Transcript TIGGE research Richard Swinbank GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011 TIGGE Research Following the successful establishment of the TIGGE dataset, the main focus.

TIGGE research
Richard Swinbank
GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011
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 on dynamical
processes and predictability – for example, see
presentations in this meeting.
Up to the end of 2010, 43 articles related to TIGGE have
been published in the scientific literature
Multi-model ensemble compared with
reforecast calibration
Reforecast calibration gives comparable benefit to multi-model ensemble
Choice of verification data set (in this case, ERA-Interim) could have subtle
but significant effect on relative benefits
Calibration could further enhance benefit of multi-model ensemble
Renate Hagedorn
Uncalibrated precipitation forecasts
Probabilistic verification
Single model ensembles
Multimodel ensemble
 Based on ECMWF, UKMO, NCEP, 12 hour accumulations, 2 years data (autumn
2007 - autumn 2009) for UK region.
 Verified against UKPP composite data; thresholds taken from one-month 5x5
gridpoint ukpp climatologies
 Multimodel (pfconcat) has consistent slight advantage over single model ensembles
in resolution (solid) and reliability penalty (dotted)
 The overall Brier Skill Score (resolution-reliability) is negative for long lead times and
high thresholds
Jonathan Flowerdew, Met Office
Precipitation forecasts over USA
 24 hour accumulations, data from
1 July 2010 to 31 October 2010.
 20 members each from ECMWF,
NCEP, UK Met Office, Canadian
Meteorological Centre.
 80-member, equally weighted,
multi-model ensemble verified as
well.
 Verification follows Hamill and
Juras (QJ, Oct 2006) to avoid
over-estimating skill due to
variations in climatology.
 Conclusions:
 ECMWF generally most skillful.
 Multi-model beats all.
Tom Hamill
Comparison of extra-tropical cyclone tracks
Ensemble mean error:
Position
(verified against ECMWF
analyses)
Ensemble mean error –
Propagation speed
Propagation speed bias
Lizzie Froude, U. Reading
V(t) (log) variance
Spatiotemporal Behaviour of TIGGE
forecast perturbations
Indicates how spatial
correlation & localisation
vary as perturbations grow.
M(t) (log) perturbation amplitude
Kipling et al, 2011
North Atlantic eddy-driven jet “regimes”
 North Atlantic eddy-driven jet
profile is taken as
vertically/zonally averaged
low-level zonal wind in North
Atlantic sector (15-75N, 300360E)
 Split into three clusters S, M,
N using K-means clustering

X t  arg min Ut  U i
i S ,M , N

 Transition probability defined:
PA B ( )  P  X t   B X t  A 
Tom Frame, John Methven, U. Reading
Brier Skill Score: regime transition probabilities
3 years of TIGGE data for ONDJF (2007-2010), ECMWF, UKMO, MSC
MJO Forecast comparison
- ECMWF and UKMO have a superior
performance in simulating MJO.
- Predicted phase speed tends to be
slower than observed one.
- Predicted amplitude tends to be larger
than observed one.
Matsueda and Endo (2011, GRL accepted)
Tropical cyclone forecasts –
ensemble spread contradictions
ECMWF
NCEP
initiated at 12UTC 10
Sep. 2008
Sinlaku
(50 members)
Japan
Black line: Best track
at 00UTC 13 Dec.
2008
Grey lines: Ensemble
member
Dolphin initiated
(20 members)
Munehiko Yamaguchi
Philippines
Taiwan
Asymmetric
propagation
vector
Does not spread with time
Steering
vector
NCEP
Spread grows with time
T+48h
T+0h
ECMWF
SV-based perturbations better capture:
• Baroclinic energy conversion within a vortex
• Baroclinic energy conversion associated with mid-latitude
waves
• Barotropic energy conversion within a vortex
Comparisons of TC track forecasts
 NOAA developing EnKF for eventual operational use in hybrid EnKF/variational
data assimilation system.
 Early June 2010 through end of October 2010; verification against “best track”
information.
 Out-performs NCEP operational - differences are statistically significant.
 Also compares well with ECMWF (not shown)
24
Tom Hamill
How can we further increase impact of
TIGGE on research?
 Publicity
 New leaflet
 Website
 How to publicise better to universities?
 Scientific publications
 Conferences/meetings
 THORPEX symposia & regional meetings
 Other conference & workshops IAMAS, AMS, EMS, AGU…
 Communications
 tiggeusers mailing list hardly used
 What about social media: facebook, twitter…?
 How else?
TIGGE – next steps
 References on website
 Volunteer required
 Review Article on TIGGE research
 When?
 Additional data
 Stratospheric Network on Assessment of
Predictability (SNAP) – Andrew Charlton. Inviting
TIGGE providers to join as partners
Research needs and priorities
 Current emphasis
 Calibration and combination methods
 Bias correction, downscaling
 Multi-model ensembles; reforecasts
 Development of probabilistic forecast products – GIFS
development
 Tropical cyclones (CXML-based)
 Gridded data: heavy precipitation; strong winds
 Focus on downstream use of ensembles, rather than
on improving EPSs
Research needs and priorities
 But other important areas for EPSs include
 Initial conditions – link with ensemble data assimilation
(DAOS)
 Representing model error – stochastic physics (PDP, WGNE)
 Seamless forecasting – links with sub-seasonal forecasting
(new project)
 Convective-scale ensembles (TIGGE-LAM, MWFR)
 Fragmented approach, across several WGs.
 But these areas, particularly first two, are important for
improving EPS skill and products.
Virtuous Circle
Ensemble
Forecasts
To improve EPSs
we need to
develop a
virtuous circle –
best with a single
group with focus
on ensemble
prediction
Evaluate,
Diagnose
Develop,
Improve
Evolution of TIGGE & GIFS
TIGGE
development
Time
 The initial focus of GIFS-TIGGE WG was on establishing the
TIGGE database.
 We then broadened our scope to include downstream ensemble
combination, calibration & product development for GIFS.
 We should also use the WG as a forum to discuss R&D focused
on improving our EPS systems.