Norwegian Meteorological Institute met.no

GLAMEPS:GLAMEPS:GGrand LLimited AArea MModel EEnsemble

PPrediction SSystem

Towards establishing a European-wide TIGGE – LAM ?

Trond IversenTrond Iversenmet.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

GLAMEPS First planning document exists- will be amended as new partners enter

Contributions from partners –R & D and operational

Norwegian Meteorological Institute met.no

GLAMEPS - Working Groupestablished ad hoc at the Aladin-HIRLAM

predictabilityplanning 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

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.

The GLAMEPS objective

* List of partners should be extended !

Norwegian Meteorological Institute met.no

Why ensemble prediction?Why not use all resources to produce

the ”best possible model” and the ”best possible model” and

the ”best possible forecast”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

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

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

“100 year precipitation event” in the middle part of Norway

30-31. January 2006

>90mm/24h

130mm/24h

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

Norwegian Meteorological Institute met.no

LAMEPSP24 > 60mm

TEPSP24 > 60mm

Forecasted risk =probability x potential

damage

Norwegian Meteorological Institute met.no

Schematic: Sources of prediction spread in a LAM

• Initial analysis and lateral boundaries• Lower and upper boundaries

t=0

true development

”best forecast” model x

t=60h

Tem

pera

ture

, p

recip

itati

on

, etc

.

ensemble mean model x

model xTruth outside spread model error?

Non-linear filtering of unpredictable components

ensemble mean better than

control

Norwegian Meteorological Institute met.no

Schematic: Sources of prediction spread in a LAM

• Initial analysis and lateral boundaries• Lower and upper boundaries

t=0

true development

”best forecast” model y

t=60h

Tem

pera

ture

, p

recip

itati

on

, etc

.

Ensemble mean model y

model yslightly better than model x

Norwegian Meteorological Institute met.no

Schematic: Sources of prediction spread in a LAM

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

t=0

true development

”best forecast” model x

”best forecast” model y

t=60h

Tem

pera

ture

, p

recip

itati

on

, etc

.

ensemble mean model x

Ensemble mean model y

multimodel

ensemble mean

Warning: it’s not always this simple….

Norwegian Meteorological Institute met.no

In the multi-modal spread regime: Cluster or modal means in stead of ensemble means(Fig. by R. Hagedorn)

Forecast time

Tem

pera

ture

Complete description of weather prediction in terms of aProbability Density Function (PDF)

Initial condition Forecast

Norwegian Meteorological Institute met.no

Combining probabilistic forecasts from several models may give better scores than even the better of each models.

Example (Norwegian system):

ROC – Areaas a function of precipitationTreshold.

Black curve (NORLAMEPS)is a combination ofthe blue (LAMEPS) and the green (TEPS)

[The red is the standard ECMWF

EPS for reference.]

Norwegian Meteorological Institute met.no

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)

MeridionalCross-sectionalong 0 deg.From north poleTo 40 deg N

Norwegian Meteorological Institute met.no

Approach and aims during Approach and aims during HIRLAM-AHIRLAM-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

SRNWP-PEPSSRNWP – PEPS

(only a tiny common area)

Norwegian Meteorological Institute met.no

Quality objectiveQuality 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

Hirlam + Aladin +……+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

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

Initial Step:Initial Step:build on existing operational build on existing operational

experienceexperience • 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

OperationalOperational

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

Further onFurther on1. Through research to gradually increase ensemble

size and error-sourcesInclude 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

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

From climatePrediction.netUniv of Oxford & The Hadley CentreOnly cloud-related parameters are perturbed

Pdf of climate sensitivity:

dT at 2xCO2Based on

~50000ensemble members

Does Climate modelers and SRNWP-modelers have common challenges?

Norwegian Meteorological Institute met.no

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

Thank you for your attention

Mother of Pearl clouds over Oslo, January 2002