David Parsons WWRP and THORPEX International...

1

1CAS MG3 October 2008

WW

RP

David Parsons

WWRP and THORPEX International Programme Office

(WMO)www.wmo.int/thorpex


WW

RP

Global Interactive Forecasting System –THORPEX Interactive Grand Global Ensemble Working Group

Zoltan Toth and Philippe Bougeault

GIFS-TIGGE WG

Data Assimilation and Observing Strategies Working Group

Florence Rabier and Pierre Gauthier

DAOS WG

Predictability and Dynamical Processes Working Group

Heini Wernli and Istvan Szunyogh

PDP WG

Regional CommitteesAfricaAsiaEuropeNorth AmericaSouthern Hemisphere

International Project Office

International Core Steering Committee

Executive Committee

THORPEX Organisational Structure

Links to the WWRP SERA and the Joint Verification WGs

Six weeks ago the Working Groups and the co-chairs ofThe Regional Committees met in Geneva to complete astrategic plan of work for the next few years

2


WW

RP

Predictability ANd Dynamics Of Weather Systems in the Atlantic-European Sector

SevereConvection

European Windstorms

MediterraneanCyclonesTropical

Cyclones

DFG funded Research Group PANDOWAE


WW

RP

THORPEX Highlights for WGNE THORPEX Interactive Grand Global Ensemble (TIGGE)

THORPEX Cluster under the International Polar Year

THORPEX Africa and the developing world

THORPEX covered elsewhere

T-PARC (next presentation -- D. Parsons)

Report from Data Assimilation and Observation System Working Group including targeting (Florence Rabier)

Year of Tropical Convection (WGNE Co-chairs)

Weather-climate interface and THORPEX’s role in seamless prediction (Gilbert Brunet)

Some aspects of TIGGE in Ensemble Forecasting presentation (Tom Hamill)

3


WW

RP

TIGGE partners and data flow

CAS MG3 October 2008

Slide 6

Slide 6

TIGGE objectives (agreed in March 2005)

Enhance international collaboration on ensemble prediction for severe weather

Collaboration between operational centres and universities

Develop theory and practice of multi-model ensembles

Examine the feasibility of interactive ensembles responding dynamically to changing uncertainty

Develop the concept of a Global Interactive Forecasting System (GIFS)

4


Slide 7

Slide 7

What is available now? Operational global ensemble forecasts from ten centres:

BoM (Australia), CMA (China), CPTEC (Brazil), ECMWF (Europe), JMA (Japan), KMA (Korea), Meteo-France, MSC (Canada), NCEP (USA), Met Office (UK)

Archives start between October 2006 and January 2008 (depending on centres)

The depth of the archive is reaching 2 years for the first providers

Work to be done, but many user oriented features.

Pressure level data + all usual surface fields (e.g. T2m, U10m, MSLP, rainfall) available from all providers

More “exotic” fields (e.g. CAPE, sunshine duration, etc…) available from some providers only, but improving regularly


WW

RP

Current Data Portals’ Functionshttp://tigge.ucar.eduhttp://tigge-portal.ecmwf.int/http://wisportal.cma.gov.cn/tigge/ Registration Search and discover facilities Select data by

Initial date/time and forecast time

Spatial sub-domain

Data provider

Atmospheric quantity and level Check volume and download data By agreement, access is open 48h after forecast start time Quicker access is possible in some cases (by

THORPEX/IPO determination)

5


WW

RP

Can MCGE Outperform the ECMWF Ensemble?

M. Matsueda and H. L. Tanaka -- SOLA, 2008.

Multi-Center Grand Ensembles (MCGEs) were constructed by combining five operational medium-range ensemble forecasts: CMC, ECMWF, JMA, NCEP, and UKMO with equal weights and no bias correction and compared to the ECMWF ensemble.

Seasonal Root Mean Square Error (RMSE) and the Ranked Probability Score (RPS) were derived for 500 hPa geopotential height over the Northern Hemisphere (20°N-90°N) from December 2006 to November 2007.

Deterministic and probabilistic verifications that the MCGEs generally outperformed the ECMWF ensemble at least in the medium forecast range (day 6-9) for all seasons.

The improvements in the RMSE and the RPS are several percentage points. These are almost comparable with the rate of improvement in a single-center ensemble forecast during the latest few years.


Slide 10

Slide 10

Recent research results based on TIGGE

Acknowledgments to

Young-Youn Park, KMA

Renate Hagedorn, ECMWF

Florian Pappenberger, ECMWF

Richard Swinbank et al., UK Met Office

6

Upper air variables

Significant differences in quality between the systems Up to 3 days differences in probabilistic forecast skill

Agreement between spread and skill is the most variable aspect and has a strong impact on probabilistic skill scores

In the Tropics the spread is underestimated by almost all systems

Impact of the verification analysis Relatively little impact in the extra-Tropics (as long as the analysis comes

from one of the best systems)

Large impact in the Tropics (and difficult to decide which is the best analysis)

Skill of multi-model system versus single-model systems Only marginal improvement in the extra-Tropics

Significant improvement in the Tropics (subject to significant bias corrections)


Slide 12

Slide 12

How about the surface weather?

Recently Johnson and Swinbank found that multi-model forecasts of T2m outperform significantly any single model EPS

Interpreted as a proof that the variety of physics (soil, vegetation, PBL) between the models captures better the uncertainty in surface parameters

Renate Hagedorn is currently trying to reproduce these results at ECMWF

Results depend strongly from the verification analysis (at variance from upper air variables)

Comparison with actual observations is necessary for T2m

Incidentally: T2m from TIGGE database at Fcst time=0 is NOT an analysis of T2m temperature (it is an “intelligent”vertical interpolation) - do not use it for verification!

7


Slide 13

Slide 13

Verification of T2m against observations

0 2 4 6 8 10Lead time / days

-0.2

0.0

0.2

0.4

CR

PS

S

Multi-ModelECMWF

Met OfficeNCEP

Solid: no BC

T-2m, 250 European stations2008060100 – 2008073000 (60 cases)


Slide 14

Slide 14


-0.2

0.0

0.2

0.4

CR

PS

S

Verification of T2magainst observations

Multi-ModelECMWF

Met OfficeNCEP

Dotted: 30d-BCSolid: no BC


8


Slide 15

Slide 15


-0.2

0.0

0.2

0.4

CR

PS

S

Verification of T2magainst observations

Multi-ModelECMWF

Met OfficeNCEP

Dashed: REFC-NGRDotted: 30d-BCSolid: no BC


Preliminary conclusions for T2m (very tentative!)

Results are sensitive to the choice of verifying analysis

Generally speaking, MM is better than any single model

Generally speaking, MM superiority comes from ECMWF,

and ECMWF alone is better than any MM without ECMWF

Calibration using recent forecasts reduces the superiority of

the MM but does not change the above conclusions

Calibration using a special set of re-forecasts may offset

completely the superiority of the MM (?)

The superiority of the MM may also be challenged if

uncertainty in soil moisture is added in the single systems

9


Slide 17

Slide 17

Additional work

Confirm results on longer time series, with more observations

Examine other weather parameters

Rainfall, 10m wind, clouds, etc….

Examine impact of multi-model on applications (end-to-end forecast systems)

Obvious example is with ensemble hydrological forecasts forced by TIGGE, and initial results are supporting superiority of MM

Use TIGGE MM as a benchmark to improve single-model systems

Real scientific progress would be to encapsulate all aspects of uncertainty in a single, optimal system: TIGGE can help us to locate and repair the deficiencies of existing operational EPSs

18

Multimodel Superensemble using TIGGE data over China

T. N. Krishnamurti, Anitha D. Sagadevan, Arindam Chakraborty & A. K. Mishra

Department of Meteorology, Florida State UniversityTallahassee, Florida USA

10

19

Multimodel Superensemble Experiment with TIGGE Data

Details of TIGGE Models:

for FSU multimodel suite only 5 members of TIGGE archive were used : ECMWF, UKMO, CMA, NCEP and BOM

Center Ensemble

Members

Model

Resolution

Forecast

Length

ECMWF 51 N200

(Reduced

Gaussian)

10 day

ECMWF 51 N128

(Reduced Gaussian)

10-15 day

UKMO 24 1.25 x 0.83 Deg 15 day

JMA 51 1.25 x 1.25 Deg 9 day

NCEP 21 1.00 x 1.00 Deg 16 day

CMA 15 0.56 x 0.56 Deg 10 day

CMC 21 1.00 x 1.00 Deg 16 day

BOM 33 1.50 x 1.50 Deg 10 day

MF 11 1.50 x 1.50 Deg 2.5 day

KMA 17 1.00 x 1.00 Deg 10 day

CPTEC 15 1.00 x 1.00 Deg 15 day

Forecast Period: May 2008 July 2008Training Period: February 2008 to April 2008

20

Model 3

Model 1

Model 4

Model N

Model 2

Train

ing T

ime S

eries

2)1( i

train

i ON

iSG

Multiple Linear Regression:

Training Phase

N

i

iii OFFaS1

)(

Foreca

sts

a1

a2

a3

a4

aN

Forecast Phase

Minimization of error term G.

Foreca

st Tim

e Series

Weights

Statistical weights obtained in the training phase are passed on to the forecast phase.

Superensemble Forecasts:

F => Forecasts O=>Observations ai => Weights. Overbar represents climatology.

In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.

11

21

Influence of Asian Monsoon (in Eastern side) and Tropical System Frank (Fengshen) in

Western side on 26th Jun 2008

22

RMS Errors and Spatial Correlations of Day 1 and Day 2 Precipitation Forecastsvalid on 26th Jun 2008

Tropical System Frank (Fengshen)

Day 1 Day 2

12

23

Onset of South China Sea Monsoon 2008

24

RMS Errors and Spatial Correlations of Day 3 and Day 5 Precipitation forecasts valid on May 3rd 2008

Onset of South China Sea Monsoon

Day 3 Day 5

13


WW

RP

EPS 2

EPS 6 EPS 1 EPS 3

EPS 5 EPS 4

EPS 7

ECMWF


WW

RP

EPS 1 EPS 2

EPS 3 EPS 4

EPS 5 EPS 6

EPS 7

ECMWF

14


WW

RP

EPS 6 EPS 1 EPS 7

EPS 2 EPS 4 EPS 3

EPS 5

ECMWF


Slide 28

Slide 28

The Future: 1. TIGGE-LAM (Limited Area Model)The general approach will be similar to TIGGE-

global: archive the operational forecasts of a large number of LAM-EPS systems

TIGGE archive centres have agreed to host this additional dataset

Access for research will be open 24h after forecast start time (vs 48h for TIGGE global)

Practical aspects still under discussion (grids, etc..)

Vision of adaptive LAM ensembles for developing based on early warning from global TIGGE

TIGGE-LAM led by Tiziana Paccagnella…

15


Slide 29

Slide 29

The Future: 2. The Global Interactive Forecast System (GIFS)

A document describing a vision of an “operational version of TIGGE”, the GIFS, has been agreed by the TIGGE partners and the THORPEX Exec Com last week

This will be submitted to the THORPEX International Core Steering Committee at its November 2008 meeting, then to CBS

Although we have agreed a roadmap, the implementation agenda is still very uncertain. A number of issues need to be solved before the operational forecast centres can agree to exchange forecasts in real time for operational multi-model products


Slide 30

Slide 30

GIFS issues Science: TIGGE results have not yet demonstrated unambiguously

the benefits of multi-model systems. More research is needed. As research progresses, the superiority of MM may decrease

Resource: Much hardware and manpower is needed to develop reliable exchange mechanisms for real-time production. Resources will be made available by the operational centres only if real benefits are expected, based on research results

Operational continuity: How to manage smoothly operational changes occurring at different times for the various components,guarantying smooth progress of MM skill and proper user information? The “interactivity” will be even more difficult

Data policy: Many TIGGE providers will want to protect their commercial revenues. Negotiations will be needed to agree a scheme satisfying all partners. Usage may be restricted to severe weather warnings.

16


Slide 31

Slide 31

A symbolic initial step towards the GIFS

Starting with severe weather products is a good idea!

Most TIGGE partners have agreed to exchange data on tropical cyclone tracks in real-time, for the whole duration of the T-PARC experiment (June 2008 - March 2009)

A special format (CXML) has been developed to make these data more easy to interpret and use by the academic community

Data are available in real time from providers or from NCAR archive at http://dss.ucar.edu/datasets/ds330.3/

More information on CXML project web site http://www.bom.gov.au/bmrc/projects/THORPEX/CXML/index.html


Slide 32

Slide 32

Key Message for WGNE TIGGE is a major research infrastructure and every effort has been made to make it user-

friendly -- However, efforts to develop software tools are underfunded at some archive

centres.

About 100 “serious” users currently: THIS IS NOT ENOUGH -- Any action that WGNE can

undertake to increase research usage and answering the research questions posed by

TIGGE would be appreciated.

Demonstration project based on TIGGE Tropical Cyclones tracks

A TIGGE Users’ Meeting will take place in the frame of the next THORPEX Conference

(Monterey, 4-8 May 2009)

Will include tutorials and discussions with users to get feedback and decide about priorities for service improvements

17


Slide 33

Slide 33

Acknowledgments

BoM: Beth Ebert

CMA: Shi Peiliang, Yang Xin, Lang Honglian, Tian Hao

ECMWF: Baudouin Raoult, Manuel Fuentes, Joerg Urban

JMA: Shintaro Yokoi, Yoshiaki Takeuchi

KMA: Young-Youn Park

NCEP: Zoltan Toth, Gordon Brent

NCAR: Doug Schuster, Dave Stepaniak, Nathan Wilhelmi, Luca Cinquini, Steve Worley

UKMO: Richard Swinbank, Simon Thompson

UNIDATA: Steve Chiswell, Tom Yoksas

FSU: T. N. Krishnamurti et al

Meteorologisk Institutt met.noMeteorologisk Institutt met.no

The IPY-THORPEX Cluster10 individual projects

(see WMO Bulletin Oct. 2007)

The objectives of the IPY-THORPEX Cluster proposal are:

– Explore use of satellite data and optimised observations to improve high impact weather forecasts (form a Polar Trec and/or provide additional observations in real time to the WMO GTS)

– Better understand physical/dynamical processes in polar regions

– Achieve a better understanding of small scale weather phenomena

– Utilise improved forecasts to the benefit of society, the economy and the environment

– Utilise of TIGGE for polar prediction

18


The WWRP-THORPEXIPY cluster

WWRP-THORPEX IPY Cluster(T.E. Nordeng, coordinator)

GFDexGreenland Flow

Distortion experiment(I. Renfrew, U. East Anglia)

STARStorm Studies of the Arctic(J. Hanesiak, U Manitoba)

ConcordiasiUse of IASI data

(F. Rabier, Meteo-France)

Norwegian IPY-THORPEX(J.E. Kristjansson, U Oslo)

Greenland Jets(A. Dombrack, DLR)

GREENEX(H. Olafsson, Iceland & DLR)

ARCMIPArctic Regional Climate

Model Intercomparison Project(K. Detholf, Alfred-Wegener Institute)

Impacts of surfaces fluxeson severe Arctic storms, climate change

and coastal orographic processes(W. Perrie, BIO Canada))

T-PARCTHORPEX Pacific Asian

Regional Campaign(D. Parsons, NCAR)

TAWEPITAWEPIThorpexThorpex Arctic WeatherArctic Weather

and Environmental and Environmental Prediction InitiativePrediction Initiative

((AyrtonAyrton ZadraZadra, , Environment Canada)Environment Canada)


Polar-GEM*: domain descriptionof RPN’s research model**

Fig.: Arctic sea-ice concentration climatology from 1978-2002, at the approximate seasonal maximum level. Image provided by National Snow and Ice Data Center, University of Colorado. http://nsidc.org/sotc/sea_ice.html

____________________________________

* GEM = Global Environmental Multiscale** Thanks to Bertrand Denis (MRD/EC)

LAM domain considerations

• No ice flow through lateral boundaries

• LBCs on mountain ranges (touchy)

• Zone of interest far enough from LBs

• Great Lakes not included

• Computation efficiency- Smaller domain = faster- Use of FFTs => restriction on NI

Polar-GEM (15km)

maximum sea-ice cover consideration

19



20


-38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18

temperature

1000

900

800

700

600

500

pres

sure

[hP

a]

total_HIRHAM

total_ARCSyM

total_COAMPS

total_RCA

total_REMO

total_RegCM

total_CRCM

ensemblemean

ecmwf

Temperature, winter, total

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

specific humidity [g/kg]

1000

950

900

850

800

750

700

pres

sure

[hP

a]

total_HIRHAM

total_ARCSyM

total_COAMPS

total_RCA

total_REMO

total_RegCM

ensemblemean

ecmwf

Specific humidity, winter

Remarkable scatter between model temperature and humidity profiles

(due to different radiation, cloud, PBL, and soil schemes)

Wrong surface fluxes, influence cloud formation and cloud properties

Temperature scatter in the order of 3°C (the range of climate scenarios) Key processes to be improved:

clouds, land surface- and boundary layer processes, coupling to ice-ocean

Intercomparison of temperature and humidity profiles for SHEBA domain, winter (Rinke, Dethloff et al., Climate Dyn., 2006)


WW

RP

African Regional CommitteeActivities

THORPEX Workshop and meetings of the Working groups,Geneva, 22-26 September

Aïda Diongue Niang and Andre Kamga

21


WW

RP

T-NAWDEXTHORPEX-

North Atlantic Waveguide and Downstream Impact Experiment

To be performed in 2011/2012 in conjunction with HYMEX

address the triggering of waveguide disturbances by different processes and the disturbances’ subsequent downstream evolution

study of the downstream impacts of the waveguide disturbances over Europe, the Mediterranean, and northern Africa


WW

RP

HYMEX: Main Scientific Topics

Better understanding of the intense events: processes and contribution to the trend

22


WW

RP

T-NAWDEX Planning

(1) Use HALO-Demonstration Mission HALO-THORPEXas nucleus for field phase


WW

RP

T-NAWDEX Planning

Planning Workshop Spring 2009

Formulation of the Science Plan scientific aims and questions methods to address the questions (theoretical,

numerical, experimental) responsible persons

Implementation Plan

National/International Cooperation and fund raising

23


WW

RP

First AnnouncementThird THORPEX International Science Symposium

and TIGGE User Workshop4 to 8 May 2009

Portola Hotel, Monterey, California

Organising Committee

Jim Hansen (NRL) - ChairIstvan Szunyogh (U. Maryland) – Programme ChairRolf Langland (NRL)Richard Swinbank (UK Met Office)Florence Rabier (Meteo France)Tetsuo Nakazawa (JMA/MRI)Huw Davies (ETH)

Gilbert Brunet (MSC)Eugene Poolman (SA Weather Service)Dan Hodyss (NRL)David Parsons (WMO)David Burridge (WMO)

www.wmo.int/thorpex

David Parsons WWRP and THORPEX International...

Documents

Transcript of David Parsons WWRP and THORPEX International...