Transcript Ensembles talk for Cargese

Ensemble Forecasting:
THORPEX and the future of NWP
Richard Swinbank,
with thanks to
Ken Mylne and David Richardson
UTLS International School, Cargese, October 2005
© Crown copyright 2005
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Ensembles - Outline
Why Ensemble forecasts?
Ensemble forecasting at the Met Office
THORPEX – improving the prediction of highimpact weather
Multi-model ensembles - TIGGE and NAEFS
The future of forecasting
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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
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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
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Ensemble forecasting
Deterministic Forecast
Forecast uncertainty
Initial Condition
Uncertainty
X
Analysis
Climatology
time
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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:
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Zonal Flow
Blocked Flow
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Ensemble Forecasting in the Lorenz Model
1. Predictable deterministic OK
2. Predictable at
first probability OK
3. Unpredictable
climatology OK
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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%
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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 ranks
Ideal is flat
Typically get excessive
outliers
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Visualising Ensemble
Forecasts (1)
Two simple ways of
showing all ensemble
members together
•Spaghetti Plot
•Postage stamp plot
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
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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
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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%
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Short-range Ensembles
 ECMWF EPS has transformed the way we do MediumRange 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
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Met Office Global and Regional EPS, MOGREPS
NAE
 Ensemble for short-range
forecasting
Regional ensemble over N.
Atlantic and Europe (NAE)
Nested within global
ensemble for LBCs
ETKF perturbations
Stochastic physics
T+72 global, T+36 regional
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ETKF Generation of Perturbations
• 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
T+12
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Observations
Analysis (Var)
ETKF
Xf1
Xf2
Xf3
…
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ETKF in global UM
ETKF set up with global UM
 Processing all observations used in data
assimilation
 12-hour cycle (f/c twice per day)
 Running in conjunction with stochastic physics to
propagate effect
 Encouraging growth rate in case studies
(ECMWF use singular vectors of linear model to
identify rapidly growing modes)
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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)
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Stochastic Physics Summary
 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
•New SKEB scheme has:
•Larger
impact
•Realistic
growth rate
Increase in spread for an IConly ensemble
500 hPa geopotential height
SKEB
RP+SCV
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THORPEX
© Crown copyright 2005
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Accelerating improvements in the accuracy
of one-day to two weeks high-impact weather forecasts
for the benefit of society, economy and environment
2005
2014…
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.
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 tenyear 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)
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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.
2.
3.
Extending the range of skilful weather forecasts to time scales of value
in decision-making (up to 14 days) using probabilistic ensemble
forecast techniques;
Developing accurate and timely weather warnings in a form that can
be readily used in decision-making support tools;
Assessing the impact of weather forecasts and associated outcomes
on the development of mitigation strategies to minimise the impact of
natural hazards.
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High-impact weather events
The objective is to improve the forecasting of
high-impact weather at short- and mediumrange, 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 cyclones
 Hurricane Katrina, New Orleans, August 2005
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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.
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Benefits of multi-model ensembles
Reliability: 2m temperature above normal, DEMETER seasonal forecasts
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
ECMWF
Met Office
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
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Meteo-France
Combined
multi-model
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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)
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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
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Medium Range Ensemble Forecast Process
Met Office
Initial Analysis
ECMWF
Initial Analysis
Create
Initial Conditions
Perturbations
Products
Perturbed
Initial conditions
Perturbations
Products
Product generation
Run Ensemble
forecast
Ensemble forecasts
from other models
Single-model
ensemble
TIGGE
archive
Combine
Ensemble
forecasts
Multi-model
Ensemble
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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
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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
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TIGGE infrastructure Phase 1
 Data collected in
near-real time (via
internet ftp) at central
TIGGE data archives
Predictability
science
NHMS
Real-world
applications
academic
End user
 Can be implemented
now at little cost
TIGGE Centre A
 Can handle current
data volumes within
available network and
storage capabilities
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EPS 1
EPS 2
TIGGE Centre B
EPS n
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North American Ensemble Forecast System
 USA, Canada, and Mexico
have set up NAEFS
 This is an operational multimodel 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
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Forecasting – the future?
Traditional forecast system
observations
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Assimilation
Forecast
users
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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.
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Observing System
Data + error
Data
estimate
Observation
targeting
Data Assimilation
Initial
state
Analysis
+ errors
Forecast error
covariance
Forecast System
Probabilistic
Single-value
forecast
forecast
Targeted
forecast
requirements
Applications
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A possible Global Interactive Forecast System
Initial risk from
medium-range
global
ensemble
Initiate and maintain
links with civil
protection agencies
Forecaster requests
high resolution
regional ensemble
Forecaster requests
observations in
sensitive area
Forecaster
runs
‘sensitive
area’
prediction
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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)
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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.
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The End
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