Upload
ariana-shaw
View
213
Download
0
Tags:
Embed Size (px)
Citation preview
The THORPEX Interactive Global Ensemble (TIGGE)
Multi-model ensembles and
Tropical cyclone forecasting
Richard Swinbank, with thanks to
the GIFS-TIGGE working group, THORPEX IPO and Met Office & other colleagues
Ensemble TC forecasting workshop, Nanjing, 14-16 December 2011
TIGGE and ensemble forecast products
TIGGE Objectives TIGGE archive
Ensemble forecasting Probabilistic forecasting Use of multi-model ensembles
Forecast products Plans for development of Global Interactive Forecast System Developing links with CBS/SWFDP Tropical cyclone forecast products
TIGGE THORPEX Interactive Grand Global Ensemble
A major component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts
GEO task WE-06-03 – “TIGGE and the Development of a Global Interactive Forecast System for Weather”
Objectives: Enhance collaboration on ensemble prediction, both internationally
and between operational centres & universities. Facilitate research on ensemble prediction methods, especially
methods to combine ensembles and to correct systematic errors Enable evolution towards a prototype operational system, the “Global
Interactive Forecast System”
TIGGE data Ten of the leading global forecast centres are providing regular
ensemble predictions to support research on predictability, dynamical processes and the development of probabilistic forecasting methods.
TIGGE data is made available for research after a 48-hour delay. Near real-time access may be granted for specific projects through the THORPEX International Project Office.
Summary of TIGGE database (late 2010)
CentreEnsemblemembers
Output dataresolution
Forecastlength
Forecasts per day
Fields (out of 73)
Start date
BOM* 33 1.50º x 1.50º 10 day 2 55 3 Sep 07
CMA 15 0.56º x 0.56º 10 day 2 60 15 May 07
MSC 21 0.9º x 0.9º 16 day 2 56 3 Oct 07
CPTEC 15 0.94º x 0.94º 15 day 2 55 1 Feb 08
ECMWF 51N200 (Reduced
Gaussian)N128 after day 10
15 day 2 70 1 Oct 06
JMA 51 0.56º x 0.56º 9 day 1 61 1 Oct 06
KMA* 17 1.00º x 1.00º 10 day 2 46 28 Dec 07
Météo-France 35 1.50º x 1.50º 4.5 day 2 62 25 Oct 07
NCEP 21 1.00º x 1.00º 16 day 4 69 5 Mar 07
UKMO 24 0.83º x 0.55º 15 day 2 72 1 Oct 06
* Delivery of BoM data currently suspended; KMA resumed Aug 2011
TIGGE Archive Usage(NCAR + ECMWF)2010-11 TIGGE Archive Usage (All Portals)
1
10
100
1000
10000
100000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
Month
Volu
me
(GB)
0
30
60
90
120
150
Num
ber o
f Use
rs (C
ount
)
Vol Accessed (GB)
Vol Delivered (GB)
# Active Users
Information about TIGGE
Major Article in BAMS
New leaflet to publicise TIGGE to researchers
Contribution in GEO book “Crafting Geoinformation”
Tropical cyclone case study in WMO Bulletin
TIGGE website
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. Up to the end of 2010, 43 articles related to TIGGE have been published in the scientific literature
Ensemble forecasting
time Forecast uncertainty
Climatology
Initial Condition Uncertainty
X
Deterministic Forecast
Analysis
Ensemble forecasting - effect of Chaos
The atmosphere is a chaotic system: “… one flap of a seagull’s
wing may forever change the future course of the weather”, (Lorenz, 1963)
Tiny errors in how we analyse the current state of the atmosphere lead to large errors in the forecast – these are both equally valid 4-day forecasts!
Up to about 3 days ahead we can usually forecast the general pattern of the weather quite accurately
Beyond 3 days Chaos becomes a major factor
Fine details (eg rainfall) have shorter predictability
With acknowledgements to Ken Mylne for general Ensemble Forecasting slides
Lorenz Model – a simple chaotic system
Variations in predictability can be illustrated using the Lorenz (1963) model:
X aX aY
Y XZ bX Y
Z XY cZ
Simple non-linear system.
Ensemble Forecasting in the Lorenz Model
1. Predictable - deterministic OK
2. Predictable at first -
probability OK
3. Unpredictable climatology OK
Thanks to Tim Palmer, ECMWF
Ensemble Forecasting
The aim of ensemble prediction system is to represent the uncertainty in the state of the NWP model at all stages through the forecast range
Each ensemble member is designed to sample a PDF representing uncertainties in the model state. The PDF is usually represented by a control run plus many perturbed forecasts. Initial condition perturbations are designed to represent
uncertainties in the initial analysis – closely linked to data assimilation. These grow with time as a result of the chaotic nature of model dynamics.
The uncertainty in forecasts also grows as a result of model error. This effect may be represented by perturbing model physics – e.g. stochastic physics schemes.
Examples of comparison of ensemble spread & error (from 2010 MOGREPS upgrade)
Compare
Red – old
Blue − new
Note:
Ensemble spread is less than RMS errors
New system both reduces RMS error & increases spread
Under-spread in surface temperature exaggerated because of representivity errors (point observations not representative of grid-square averages)
time
Estimated forecast uncertainty
Initial Condition Uncertainty
X
Deterministic Forecast
Analysis
Under-dispersive forecast
Real forecast uncertainty
The ensemble may capture reality less often than it should.Dangerous - false sense of security!
What makes a good ensemble?
Observed
Resolution – ability to distinguish between different events.
Reliability – the forecast probabilities match the associated observed frequencies.
Calibration – apply post-processing so that the statistics match those of a reference dataset. This might include bias-correction and variance adjustment.
The observed track should lie within the ensemble.
Met Office Multi-Model Ensemble
NCEP21 Member
ECMWF51 Member
Met Office 24 Member
3 variables:
MSLP
2m Temp
500mb Height
Met Office MME results courtesy
Christine Johnson
One approach to improving the calibration is to use multi-model ensembles
ECMWF
MultimodelNCEP
UKMO
Reliability Diagrams
Multi-model gives better reliability and reduced sharpness.
Probability that 2m temperature is greater than the climatological mean at T+240. Globally averaged.
Brier
Reliability
Resolution
Probability of 2m temperature greater than climatological mean.
Brier Skill Scores
Multi-model gives improvement in reliability and resolution at all lead times.
1 day
5 day
Dissimilarity of ensembles
betweenD
between MSE
D has large values (high dissimilarity, red) if the between-model variance is large compared to the mean-square-error of the multi-model mean.
mslp temp
D+2
D+10
D+2
D+10
time
Estimated forecast uncertainty
Initial Condition Uncertainty
X
Deterministic Forecast
Analysis
Multi-model ensemble forecast
real forecast uncertainty
Use of multi-model ensembles can improve the sampling of forecast uncertainty
Multi-model ensemble forecasts of T850
Demonstrates benefit of multi-model ensemble, provided that the most skilful models are used.
Renate Hagedorn, ECMWF
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
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
Summary - EPS & multi-model ensembles
Ensembles are valuable for forecasting the risks of exceeding thresholds (e.g. for high-impact weather events).
But forecasts often need calibration to correct both biases and variability (e.g. to correct under-estimates of forecast spread).
The best approach is to address the systematic errors, i.e. reduce model biases and improve the representation of model errors in the EPSs.
That is a long-term goal. In the mean time…. Use of multi-model ensembles is a pragmatic approach that
reduces calibration errors, especially where models have similar skill but different types of systematic error
Reforecasts (forecasts of past cases with current NWP models) can be used to estimate, and then correct, model biases
Towards the Global Interactive Forecast System (GIFS)
Many weather forecast situations are low probability but high risk – unlikely but potentially catastrophic. Probabilistic forecasting is a powerful tool to improve early warning of high-impact events.
The objective of the GIFS is to realise the benefits of THORPEX research by developing and evaluating probabilistic products to deliver improved forecasts of high-impact weather.
GIFS-TIGGE WG has initiated a GIFS development project to develop & evaluate products, focused on Tropical cyclones Heavy precipitation Strong winds
Tropical cyclones
As a first step, the GIFS-TIGGE working group set up a pilot project for the exchange of real-time tropical cyclone predictions using “Cyclone XML” format.
Example of combined TC track forecasts (Met Office + ECMWF)
CXML track exchange
GIFS links with SWFDP
EPS1 EPS2 EPS3
Generate Products
Regional Centre
users
National Centre
National Centre
National Centre
users usersusers
In-SituObservations
AirborneObservations
Satellite Observations
GIFS will collaborate with WMO Severe Weather Forecast Demonstration Project (SWFDP) and other FDPs and RDPs to ensure that products address
needs of operational forecasters and end users;
to provide an environment for the evaluation of prototype products.
GIFS will use global-regional-national cascade pioneered by the SWFDP. No single “GIFS centre”.
Use of web-enabled technology for generation and distribution of products.
Tropical cyclone products:Examples from Met Office
Uses Julian Heming’s code to identify and track tropical cyclones (TC), originally developed for the deterministic global Unified Model.
Tropical Cyclones (TCs) are identified where 850hPa relative vorticity (RV) maxima are greater than a threshold
For TC tracking, use search radius of 4 degrees for analysis and 5 degrees for forecast positions
Identified storms that do not match with a named storm or a TC identified at a previous time are counted as TC genesis
Met Office TC products courtesyPier Buchanan, Helen Titley and Julian Heming
MOGREPS-15 products for Ma-On, 12Z Friday 14th July 2011.
Left hand plot: 24 ensemble tracks Middle plot: strike probability – probability that the storm will be within 75 miles
within thhe next 15 days. Right hand plot: MOGREPS ensemble mean (blue), control (cyan), previous
observations (red) and deterministic track (green)
Products for named and potential storms on a basin by basin basis.
StormTracker Ma-On, 12Z Friday 14th July 2011.
Black – previous track White – deterministic model forecast Purple UKMO ensemble mean, mustard ECMWF ensemble mean and pink NCEP
GEFS ensemble mean.
TOMAS: MOGREPS-15 12Z North Atlantic forecast on Wednesday 27th October 2010.
Yasi MOGREPS Forecasts
Comparison of deterministic forecast with MOGREPS-15 ensemble mean
Comparison of MOGREPS, ECMWF and multi-model ensemble.
Tropical cyclone products from MRI/JMA http://tparc.mri-jma.go.jp/cyclone/
New tropical cyclone product:Strike probability time-series at a city
Tetsuo Nakazawa
Use of MOGREPS-15 products in SWFDDP
Warnings of heavy precipitation
Prototype product courtesy Mio Matsueda
Similar products also available for
• hot & cold temperatures,
• strong winds
Case 1: heavy rainfall by cyclone YASI (Feb. 2011)
+ 7-day forecast
TRMM dailyprecipitationexceeds the TRMM's 95th percentile.
Case 1: heavy precipitation by cyclone YASI
+ 6-day forecast
Case 1: heavy rainfall by cyclone YASI (Feb. 2011)
+ 5-day forecast
Case 1: heavy rainfall by cyclone YASI (Feb. 2011)
+ 4-day forecast
Case 1: heavy rainfall by cyclone YASI (Feb. 2011)
+ 3-day forecast
Case 1: heavy rainfall by cyclone YASI (Feb. 2011)
+ 2-day forecast
Case 1: heavy rainfall by cyclone YASI (Feb. 2011)
+ 1-day forecast
Other possible visualisation: combined warnings
Prototype product courtesy Mio Matsueda
ICDM workshop
Workshop on Dynamics and Predictability of High-Impact Weather Events and Climate Extremes
Kunming, China 6-9 August 2012 Organised by IAMAS International Commission on
Dynamical Meteorology Sponsored by WWRP, WCRP, IUGG, IAMAS,
Chinese IUGG committee, CAS, NSFC, LASG/IAP, Nanjing University & research projects.
Further information (first announcement) available from me.
Website: http://icdm2012.csp.escience.cn
Summary Since October 2006, the TIGGE archive has been
accumulating regular ensemble forecasts from leading global NWP centres.
The archive is a tremendous resource for the research community at large, particularly on EPS – including use of multi-model & other approaches to reduce systematic errors.
As the basis of the development of the future Global Interactive Forecast System, products are being developed to enhance the prediction of high-impact weather, starting with tropical cyclones.
GIFS products will be developed & evaluated in conjunction with SWFDP and other regional projects.
TIGGE website: http://tigge.ecmwf.int