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

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in

itia

ted

at 1

2UT

C 1

0 Se

p. 2

008

Dol

phin

init

iate

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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

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ad w

ith

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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