Michel Béland, Président, Commission des sciences de l ...files.meteofrance.com/files/guides/expose_michel_beland_colloque_csm_2012.pdfMichel Béland, Président, Commission des

Michel Béland, Président,

Commission des sciences de l’atmosphère,Organisation météorologique mondiale.

Colloque du conseil supérieur de la Météorologie, le 11 mai 2012, Paris.

2012-08-02 MeteoFrance, Paris.


Near 90 % of disasters were caused by Weather-, Climate-, and Water-related hazards

Slides

5%Flood

30%

Extreme

Temperature

3.6%

Drought

7%

Earthquake

9%

Windstorm

25%

Wild Fires

3.5%

Epidemic, famine,

insects

14%

Tsunami

0.39%

Volcano

1.6%

Source: EM-DAT: The OFDA/CRED International Disaster Database - www.em-dat.net - UniversitéCatholique de Louvain-Brussels - Belgium


Augmentation des désastres naturelsAugmentation des désastres naturels



Denise Mauzeral, Princeton University, October 20, 2008.


� Le programme mondial de recherche en météorologie (PMRM, ou WWRP)

� THORPEX� WGNE� Le programme mondial de recherche en

climatologie (PMRC, ou WCRP)� La veille atmosphérique globale (VAG ou GAW)� Le programme de recherche environnementale et

de chimie atmosphérique (OPAG EPAC)


� IAMAS, IAPSO, IACS, IAHS (ICSU);� IGBP, DIVERSITAS, IHDP, IRDR (ICSU;)� IASC, SCAR (ICSU,…);� GCOS,GODAE, GOOS, SOOS, etc… (CIO, OMM);� GEO;� PROGRAMMES DE RECHERCHES NATIONAUX

OU RÉGIONAUX FP7, HORIZON 2020;� PROGRAMMES DES SERVICES

MÉTÉOROLOGIQUES ET CLIMATOLOGIQUES NATIONAUX.


EARTH SYSTEM MODELINGSEAMLESS MODELING SYSTEMSCOUPLED MODELING SYSTEMS.


Earth-System: Our Planet is a System of Systems

Acknowledgement to Randy Dole

Un nombre croissant de centres de prévision et de modélisation ont adopté une approche de modélisation “seamless”:

ECMWFMet Office, UKMeteoFranceBrazil (CPTEC)Meteorological Service of CanadaNCARCMA,Japan,Etc…


� Established models exist for most components� Modeling scales are converging

()

24 May 2011

� Philippe Bougeault, CG de la CAS� Véronique Ducrocq, WWRP JSC� Vincent-Henri Peuch, SDS-WAS� Florence Rabier, WGNE et THORPEX Europe (avec Olivier Talagrand)

� Philippe Abrogast, TIGGE� Jean-Pierre Ceron, Subseasonal-Seasonal Forecast Project

� Joel Stein, WG Verification� Jean-Pierre Chalon, ET WM


� TIGGE et GIFS� Assimilation des observations (DAOS)� Prévision immédiate (Nowcasting)� Modélisation à échelles fines (Grey Zone)� Techniques de vérification des prévisions� Prévisions infra-saisonnières et saisonnières� Prévisions polaires� Couplages, océans, bassins hydrologiques, etc � INCA, MDP, FROST, etc…


� Since 2006, TIGGE has been collecting ensemble predictions from 10 of the leading global forecast centres.

� TIGGE data are made available after a 48-hour delay, to support research on probabilistic forecasting methods, predictability and dynamical processes.

� 50+ TIGGE articles published in scientific literature.

2011/2012 TIGGE Archive Usage (All Portals)

1

10

100

1000

10000

100000

Jan-11 Feb-11 Mar-

11

Apr-11 May-

11

Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 Feb-12

Month

Vo

lum

e (

GB

)

0

30

60

90

120

150

Nu

mb

er

of

Use

rs (

Co

un

t)

Vol Accessed (GB)

Vol Delivered (GB)

# Active Users

NB. Now includes statistics from CMA

Following the successful establishment of the TIGGE dataset, the main focus of the GIFS-TIGGE working group has shifted towards research on ensemble forecasting. Particular topics of interest include:

� a posteriori calibration of ensemble forecasts (bias correction, downscaling, etc.);

� combination of ensembles produced by multiple models;

� research on and development of probabilistic forecast products.

TIGGE data is also invaluable as a resource for a wide range of research projects, for example: comparing different Ensemble prediction systems; research on dynamical processes and predictability. Currently, over 50 articles related to TIGGE have been published in the scientific literature.

� Our objective is to realise the benefits of THORPEX research by developing and evaluating probabilistic products.

� Focus on risks of high-impact weather events – unlikely but potentially catastrophic.

� First step: exchange of real-time tropical cyclone predictions using “Cyclone XML” format.

� Followed by development of products based on gridded forecasts of heavy precipitation & strong wind.

Piers Buchanan, Met Office

GIFS-TIGGE 31 August - 2 September 2011

� GEOWOW (GEOSS interoperability for Weather, Ocean and Water) is a 3-year EU-funded FP7 project starting September 2011.

� The Weather component includes: � improving access to TIGGE data at ECMWF.� developing and demonstrating forecast products.

� Weather participants: ECMWF, Met Office, Météo-France, KIT� Involve other TIGGE partners in planning development &

demonstration of products in conjunction with SWFDP.

TIGGE development

Calibration, combination, products

EPS improvement

Time

� We propose that the GIFS-TIGGE should also be a forum to focus on R&D directed at improving our EPS systems, to help us develop a “virtuous circle”.

� We will have a section of future WG meetings for discussing ensemble initial conditions, stochastic physics & other aspects of improving our EPSs.

� We will also maintain an interest in ensemble verification and links with convective-scale EPS and the new sub-seasonal to seasonal group.

METOP : MetOp ATOVS,MetOp IASI, MetOp ASCAT NOAA : NOAA15 ATOVS AMSUA, NOAA17 ATOVS HIRS, NOAA18 ATOVS, NOAA19 ATOVSOTHER LEO: EOS AIRS, F16 SSMIS, ERS, WINDSATGEO : GOES, MTSAT, MSGAircraft : AMDAR, AIREPSONDE : PILOT, TEMPSFC Land : SYNOP, BOGUSSFC Sea : BUOY,SHIP

Total Impact = Number of soundings/profiles * mean observation Impact of each sounding/profile

Observation Impacts to NWP forecast

-16-12-8-40

Obs

erva

tion

Typ

es

Total Observation Impact[J/kg]

METOP

NOAA

OTHER LEO

GEO

AIRCRAFT

SONDE

SFC LAND

SFC SEA

Impact of different observation platformsfrom forecast sensitivity diagnostic

Relative Contribution of Observations to NWP forecast

3.1

15.3

13.2

9.9

5.9

7.9

20.4

24.3

0 5 10 15 20 25 30

Obs

erva

tion

Typ

es

Relative Observation Impact[%]

METOP

NOAA

OTHER LEO

GEO

AIRCRAFT

SONDE

SFC LAND

SFC SEA

ASSESS THE CURRENT AND POTENTIAL CAPABILITIES OF WEATHER RADARS FOR THE USE IN WMO INTEGRATED GLOBAL OBSERVERING SYSTEM (WIGOS )by Ercan Büyükbaş, Turkish State Meteorological Service (TSMS)

Radars now used to Verify NWP model Precipitation forecasts

Need to advocate a common format worldwide to enablewider verification of precipitation

� ASCAT winds for Irene and model background

� Only one scat now used for NWP

� Trials using scatterometer on Oceansat-2

A High-Resolution Land-Surface-Hydrological Coupled System

Land, Urban (Land-Water Interface)

Hydrology

TopographyAtmosphere

Atmosphere-Surface Interaction --- Very Important: Weather and Environment

Montréal

St-Lawrence River

Châteauguay

river basin

Canada

USA

Ottawa River

GFS

grid point

Watershed area: 2500 km³Montréal

St-Lawrence River

Châteauguay

river basin

Canada

USA

Ottawa River

GFS

grid point

Watershed area: 2500 km³

HydrologyVegetation

Forecast periodForecast period

Forecast accuracyForecast accuracy

2h2h 6hr6hr 1day1day

ExtrapolationExtrapolation

Limit of deterministic forecast Limit of deterministic forecast NowcastingNowcasting

Short range forecast for precipitation

Cloud resolving model and data assimilation

Reduce the gap Reduce the gap between nowcasting between nowcasting and NWP by highand NWP by high--resolution data resolution data assimilation assimilation

Current NWP model

� NWP going to a few hundred kilometers to improve first few hours

� Ingestion of radar reflectivity considered progress but few models can do this

� High resolution ensembles are in their infancy

� Blending is the commonality to produce 6 hour nowcasts

� Indirect use of model need study


� subgroup from WGNE, WG-MWFR, GCSS tasked to set up idealized grey zone experiment

� Idea: Set up basic experimental framework and gain participants for initial “simple” extratropical case of cold air outbreak over sea, in 2011-2012. Later extend this framework and modelling community to other cases, including e.g. deep convection in tropics, open cell convection over land.

� Status: Interest expressed by ~10 modelling groups. Case prepared, ready for release in May 2012. First results to be discussed in Pan-GASS meeting, Sep 2012.

JSC meeting, 11-13 April 201230

© Crown copyright Met Office

� The proposed WWRP/THORPEX-WCRP joint research project to improve forecast skill and understanding on the subseasonal to seasonal timescale will require:

� The establishment of a project Steering Group representing both the research and operational weather and climate communities. The steering group will be responsible for the implementation of the project;

� The establishment of a project office to coordinate the day to day activities of the project and manage the logistics of workshops and meetings;

� The establishment of a multi-model data base consisting of ensembles of subseasonal (up to 60 days) forecasts and supplemented with an extensive set of reforecasts following TIGGE protocols. A workshop will be necessary to address several technical issues related to the data base;

� A major research activity on evaluating the potential predictability of subseasonal events, including identifying windows of opportunity for increased forecast skill. Attention will also be given to the prediction of intraseasonal characteristics of the rainy season which are relevant to agriculture in Africa and Asia.

� A series of science workshops on subseasonal to seasonal prediction. The first topic identified is "Sources of predictability at the subseasonal timescale- windows of opportunity for applications";

� Appropriate demonstration projects based on some recent extreme events and their impacts, in conjunction with the WWRP SERA


� Verification

� strengthening of verification activity utilizing operational andresearch data bases such as the TIGGE data bases is needed.

� Data Assimilation and Observation

� the establishment of the utility of existing surface based and satellite observations through data assimilation experiments (e.g. CONCORDIASI project);

� Predictability and Physical and Dynamical Processes

� There is a need for concerted physical process studies which will need new field campaigns;

� We need to establish well thought out numerical experiments with coupled models in the Polar Regions in collaboration with WCRP (CMIP5, SPARC);

� More efforts need to focus on research and development for coupled atmosphere–hydrological–cryosphere–surface modelling and observation.

� Planetary Boundary Layer (PBL) parameterization including PBL clouds (e.g. low visbility)

� The underlying surface and the need for surface–sea-ice coupled models

� Need for an accurate and detailed description of the underlying surface in terms of ice, snow, leads, polynyas and tides and sea-ice characteristics and sea surface temperature;

� There is a need for detailed process studies and careful parameterizations supported by observations (e.g blowing snow).

EXTERNAL HIGH-RESOLUTION LAND SURFACE PREDICTION SYSTEM

ATMOSMODEL

3D INTEGRATION

ExternalLand SurfaceModel

With horizontal resolution as high as that of surface databases (e.g., 100 m)

ATMOSPHERIC FORCING at FIRST ATMOS. MODEL LEVEL (T, q, U, V)

2D INTEGRATION

Computational cost of off-line surface modeling system is much less than an integration of the atmospheric model

ATMOSPHERIC FORCING at SURFACE (RADIATION andPRECIPITATION)

LOW-RES

HIGH-RES

EXAMPLE 1: URBAN HEAT ISLANDS

Urban offUrban off --line line modeling systemmodeling systemResolution: 928 m→ upscaling

MODIS MOD11A1 productResolution: 1km

(exactly 928 m)� Atmospheric effects corrected� Satellite View Angle : 15°

Radiative Surface Temperature (°C)July 6th 2008 (10:54 LST)

EPS

Land surfacescheme and

hydrological model

MESH

mass, energy andmomentum fluxes

prognostic variables,e.g. soil moisture

streamflowand lake levels

CMC EPS

H-EPS member

100 150 200 2500

0.005

0.01

0.015

0.02

0.025

0.03

0.035

P DF vs his togra m

100 150 200 2500

0.2

0.4

0.6

0.8

1CDF

Pro

ba

bilit

y

100 150 200 2500

0.005

0.01

0.015

0.02

0.025

0.03

0.035

Adaptive vs Normal ke rnel

AdaptiveNormal

100 150 200 2500.0000

0.0013

0.0228

0.1587

0.5000

0.8413

0.9772

0.9987CDF (normal probability paper)

Pro

ba

bilit

y

Smoothed ensemble forecast ofmean monthly outflow

for Lake Superior

28/2/03 7/3/03 14/3/03 21/3/03 28/3/03 4/4/03 11/4/03 18/4/03 25/4/03 2/5/03

Date

0

40

80

120

160

200

For

ecas

t flo

w B

lack

Was

h R

iver

(cm

s) ObservedEnsemble 1 to 16Deterministic

Meterological Service of Canada Ensemble run March 29, 2003

1/3/03 8/3/03 15/3/03 22/3/03 29/3/03 5/4/03 12/4/03

80

100

120

140

160

Lak

e O

nta

rio

Net

Infl

ow

(cm

s)

Streamflow forecastBlack River near Washago Lake Ontario level

Hydrological spaghetti plots

4


Andy Brown and Christian JakobWGNE co-chairs

• Working Group on Numerical Experimentation • Jointly established by the WCRP and the WMO Commission for

Atmospheric Sciences (CAS)• Responsibility of fostering the development of atmospheric circulation

models for use in weather prediction and climate studies on all time scales and diagnosing and resolving shortcomings.

� A distillation of the Terms of ReferenceAdvice, liaison

� Co-ordinated experiments� Workshops, publications, meetings


� Transpose-AMIP GOOD PROGRESS� SURFA SLOW PROGRESS

� Cloudy-radiance DONE� Grey-zone GOOD PROGRESS� Verification

� NWP performance (eg TCs, precipitation) ONGOING

� Polar (CBS-style; ConcordIASI intercomparsion) NEW

� Climate metrics GOOD PROGRESS� Issues with verifcation against own analysis

NEW


� Core experiment is to run 64 hindcasts, each 5 days long, initialised from ECMWF YOTC analysis.

� Optional experiment to repeat the same set of hindcasts with NASA MERRA re-analysis or own analysis.

� The hindcasts spread through the annual and diurnal cycles and chosen to tie in with YOTC and coincide with some of the IOPs in:

� VOCALS (SE Pacific stratocumulus)� AMY (Asian monsoon)� T-PARC (mid-latitude Pacific)

� 9 centres committed to submit data� MIROC5, HADGEM2, CNRM-CM5 now available to download


green : wrt own analysispink : wrt ECMWFBlue : wrt radiosondesRed : wrt MO analysisPurple : wrt ECMWF profiles at radiosonde locations

Best scores wrt own analysisSimilar results wrt ECMWF analysis globally or projected on radiosonde locations: discretisation by radiosonde network sufficient to capture main errorsWorse scores wrt radiosondes: difference in scales and quality of analysis Convergence of scores after a few days, depending on the model

Issues with verification against own analysis

Being taken forward bysmall group led byTom Hamill, Laurie Wilson, and Jean-Noel Thepaut

Ghassem R Asrar, Director, WCRPAntonio J. Busalacchi, chair JSC, WCRP

� WWRP/WCRP Planning Group for a “Sub-seasonal prediction research project” established (MJO TF, stratosphere processes, land IC, coupling and DA, parameterization & uncertainties, GHG, YOTC, etc)

� WWRP/WCRP Steering Group for a “Polar Weather Prediction Research Project” being established (leveraging IPY legacy)

DÉCISIONS RÉCENTES

� Sophie Godin-Beekmann, (LATMOS) SAG Ozone

� Paolo Laj, (LGGE) SAG Aérosols� Phiulippe Goloub, (LOA) SAG AOD


Network or System (in situ)

Essential Climate Variables (ECVs)

International Data Centres and Archives

Coordinating Bodies

GCOS-affiliated

WMO/GAW Global

Atmospheric CO2 and

CH4 Monitoring

Networks

• Carbon dioxide• Methane

• WDC-GG (JMA)• Carbon Dioxide Information

Analysis Centre (Oak Ridge National Laboratory)

WMO CAS

• WMO/GAW GCOS

Global Baseline

Total Ozone

Network

• WMO/GAW GCOS

Global Baseline

Profile Ozone

Network

Ozone

• WOUDC (MSC)• Network for Detection of

Atmospheric Composition Change (NDACC) Archive

• Norwegian Institute for Air Research

• Southern Hemisphere Additional Ozonesondes (SHADOZ –NASA) Archive

WMO CAS

WMO/GAW Aerosol

NetworkAerosol properties

• AERONET, SKYNET, BSRN and GAWPFR data centres

• World Data Centre for Aerosols (NILU)

WMO CAS

� Aérosols (applications météorologiques, climatologiques, qualité de l’air, etc…).

� Qualité des environnements urbains.� Bilan carbone (Carbon Tracking).� Carbone suie (Black Carbon)� Polluants toxiques.


� GAW is responsible for GCOS Essential Climate Variables for "Aerosol Properties"

� Aerosol optical depth is a high-priority ECV� GAW SAG-Aerosol is developing an

implementation plan for a GCOS AOD network

� Essential components of GCOS AOD network:� network of networks� regional calibration facilities� travelling standard (precision filter radiometer)� overlap sites (multiple networks at some sites)� data publication at World Data Center for Aerosols

J. Ogren 2011-04-26

� Regular measurement scheme implemented� emphasis on establishing a climatology

� GALION stations included in GAWSIS

� Special event alerting system established

� Second GALION workshop held in Sept. 2010� 80 participants (representatives of the lidar networks, satellite and modelling communities)

� 2-year implementation plan for working groups;

J. Ogren 2011-04-26

Quicklooks made available almost in near real time on the EARLINET website

J. Ogren 2011-04-26

• Develop a three-dimensional global atmospheric chemistry measurement network

• Develop coherent data processing chains

• Implement near-real-time delivery of a few measured parameters

• Assimilate data into models• http://www.wmo.int/pages/prog/arep/gaw/documents/gaw172-26sept07.pdf

J. Ogren 2011-04-26

Influenza forecast

Heat wave and cold spell forecast

Pollen forecast

Heat index, Sunstoke, and Diarrhea forecast for EXPO 2010

UV forecast

Un système opérationnel intégré de prévision environnementale pour Shanghai, EXPO 2010

Bacterial Food Poisoning

Ozone forecast

Haze forecast

Forecast models

Observations

Bacterial food poisoning, Influenza,Heatstroke, Trauma, Diarrhea diagnostic

UV radiation

Meteorological measurements (temperature, wind, humidity, pressure, cloud, etc)

Pollen measurements (open plat method, Microscope filter)

SporeWatch electronic spore & pollen sampler

Atmospheric chemistry observation (O3, NOX, CO, SO2, aerosols)


Page 56

Globalview,

(annual)

Data sets &

Visual displays

(variable)

Interactive Data

Visualization (daily)

N O A A A n n u a l G re e n h o use G a s In d ex

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Rad

iativ

e F

orci

ng (

W m

-2)

0 .0

0 .5

1 .0

1 .5

2 .0

2 .5

3 .0C O 2

C H 4

N 2O

C F C 1 2C F C 1 110 M in o r

Ann

ual G

reen

hous

e G

as In

dex

(AG

GI)

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4

Greenhouse Gas

Index (annual)CarbonTracker

(annual)

Global trends

(monthly)

DATA Products

Services


Carbone suie et forçage radiatif

BC Radiative Forcing is significant+0.34 W/m2 [Forster et al., 2007] -+0.9 W/m2 [Ramanathan and Carmichael, 2008]. –+ 0.05 W/m2 Black Carbon on snow

Locally, BC forcing can be much higher•Schreekant et al., 2007, + 40 W/m2 for wintertime in Northern India•Marcq et al., 2010 : + 12 W/m2 Nepal Pyramid Observatory (5100 m)

BC forcing dependent upon injection height in models : BC RF increases by 70% if injected at 5 km in a global model (Haywood and Ramaswamy, 1998)

Carbone suie: un outil pour les discussions post-Kyoto…

A likely candidate for linking climate and air quality policies

Main emission sources: - Biomass combustion- Other man-made

combustion- Transport

BUT still a lot of uncertainties •Variability between emission inventories linked to emission factors Differences between from -30 to +120 % (man-made emissions) and -50 to +200% (biomass burning).

•Limited information from in-situ observations

Result for Global Temperature Change (hybrid of results from GISS and ECHAM models informed by the literature) added to the historical record

� Augmentation des impacts environnementaux, dans tous les secteurs d’activité, pour les raisons présentées dans la première partie de cette présentation.

� Une accélération de ceux-ci dans certaines régions du globe (régions polaires, côtières, sub-tropicales).

� Nécessité croissante d’une approche multidisciplinaire et intégrée (seamless) dans la modélisation .

� Nécessité croissante d’une obligation de résultats pour les grands programmes de recherche internationaux en météo et climat, et en particulier pour les services météorologiques nationaux.

� Nécessité croissante d’une étroite collaboration entre les différents acteurs scientifiques, nationaux et internationaux, en partie causée par les coûts élevés pour le maintien de systèmes opérationnels de type “earth system”, ainsi que les coûts des infrastructures de calculs et d’observations.

Documents

Michel Béland, Président, Commission des sciences de l ...files.meteofrance.com/files/guides/expose_michel_beland_colloque_csm_2012.pdfMichel Béland, Président, Commission des