9 th GIFS TIGGE WG Geneva 31 Aug – 2 Sept 2011 TIGGE LAM Report Tiziana Paccagnella
TIGGE research Richard Swinbank GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011.
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Transcript of TIGGE research Richard Swinbank GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011.
![Page 1: TIGGE research Richard Swinbank GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011.](https://reader036.fdocuments.us/reader036/viewer/2022062417/551543fe55034685568b4fcc/html5/thumbnails/1.jpg)
TIGGE research
Richard Swinbank
GIFS-TIGGE Working Group meeting #9, Aug-Sep 2011
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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
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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
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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
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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
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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
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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.
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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
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Brier Skill Score: regime transition probabilities
3 years of TIGGE data for ONDJF (2007-2010), ECMWF, UKMO, MSC
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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
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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
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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
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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
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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?
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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
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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
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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.
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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
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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