Transcript ppt - WMO

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:
 Other members:
 Richard Swinbank (Met Office,
UK)
 Young-Youn Park (KMA, Korea)
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Italics indicate changes in past year
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Mike Naughton (BOM, Australia)
Osvaldo Moraes (CPTEC, Brazil)
Laurie Wilson (EC, Canada)
Gong Jiandong (CMA, China)
David Richardson (ECMWF,
Europe)
Philippe Arbogast (MétéoFrance, 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
150
10000
120
1000
90
100
60
10
30
1
0
Jan-11 Feb-11 Mar- Apr-11 May- Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 Feb-12
11
11
Month
NB. Now includes statistics from CMA
Number of Users (Count)
Volume (GB)
2011/2012 TIGGE Archive Usage (All Portals)
Vol Accessed (GB)
Vol Delivered (GB)
# Active Users
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 -1Strike 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).
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 at 3 day predictions
x axis: ensemble spread
y axis: position error of ensemble mean track
prediction
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 mediumspread cases, and in turn smaller than the average position error of largespread 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
gpm
Time
longitude
Surface position of Ike in members
longitude
Analysis position of Ike at ET time
TIGGE
(8 EPSs)
10
20
30
40
50
60
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90
100
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120
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140
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
latitude
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
4th August
Wildfires brought
heavy smog
The heatwave killed at least
15,000 people, and brought
wildfires, smog-induced
health injury, and huge
economic loss.
Moscow
New maximum
record of 39℃!
Moscow
1,600 drowning
deaths!
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, Météo-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
 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.
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