Presentation on theme: "Trond Iversen met.no & Univ. Of Oslo"— Presentation transcript:
1Trond Iversen met.no & Univ. Of Oslo GLAMEPS: Grand Limited Area Model Ensemble Prediction System Towards establishing a European-wide TIGGE – LAM ?Trond Iversenmet.no & Univ. Of OsloBased 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.
2Contributions from partners – GLAMEPS First planning document exists - will be amended as new partners enterContributions from partners –R & D and operational
3GLAMEPS - Working Group established ad hoc at the Aladin-HIRLAM predictability planning meeting in Madrid, March 2006Jan Barkmeijer (KNMI), Jose Antonio Garcia-Moya (INM),Nils Gustafsson (SMHI), Andras Horanyi (HMS), Trond Iversen (met.no),Martin Leutbecher (ECMWF)
4The GLAMEPS objectiveis in real time to provide to all HIRLAM and ALADIN partner countries * :an operational, quantitative basis forforecasting probabilities of weather eventsin Europe up to 60 hours in advanceto the benefit of highly specified as well as general applications,including risks of high-impact weather.* List of partners should be extended !
5Why ensemble prediction? Why not use all resources to producethe ”best possible model” andthe ”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 propertyPredictability of free flows decreases with decreasing scales; i.e.: higher resolution increases the need for information about spread and the timing of spread saturation
6Why 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) forecastWhat are the risks of high-impact events?In a well calibrated EPS:Probable sources to forecast errors can be diagnasedForecasts beyond the predictability limit of pure atmospheric forecasts (monthly, seasonal, and longer) is impossible with a deterministic strategy.
7A. Murphy (1993): What is a good forecast? Consistency: correspondence between forecaster‘s best judgement and their forecastsQuality: correspondence between forecasts and matching observationsValue: benefits realised by decision makers through the use of the forecasts
8“100 year precipitation event” in the middle part of Norway Example: Norwegian 20 member LAMEPS (HIRLAM) and Targeted EPS (ECMWF IFS)>90mm/24h130mm/24h“100 year precipitation event” in the middle part of NorwayJanuary 2006
9probability x potential damage LAMEPSP24 > 60mmForecasted risk =probability x potential damageTEPSP24 > 60mm
10Schematic: Sources of prediction spread in a LAM Initial analysis and lateral boundariesLower and upper boundariesNon-linear filtering ofunpredictable componentsensemble mean better than control”best forecast” model xensemble mean model xTemperature, precipitation, etc.true developmentmodel xTruth outside spread model error?t=0t=60h
11Schematic: Sources of prediction spread in a LAM Initial analysis and lateral boundariesLower and upper boundariesmodel yslightly better than model xTemperature, precipitation, etc.true developmentEnsemble mean model y”best forecast” model yt=0t=60h
12Schematic: Sources of prediction spread in a LAM Initial analysis and lateral boundariesLower and upper boundariesNumerical approximations and parameterized physicsmultimodel”best forecast” model xensemble mean model xensemble meanTemperature, precipitation, etc.true developmentEnsemble mean model y”best forecast” model yWarning: it’s not always this simple….t=0t=60h
13In the multi-modal spread regime: Cluster or modal means in stead of ensemble means (Fig. by R. Hagedorn)TemperatureForecast timeInitial conditionForecastComplete description of weather prediction in terms of aProbability Density Function (PDF)
14Combining 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 thegreen (TEPS)[The red is the standard ECMWFEPS for reference.]
15Influence 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
16Approach and aims during HIRLAM-A An array of LAM-EPS models or model versions:Each partner runs a unique model versionEach partner runs between 5 and 20 ensemble members based on initial and lateral boundary perturbationsSome partners also perturb the lower boundary dataGrid resolution10 km or finer, 40 levels, identical in all model versionsForecast range 60h - starting daily from 12 UTA common integration domain (!), including:North Atlantic Ocean north of ca. 15 deg N.Greenland,European part of the ArcticEuropean continent to the Urals
17(only a tiny common area) SRNWP-PEPSSRNWP – PEPS(only a tiny common area)
18Quality objective To operationally produce ensemble forecasts with a spread reflecting known uncertainties in data and model;a satisfactory spread-skill relationship (calibration); anda better probabilistic skill than the operational ECMWF EPS;forthe chosen forecast range of 60 hours;our common target domain; andweather events of our particular interest(probabilistic skill parameters).
19Hirlam + 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.
20Experience 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 mainlywith 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
21Initial 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 atmospheredifferent 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 parametersAtlantic SSTSoil mostureTo construct initial/lateral boundary perturbations representative for trigging the ”instability of the day” given the uncertainty constraintsECMWF TEPS / EPS (build on met.no LAMEPS)Ensemble calibration (spread-skill-ratio)
22Operational To run a first phase suite at ECMWF (Special Project) Some scripts are readySome verification tools are readySome tools for probabilistic products are readyA possibility is to establish a ”PAF” (Prediction Application Facility) with ECMWF
23Further on1. Through research to gradually increase ensemble size and error-sourcesInclude other types of model and lower boundary perturbationsvary 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 perturbationsETKF generalized breeding (SMHI),HIRLAM and ALADIN LAM SVs (KNMI, SMHI, HMS),…To run a de-centralized system with real-time dissemination of data, or through a common central (e.g. ECMWF)Ensemble calibration in all phases
24Calibration: Spread-skill ratio Example from medium-range (T. Palmer) Too little spread:Model error missing?Too much spread:Wrong correctionof too small spread at day 2-3?
25Does Climate modelers and SRNWP-modelers have common challenges? From climatePrediction.net Univ of Oxford & The Hadley Centre Only cloud-related parameters are perturbedDoes Climate modelers and SRNWP-modelers have common challenges?Pdf of climate sensitivity:dT at 2xCO2Based on ~50000ensemble members
26Practical 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 , 2006
27Thank you for your attention Mother of Pearl clouds over Oslo, January 2002