble forecast system

There are strong links with the TIGGE research programme

Met Office will join on an experimental basis while we evaluate our medium-range ensemble system and the benefit of multi-model ensembles

Forecasting – the future?


Traditional forecast system

observations Assimilation Forecast users


A new interactive NWP process

The traditional NWP process is characterized by separate steps with one-way flow of information.

In a future NWP process there will be strong feedback among the components, with two-way interaction. Errors and uncertainty will be accounted for.

Observing System

Data Assimilation

Forecast System

Applications

Data

Analysis

Single-value forecast

Observation targeting

Forecast error covariance

Targeted forecast requirements

Probabilistic forecast

Initial state + errors

Data + error estimate


A possible Global Interactive Forecast System

Forecaster requests high resolution regional ensemble

Initial risk from medium-range global ensemble

Initiate and maintain links with civil protection agencies

Forecaster requests observations in sensitive area

Forecaster runs ‘sensitive area’ prediction


Observation targeting

• Prediction of sensitive areas where extra observations will provide most benefit to forecasts

• Adaptive control of observing network

• Targeted use of satellite data (adaptive, intelligent thinning)


Summary

Ensemble forecasting enables us to get a probabilistic perspective on weather forecasts.

This is particularly important to highlight the possibility of high-impact weather events.

A key part of the THORPEX programme is the TIGGE project, intended to lead to the development of a global interactive forecast system.

The Met Office has developed an ensemble forecasting system including ETKF perturbations and stochastic physics that will contribute to the international TIGGE project.


The End

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Page 1© Crown copyright 2005 Ensemble Forecasting: THORPEX and the future of NWP Richard Swinbank, with thanks to Ken Mylne and David Richardson UTLS International