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

100000 10000 1000 100 10

2011/2012 TIGGE Archive Usage (All Portals)

150 60 30 120 90 Vol Accessed (GB) Vol Delivered (GB) # Active Users 1 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

NB. Now includes statistics from CMA

0

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.

-Verification for ECMWF EPS 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.

Benefit of Multi-model Grand Ensemble

Best SME (ECMWF) MCGE-3 (ECMWF+JMA+UKMO) 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.

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

Verification of ensemble spread

Verification of confidence information

Position 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).

Characteristics of TIGGE in forecasting ET events

- explore the benefit of multi-model approach Differences in Uncertainty – Standard devation of 500 hPa gph Sample case: Fcst during ET of Hurricane Ike, initialized 10 Sep 2008, 12 UTC longitude longitude

TIGGE (8 EPSs)

gpm 90 100 110 120 130 140 10 20 30 40 50 60 70 80 Surface position of Ike in members Analysis position of Ike at ET time

Courtesy Julia Keller

Regions Of Variability: EOFs Ensemble mean (color) and EOF pattern (contours) of 500 hPa geopotential height Fcst during ET of Hurricane Ike, EOFs at 15 Sep 2008, 12 UTC (+120h) TIGGE TIGGE - ECMWF ECMWF longitude Ike best track gpm

Julia Keller

Contributions of EPS to Clusters

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

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

Predictability of the 2010 Russian blocking

4 th August Wildfires brought heavy smog Moscow New maximum record of 39 ℃ !

1,600 drowning deaths!

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

Moscow

Mio Matsueda (2011, GRL)

Predictability of the 2010 Russian blocking

Ensemble-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

 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, M étéo-France, KIT  Involve other TIGGE partners in planning development & demonstration of products in conjunction with SWFDP.

GIFS-TIGGE 31 August - 2 September 2011

TIGGE Research needs & priorities

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

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

Evolution of TIGGE & GIFS

TIGGE development 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.

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