Report from

GIFS-TIGGE working group

Richard Swinbank, and Young-Youn Park,

with thanks to the GIFS-TIGGE working group, THORPEX IPO and other colleagues

Presentation for WWRP/JSC5, April 2012

TIGGE and GIFS

Working Group TIGGE

TIGGE archive status TIGGE research

GIFS developments Examples of products based on TIGGE data Building links with SWFDP

GIFS-TIGGE working group

Co-chairs: Richard Swinbank (Met Office,

UK) Young-Youn Park (KMA, Korea)

Italics indicate changes in past year

Other members: Mike Naughton (BOM, Australia) Osvaldo Moraes (CPTEC, Brazil) Laurie Wilson (EC, Canada) Gong Jiandong (CMA, China) David Richardson (ECMWF,

Europe) Philippe Arbogast (Météo-

France, France) Tiziana Paccagnella (ARPA-SIM,

Italy) Masayuki Kyouda (JMA, Japan) Doug Schuster (NCAR, USA) Yuejian Zhu (NOAA/NCEP,

USA)

TIGGE project 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.

TIGGE Archive Usage

2011/2012 TIGGE Archive Usage (All Portals)

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NB. Now includes statistics from CMA

TIGGE Research

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.

Verification result of TC strike probability -1-

Strike prob. is computed at every 1 deg. over the responsibility area of RSMC Tokyo - Typhoon Center (0∘-60∘N, 100∘E-180∘) based on the same definition as Van der Grijn (2002). Then the reliability of the probabilistic forecasts is verified.Reliability Diagram

-Verification for ECMWF EPS-

In an ideal system, the red line is equal to a line with a slope of 1 (black dot line).

In an ideal system, the red line is equal to a line with a slope of 1 (black dot line).

The number of samples (grid points) predicting the event is shown by dashed blue boxes, and the number of samples that the event actually happened is shown by dashed green boxes, corresponding to y axis on the right.

Thanks to Munehiko Yamaguchi, MRI/JMA

Verification result of TC strike probability -2-

All SMEs are over-confident (forecasted probability is larger than observed frequency), especially in the high-probability range.

All SMEs are over-confident (forecasted probability is larger than observed frequency), especially in the high-probability range.

Best SME (ECMWF)MCGE-3

(ECMWF+JMA+UKMO)

MCGE-6 (CMA+CMC+ECMWF+JMA+NCEP+UKMO) MCGE-9 (All 9 SMEs)

Benefit of Multi-model Grand Ensemble

MCGEs reduce the missing area! The area is reduced by about 1/10 compared with the best SME. Thus the MCGEs would be more beneficial than the SMEs for those who need to avert missing TCs and/or assume the worst-case scenario.

MCGEs reduce the missing area! The area is reduced by about 1/10 compared with the best SME. Thus the MCGEs would be more beneficial than the SMEs for those who need to avert missing TCs and/or assume the worst-case scenario.

Verification of ensemble spreadV

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Verification of confidence informationPosition errors (km) of 1 to 5 day ensemble mean TC track predictions with small (blue), medium (orange) and large (red) ensemble spread. Each color has five filled bars, corresponding to the position errors of 1 to 5 day predictions from left to right.If a SME is successful in extracting the TC track confidence information, the average position error of small-spread cases is smaller than that of medium-spread cases, and in turn smaller than the average position error of large-spread cases. The frequency of each category is set to 40%, 40% and 20%, respectively (Yamaguchi et al. 2009).

If a SME is successful in extracting the TC track confidence information, the average position error of small-spread cases is smaller than that of medium-spread cases, and in turn smaller than the average position error of large-spread cases. The frequency of each category is set to 40%, 40% and 20%, respectively (Yamaguchi et al. 2009).

Differences in Uncertainty – Standard devation of 500 hPa gph

Sample case: Fcst during ET of Hurricane Ike, initialized 10 Sep 2008, 12 UTC

Time

gpm

102030405060708090

100110120130140

Surface position of Ike in members Analysis position of Ike at ET time

longitude

Characteristics of TIGGE in forecasting ET events- explore the benefit of multi-model approach

longitude

Courtesy Julia Keller

TIGGE (8

EPSs)

TIGGE TIGGE - ECMWF ECMWF

gpm

longitude

Fcst during ET of Hurricane Ike, EOFs at 15 Sep 2008, 12 UTC (+120h)

Ensemble mean (color) and EOF pattern (contours) of 500 hPa geopotential height

Ike best track

Regions Of Variability: EOFs

latitude

Julia Keller

Different contribution to EOF distribution

Distinct partitioning in clusters(development scenarios)

Clustering result for sample case Ike:

6 different clusters (colours)

Australia and Brazil contribute to one or two scenarios

Japan and ECMWF contribute to five of the six scenarios

Contributions of EPS to Clusters

Main conclusions

TIGGE contains broader variations and thus offers more possible development scenarios during ET than ECMWF

ECMWF is necessary to obtain full scope of variations

4th August

Moscow

Wildfires brought heavy smog

1,600 drowningdeaths!

Moscow

New maximum record of 39℃!

The heatwave killed at least 15,000 people, and brought wildfires, smog-induced health injury, and huge economic loss.

Mio Matsueda (2011, GRL)

Predictability of the 2010 Russian blocking

Predictability of the 2010 Russian blockingEnsemble-based occurrence probability of blocking (JJA 2010)

Blocking in early August, especially western part of blocking, showed a lower predictability than the other blockings. This indicates a difficulty in simulating maintenance and decay of blocking.

Towards a Global Interactive Forecast System (GIFS)

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

Flash floods/snow in South Africa (June 2011)

+ 8-day forecast

Mio Matsueda

Steps to progress use of GIFS products in SWFDP

Progress so far TC products based on CXML data; prototype products based

on gridded TIGGE forecast data Provided documentation of prototype products GIFS-TIGGE WG co-chair attended recent SWFDP SG

meeting Seek feedback from RSMCs coordinating SWFDP regional

subprojects Future

Develop real-time products for SWFDP based on preferred prototypes, e.g., Multi-model versions of TC products; near real-time versions of highest priority rainfall products.

Supply products to SWFDP regional websites Provide training via SWFDP

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 Research needs & priorities

So far, focus has been on downstream application of ensembles, rather than on improving EPSs. But other important areas for EPSs include Initial conditions – link with ensemble data assimilation (DAOS)

Representing model error – stochastic physics (PDP, WGNE)

Verification of ensemble forecasts (JWGFVR)

Seamless forecasting – links with sub-seasonal forecasting (new project)

Convective-scale ensembles (TIGGE-LAM, MWFR)

These areas, particularly first two, are important for improving EPS skill and products.

TIGGE is an invaluable resource for comparing both EPS techniques and systematic model errors, worthy of continuation into the future.

TIGGE development

Calibration, combination, products

EPS improvement

Time

Evolution of TIGGE & GIFS

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.

Summary Since October 2006, the TIGGE archive has been

accumulating regular ensemble forecasts from leading global NWP centres.

The TIGGE data set has been used for a wide range of scientific research studies (some examples shown).

Various products have been developed to use the tropical cyclone forecast data exchanged using CXML, and, more recently, prototype gridded products from the TIGGE data set.

The SWFDP regional centres will assess the prototype GIFS products for possible inclusion as real-time products on the SWFDP websites, and we will collaborate with them on implementation & evaluation.

TIGGE website: http://tigge.ecmwf.int