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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
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
Single model ensembles Multimodel ensemble
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
Lizzie Froude, U. Reading
Ensemble mean error: Position(verified against ECMWF analyses)
Ensemble mean error – Propagation speed
Propagation speed bias
Spatiotemporal Behaviour of TIGGE forecast perturbations
Kipling et al, 2011
M(t) (log) perturbation amplitude
V(t
) (l
og)
vari
anc
e
Indicates how spatial correlation & localisation
vary as perturbations grow.
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, 300-360E)
Split into three clusters S, M, N using K-means clustering
Transition probability defined:
, ,
arg min it ti S M N
X U U
( )A B t tP P X B X 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
Matsueda and Endo (2011, GRL accepted)
- 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.
MJO Forecast comparison
ECMWF (50 members)
Sin
laku
in
itia
ted
at 1
2UT
C 1
0 Se
p. 2
008
Dol
phin
init
iate
d at
00U
TC
13
Dec
. 20
08
Japan
Philippines
Taiwan
NCEP (20 members)
Black line: Best track
Grey lines: Ensemble member
Munehiko Yamaguchi
Tropical cyclone forecasts – ensemble spread contradictions
ECMWF NCEP
T+
0hT
+48
h
Sp
read
gro
ws
wit
h ti
me
Doe
s n
ot s
pre
ad w
ith
tim
e
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
Munehiko Yamaguchi
Steering vector
Asymmetric propagation
vector
24
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)
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 websiteVolunteer required
Review Article on TIGGE research When?
Additional dataStratospheric 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
Develop,Improve
Evaluate,Diagnose
Ensemble Forecasts
To improve EPSs we need to develop a virtuous circle – best with a single group with focus on ensemble prediction
Evolution of TIGGE & GIFS
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.
TIGGE development
GIFS Products
EPS improvement
Time