Slide 1

GLAMEPS:
Grand Limited Area Model Ensemble
Prediction System
Towards establishing a European-wide
TIGGE – LAM ?

Trond Iversen
met.no & Univ. Of Oslo

Based on discussions involving (inter alia)
Dale Barker, Jan Barkmeijer, Jose Antonio Garcia-Moya,
Nils Gustafsson, Bent Hansen Sass, Andras Horanyi, Trond Iversen,
Martin Leutbecher, Jeanette Onvlee, Bartolome Orfila, Xiaohua Yang.
Norwegian Meteorological Institute met.no


Slide 2

GLAMEPS First planning document exists
- will be amended as new partners enter
Contributions from partners –
R & D and operational

Norwegian Meteorological Institute met.no


Slide 3

GLAMEPS - Working Group
established ad hoc at the Aladin-HIRLAM predictability
planning meeting in Madrid, March 2006

Jan Barkmeijer (KNMI), Jose Antonio Garcia-Moya (INM),
Nils Gustafsson (SMHI), Andras Horanyi (HMS), Trond Iversen (met.no),
Martin Leutbecher (ECMWF)

Norwegian Meteorological Institute met.no


Slide 4

The GLAMEPS objective
is in real time to provide to all HIRLAM and
ALADIN partner countries * :
an operational, quantitative basis for
forecasting probabilities of weather events
in Europe up to 60 hours in advance
to the benefit of highly specified as well as
general applications,
including risks of high-impact weather.
* List of partners should be extended !
Norwegian Meteorological Institute met.no


Slide 5

Why ensemble prediction?
Why not use all resources to produce
the ”best possible model” and
the ”best possible forecast”,
in stead of a multitude of inferior models and forecasts?

Answer:
Yes, we need ”the best possible model” (e.g. high resolution),
BUT: the best possible forecast is not ”deterministic”, because:
• Weather prediction is not a deterministic problem!
• If we pretend it’s deterministic, we lose crucial information
for protecting human lives and property
• Predictability of free flows decreases with decreasing scales;
i.e.: higher resolution increases the need for information
about spread and the timing of spread saturation
Norwegian Meteorological Institute met.no


Slide 6

Why ensemble prediction?
Forecast products which require information of spread:
• How certain is today’s weather forecast?
• Ensemble, or rather cluster, averages, have longer
predictability than the single ”best” (control) forecast
• What are the risks of high-impact events?
• In a well calibrated EPS:
Probable sources to forecast errors can be diagnased
• Forecasts beyond the predictability limit of pure
atmospheric forecasts (monthly, seasonal, and longer) is
impossible with a deterministic strategy.
Norwegian Meteorological Institute met.no


Slide 7

A. Murphy (1993): What is a good forecast?

1.

Consistency: correspondence between forecaster‘s
best judgement and their forecasts

2.

Quality: correspondence between forecasts and
matching observations

3.

Value: benefits realised by decision makers through
the use of the forecasts

Norwegian Meteorological Institute met.no


Slide 8

Example: Norwegian 20 member LAMEPS
(HIRLAM) and Targeted EPS (ECMWF IFS)

>90mm/24h

130mm/24h

“100 year precipitation event”
in the middle part of Norway
30-31. January 2006

Norwegian Meteorological Institute met.no


Slide 9

LAMEPS
P24 > 60mm

Forecasted risk =
probability x potential damage

TEPS
P24 > 60mm

Norwegian Meteorological Institute met.no


Slide 10

Schematic: Sources of prediction spread in a LAM

Temperature, precipitation, etc.

• Initial analysis and lateral boundaries
• Lower and upper boundaries
Non-linear filtering of
unpredictable components

ensemble mean better than control

true development

model x
Truth outside spread  model error?

t=0

t=60h
Norwegian Meteorological Institute met.no


Slide 11

Schematic: Sources of prediction spread in a LAM

Temperature, precipitation, etc.

• Initial analysis and lateral boundaries
• Lower and upper boundaries

model y
slightly better than model x

true development

t=0

t=60h
Norwegian Meteorological Institute met.no


Slide 12

Schematic: Sources of prediction spread in a LAM

Temperature, precipitation, etc.

• Initial analysis and lateral boundaries
• Lower and upper boundaries
• Numerical approximations and parameterized physics

multimodel

true development

Warning: it’s not always this simple….

t=0

t=60h
Norwegian Meteorological Institute met.no


Slide 13

In the multi-modal spread regime: Cluster or modal
means in stead of ensemble means
Temperature

(Fig. by R. Hagedorn)

Initial condition

Forecast time

Forecast

Complete description of weather prediction in terms of a
Probability Density Function (PDF)
Norwegian Meteorological Institute met.no


Slide 14

Combining probabilistic forecasts from
several models may give better scores
than even the better of each models.
Example (Norwegian system):
ROC – Area
as a function of precipitation
Treshold.
Black curve (NORLAMEPS)
is a combination of
the blue (LAMEPS) and the
green (TEPS)
[The red is the standard ECMWF
EPS for reference.]
Norwegian Meteorological Institute met.no


Slide 15

Influence of North Atlantic SST in Europe?
(I.-L. Frogner, M.H. Jensen, met.no)

Targetted Forcing Singular Vectors,
ECMWF IFS,
Winter, High NAO
(the 20% most sensitive days)

Meridional
Cross-section
along 0 deg.
From north pole
To 40 deg N
Norwegian Meteorological Institute met.no


Slide 16

Approach and aims during HIRLAM-A
• An array of LAM-EPS models or model versions:
– Each partner runs a unique model version
– Each partner runs between 5 and 20 ensemble members
based on initial and lateral boundary perturbations
– Some partners also perturb the lower boundary data

• Grid resolution
– 10 km or finer, 40 levels, identical in all model versions

• Forecast range 60h - starting daily from 12 UT
• A common integration domain (!), including:
–
–
–
–

North Atlantic Ocean north of ca. 15 deg N.
Greenland,
European part of the Arctic
European continent to the Urals
Norwegian Meteorological Institute met.no


Slide 17

SRNWP-PEPS

SRNWP – PEPS
(only a tiny common area)

Norwegian Meteorological Institute met.no


Slide 18

Quality objective
To operationally produce ensemble forecasts with
• a spread reflecting known uncertainties in data and
model;
• a satisfactory spread-skill relationship (calibration); and
• a better probabilistic skill than the operational ECMWF
EPS;
for
• the chosen forecast range of 60 hours;
• our common target domain; and
• weather events of our particular interest
(probabilistic skill parameters).

Norwegian Meteorological Institute met.no


Slide 19

Hirlam + Aladin +……+
is, by construction, particularly well
suited for GLAMEPS:
by commonly exploit the distributed
resources for short-range NWP in
Europe, for the benefit of our users.
Norwegian Meteorological Institute met.no


Slide 20

Experience
• Global multimodel, grand / super ensembles:
mainly in the medium to long range, and in
climate predictions (demeter, ensembles,
climatePrediction.net, IPCC, etc.)
• Short range Europe: much less, and mainly
–
–
–
–

with single model LAMEPS,
downscaling of global EPS,
Targetted global EPS,
Multi-model LAM without initial/lateral boundary
perturbations,
– Some experience with breeding and LAM SVs

Norwegian Meteorological Institute met.no


Slide 21

Initial Step:
build on existing operational experience
• To select a small selection of HIRLAM / ALADIN model versions
which are well established and significantly different, but still
approx. equally valid representations of the atmosphere
– different models (ALADIN, HIRLAM,…? Build on i.a. INM experience)
– different physical packages (STRACO and RK-KF deep conv)

• For some models to vary selected lower boundary parameters
– Atlantic SST
– Soil mosture

• To construct initial/lateral boundary perturbations
representative for trigging the ”instability of the day” given
the uncertainty constraints
– ECMWF TEPS / EPS (build on met.no LAMEPS)

• Ensemble calibration (spread-skill-ratio)
Norwegian Meteorological Institute met.no


Slide 22

Operational
To run a first phase suite at ECMWF (Special Project)
–
–
–

Some scripts are ready
Some verification tools are ready
Some tools for probabilistic products are ready

A possibility is to establish a ”PAF” (Prediction
Application Facility) with ECMWF

Norwegian Meteorological Institute met.no


Slide 23

Further on
1. Through research to gradually increase ensemble size and
error-sources
Include other types of model and lower boundary perturbations
– vary model coefficients (e.g. learn from climate modelers;
challenge for ”physical processes – community” in NWP)
– Targeted Forcing SVs or Forcing Sensitivities (KNMI, met.no),
– weak 4D-Var perturbed tendencies?
– ….
Include alternative intial/lateral boundary perturbations
– ETKF generalized breeding (SMHI),
– HIRLAM and ALADIN LAM SVs (KNMI, SMHI, HMS),
– …

2. To run a de-centralized system with real-time dissemination
of data, or through a common central (e.g. ECMWF)
3. Ensemble calibration in all phases

Norwegian Meteorological Institute met.no


Slide 24

Calibration: Spread-skill ratio
Example from medium-range (T. Palmer)

Too little spread:
Model error missing?

Too much spread:
Wrong correction
of too small spread at
day 2-3?

Norwegian Meteorological Institute met.no


Slide 25

From climatePrediction.net
Univ of Oxford & The Hadley Centre
Only cloud-related parameters are perturbed
Does Climate modelers and SRNWP-modelers
have common challenges?

Pdf of climate
sensitivity:
dT at 2xCO2
Based on ~50000
ensemble members

Norwegian Meteorological Institute met.no


Slide 26

Practical condition for success:
Broad participation with long term allocation of human
and technical resources from at least 20 partners

( ~200 ensemble members or more)

Please sign in!
Workshop in Vienna, Nov. 13-14, 2006

Norwegian Meteorological Institute met.no


Slide 27

Thank you for your attention
Mother of Pearl clouds over Oslo, January 2002

Norwegian Meteorological Institute met.no