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WMO/CAS/WWW

SEVENTH WMO INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES Keynote Topic 2: Tropical Cyclone probabilistic forecasting and related product development issues & applications for user risk assessment Co-chairs: Gary Foley Dr Chris Landsea

Ten Sixteen and Falling Pty Ltd NOAA/NWS/NHC

6 Debenham Way 11691 SW 17th Street

Hillarys WA 6025, Aust. Miami, FLA 33165-2149, USA

Email: gusf1016@bigpond.com Chris.Landsea@noaa.gov

Fax: 1-305-553-1901

Working Group Members: Beth Ebert, Tetsuo Nakazawa, Steve Ready, Richard Swinbank, and

Zoltan Toth

Abstract

This report primarily looks at progress that has been made in respect of probabilistic forecasting

(with emphasis on tropical cyclones) particularly since IWTC-VI (2006). It draws the distinction

between statistical-dynamical techniques and ensemble methods that have been developed using

numerical weather prediction (NWP) models, mainly by the major global centres. An array of forecast

guidance is discussed that is either available or being developed to assess aspects of tropical cyclones

including track prediction, intensity, wind speed distribution, rainfall, storm surge and genesis. The

World Meteorological Organization (WMO) has, through its association with major scientific projects

such as THORPEX and by working with other organisations, taken a strong role in coordinating the

further development and promulgation of probabilistic products into forecasting centres. These

activities and organisational links form part of this report. Issues remain in respect of probabilistic

forecast products that need to be addressed. They concern the need to ensure that users of

probability products understand both their effectiveness and their limitations; that products are

verified in a meaningful way; that there is a closer relationship between product developers and

forecasters to achieve the best outcomes; and that National Meteorological and Hydrological

Services (NMHS) position themselves to further extend in an ordered and systematic way, probability

guidance to disaster reduction agencies and to the community.

2.1.0 INTRODUCTION

Standard operating procedures for Tropical Cyclone Warning Centres dictate that warning messages

to emergency managers and the at-risk community contain deterministic values for the important

parameters of the storm. The warning needs to inform that a tropical cyclone has been located at a

specific location and that it will be located at specific positions at future times, while describing

various intensity characteristics such as wind distribution, central pressure, or severity category,

with the intention that the level of threat being faced can reasonably be gauged. Some information

indicating possible errors due to the uncertainty that is inherent in this location and forecasting

process may be included in the message; the values sometimes quantified from past operational

performance, sometimes estimated by the forecaster on the basis of the current circumstances.

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Tropical cyclone forecast performance has steadily improved over time, particularly with the advent

of more sophisticated remote sensing systems for location and intensity information, with improved

numerical weather prediction (NWP), and with greater scientific understanding. While these factors

have led to more effective warning systems, it is widely recognized that the science of meteorology

will always be cloaked in uncertainties in both measurement and prediction. In this context, tropical

cyclone forecasters need to be aware of the uncertainty confronting them throughout the life cycle

of every tropical cyclone and be able to convey the real confidence associated with each warning

that is issued. As experienced forecasters would know, the burning question for emergency

managers and decision-makers is, “How confident are you with this forecast?” particularly when the

future movement of the tropical cyclone takes it into an area of potential critical impact.

There are two major approaches to probability forecasting. The first has been used and refined for

decades, and entails the use of statistical techniques to analyse historical observational and forecast

performance data to provide an insight into the likely spread of uncertainty for similar forecast

situations. The second approach, employing the concept of ensembles, has developed in conjunction

with the advances made in NWP and the increasing availability of high speed computers that have

capacity to run multiple prediction scenarios using perturbation algorithms to define slightly

different initial state conditions. A variation to perturbing a single model a number of times is the

technique of using output of the same event (such as a tropical cyclone) from a number of

independent deterministic models to achieve a similar result. This technique is often referred to as a

“Poor Man’s Ensemble” (PME). A further variation is a combination of single-model ensemble

predictions to make a grand multi-model ensemble (MME).

This report examines recent developments in probability forecasting with emphasis on their

application to tropical cyclones and those elements of cyclones that impact socio-economically such

as destructive winds, storm surge, and heavy rainfall/flooding. It highlights a range of products that

are either available or being trialled and developed for implementation, and it cites the major

activity centres involved in probability applications as well as projects that are trialling these

products. It also looks at issues associated with probabilistic forecasting that need to be resolved in

order for more widespread acceptance of such approaches by forecasters, emergency managers and

communities threatened by tropical cyclones.

2.1.1 Recommendations from IWTC-VI

Participants at the WMO Sixth International Workshop on Tropical Cyclones (IWTC-VI) in 2006

recognised both the value of statistical methods and the emergence and potential of ensembles, and

made a number of major recommendations to encourage further work (WMO, 2007):

• The WMO should take all necessary action to:

a) Improve the communication between operational centres and facilitate the dissemination of

all tropical cyclone-related NWP products, such as the deterministic and ensemble forecasts

(including the full set of ensemble runs), and

b) Make them available to all RSMCs, TCWCs and researchers in real-time.

• NWP centres should verify their forecasts (including probabilistic forecasts) and document

their performance in a common standard format...

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• IWTC-VI strongly recommends that greater efforts be put into intensity and structure

prediction of tropical cyclones. The development of dynamical models, including coupled

ocean-atmosphere models, statistical-dynamical models, and all methodologies aimed at

improving the skill in intensity and size prediction (and resulting wind and rainfall fields)

should be strongly encouraged.

• IWTC-VI considers that the tropical cyclone community should engage and cooperate with

the THORPEX Pacific Asian Campaign (T-PARC) and the THORPEX Interactive Grand Global

Ensemble/Global Interactive Forecast System (TIGGE/GIFS), which aims in particular to

develop generic probabilistic forecast products from a global ensemble forecasts originating

from a number of NWP centres.

2.2.0 TROPICAL CYCLONE PROBABILISTIC PRODUCTS

2.2.1 Track Forecasting

An early area of interest in respect of the application of ensembles to tropical cyclones was in track

forecasting. Most major NWP centres run ensembles targeting tropical cyclone track (Fig 1). The

UKMO MOGREPS (Met Office Global and Regional Ensemble Prediction System) runs track

ensembles out to 15 days; European Centre for Medium Range Weather Forecasts (ECMWF)

Ensemble Prediction System (EPS) and other centres for lesser periods (see Table 1). The ensemble

products provide objective guidance to the forecaster on the spread of probable tracks over time,

which feeds back into the warning system as the degree of confidence attached to the official

forecast.

Fig. 1: Example of ECMWF generated ensemble tracks for Typhoon Lupit(October 2009). Major

Global Centres are capable of producing these charts for any designated area (ECMWF)

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Center Ensemble

Members

Forecast

Length

Forecasts

per day

ECMWF 51 10 day 2

ECMWF 51 10-15 day 2

UKMO 24 15 day 2

JMA 51 9 day 1

NCEP 21 16 day 4

CMA 15 10 day 2

CMC 21 16 day 2

BoM * 33 10 day 2

Meteo-France 11 2.5 day 1

KMA 17 10 day 2

CPTEC * 15 15 day 2

Table 1: Operational ensembles in TIGGE (after Bougeault et al. 2010) showing the number of

members in each ensemble, forecast period performed for each model run and number of model runs

per day. (*) indicates those centres not currently contributing operational models.

2.2.2 Tropical Cyclone Strike Probabilities

Attempts have been made to encompass the inherent errors associated with cyclone track

forecasting since the 1980s when strike probabilities were introduced by the US National Hurricane

Center (NHC), (see Jarrell and Brand, 1983; Sheets, 1985). The statistical technique attempted to

describe a more realistic extent of the area under threat from a hurricane given a deterministic

forecast of the track, and tempered by an historical analysis of the spread of errors normally

encountered in hurricane forecasting. The technique could assign a probability of a tropical cyclone

approaching within some specified distance of any location, or wind speed exceeding a designated

threshold at any location, thus allowing a decision-maker to appreciate a quantifiable value of the

probability of a hurricane strike. Ideally, trigger points for action could be assigned to probability

thresholds.

The Strike Probability Program used by the NHC was replaced in 2006 by the superior-performing

Monte Carlo Wind Speed Probability Model. The technique samples errors from NHC track and

intensity forecasts over the last five years to calculate probabilities over the domain (see DeMaria et

al., 2009). Products include cumulative and incremental probabilities; 34, 50 and 64 knot wind

thresholds; 0, 12,..., 120h forecasts; and text and graphical products that are readily available to

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users. Versions have been developed for the Atlantic, NE and NW Pacific areas and the technique is

currently being incorporated into the Australian forecasting process (M. Foley, pers. comm.).

A number of NWP centres generate operational strike probability products using ensembles. These

include ECMWF, UKMO, JMA, KMA, CMA and US NCEP. Graphical products show the probabilities of

a tropical cyclone approaching within a specified distance of a point, based on the ensemble

member tracks (Fig. 2). Alternatively, probabilities can be generated to indicate the chance of

exceeding a wind speed as in the case of Fig. 3. Another option for consideration by forecasters,

industry and emergency managers is to portray the probability of approach of a tropical cyclone

relative to a single point of interest, such as the time series graph of Typhoon Morakot in respect of

its potential to impact Taipei in October 2008 (Fig.4). The guidance can assist a decision-maker to

assess critical times of approach for posting warnings or taking mitigating actions.

Fig. 2: UK MOGREPS ensemble forecast showing (a) ensemble forecast tracks, (b) strike probability

for the cyclone centre passing within 75nm of a point and (c) deterministic forecast track (UKMO)

Fig 3: Wind speed probability fields generated by TIGGE EPS (October 2008) (Nakazawa)

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Fig. 4: Strike probability expressed as a time series indicating the chances of approach of Typhoon

Morakot (August 2009) against time for Taipei. The red lines are the individual ensemble members

and the blue line corresponds to the deterministic forecast. (JMA)

2.2.3 Probabilities in Storm Surge Forecasting

The issue of storm surge warnings has long been deterministic in approach, giving surge heights

based on the official landfall point; however there is significant uncertainty associated with such

figures because of the uncertainty of track, intensity, wind distribution and time of landfall (affecting

tidal interaction). In an attempt to better inform on uncertainty issues, the US NHC introduced

tropical cyclone probability products, termed P-Surge into operations during the 2009 hurricane

season (Berg et al. 2010). P-Surge uses SLOSH (Sea, Lake, and Overland Surges for Hurricanes)

model-based simulations that take into account statistics of past performance, including cross and

along-track error, intensity error and size error. The output shows exceedance height products

expressed as a percentile (Fig. 5). Heights can be assessed at any specified critical level.

A slightly different approach has been developed in Australia with the Seatide model (Harper et al.

2009). The parametric storm surge model uses the official forecast track as its baseline, but with the

forecaster inputting a range of uncertainty parameters based on the current situation to drive the

probabilistic aspects. Uncertainty parameters associated with the tropical cyclone are forward

speed, track bearing, radius of maximum wind, wind peakedness and central pressure. The model

outputs a probability curve of surge height at any point within the coastal domain expressed in

percentiles

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Fig. 5: Output from p-surge indicating probabilities of exceeding a designated height (in this case 2

feet), (US NWS)

2.2.4 Tropical Cyclone Genesis

The need to identify tropical cyclone formation has led forecasters to look subjectively for areas of

increasing low-level vorticity in low tropospheric shear and favourable moisture environment. A

number of deterministic techniques have been developed to assist in this task. Recognising that

genesis is a relatively rare event that takes place in an oceanic, observation-sparse environment, a

probabilistic approach may benefit in defining the associated uncertainty. Using best-track tropical

cyclone positions, NCEP Global Forecasting System (GFS) analysis fields, and Water Vapor (~6.7µm

wavelength) imagery, Schumacher et al. (2009) applied a linear discriminant analysis (LDA) to a

screened data set (i.e. in the most likely formation areas) to yield a probabilistic prediction scheme

for tropical cyclone formation. This technique has been implemented as an operational product

called the NESDIS (National Environmental Satellite Data and Information Services) Tropical Cyclone

Formation Probability (TCFP) product (Fig. 6). The TCFP has been configured for application in the

Atlantic, East Pacific and West Pacific basins.

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Fig.6: Probability of tropical cyclone formation generated by Tropical Cyclone Forecast Probability

(TCFP) product (NESDIS).

Other techniques that base TC formation prediction on the behaviour of features such as large-scale

atmospheric waves, intra-seasonal oscillations, climate variations and seasonal cycles are being

trialled in Severe Weather Forecast Demonstration Projects (SWFDPs). Leading from work by Frank

and Roundy (2006) and Leroy and Wheeler (2008), products give a probabilistic assessment of

cyclogenesis potential.

Ensemble techniques are also being employed by most major NWP centres to forecast tropical

cyclogenesis. For NWP models to offer acceptable guidance over a 10-15 day period, they need to be

capable of developing a tropical cyclone as cyclones can form and dissipate within such time scales.

Global models have shown some skill in this area and products to assist forecasters in assessing

tropical cyclogenesis potential such as the MOGREPS products shown in Fig. 7 are being trialled.

2.2.5 Intensity Forecasting

Intensity forecasts present significant problems for TCWC forecasters. Most operational global

dynamical models lack the necessary horizontal resolution, smaller scale process parameterizations,

and adequate initialization to simulate the dynamics of the inner core (Knaff 2006). However

DeMaria et al. 2007 reported that high-resolution numerical models and statistical-dynamical

models designed specifically for the task represented the best skill at that time for forecasting

intensity of tropical cyclones.

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Fig. 7: UKMO tropical cyclone probability products generated for 15-day periods and indicating the

likelihood of tropical cyclone activity (including genesis) over the SW Indian Ocean from 8 February

2008. (UKMO)

Statistical-dynamical models such as SHIPS (Statistical Hurricane Intensity Prediction Scheme),

(DeMaria et al. 2005) and STIPS (Statistical Typhoon Intensity Prediction Scheme), (Knaff et al. 2005)

offer forecast guidance in the Atlantic and north Pacific basins. Refinements to these have resulted

in a consensus approach using an ensemble of different forecast models (Sampson et al. 2008) and

extension to Southern Hemisphere basins (Sampson and Knaff 2009).

Ensemble routines also exist in respect of intensity prediction. The ECMWF EPS generates EPSgrams

targeting tropical cyclone intensity. An example is shown in Fig. 8 for TC Conson in July 2010, giving

depictions of both the deterministic prediction of wind speed and central pressure as well as the

probabilistic spread for these parameters.

Clearly, forecasting intensity and intensity change remains a challenge in operations. The

uncertainties resident in the inner core of tropical cyclones will ensure that this will be an enduring

problem that will demand further research into the future. Probabilistic forecasts have provided

some useful guidance to date but the very nature of statistical models in particular acts against

capturing rapidly intensifying cyclones.

2.2.6 Rainfall

Heavy rain that accompanies most tropical cyclones and also tropical depressions is a major hazard

and a significant forecasting problem. Major NWP centres generate ensembles to derive

precipitation probability fields that offer guidance on rain amounts exceeding a specified threshold.

PME and MME techniques have been developed also. In mountainous areas in particular, rainfall

totals depend strongly on the tropical cyclone angle of approach, its time spent over the area and

orographic effects that require high resolution modelling to adequately resolve – all factors that the

forecaster should be acutely aware of when using any predicted rainfall products.

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Fig. 8: EPSgram depiction of TC Conson (West Pacific area) in July 2010 showing five-day forecast of

10m wind speed and central pressure for both the deterministic forecast component and the

ensemble spread. (ECMWF)

Another interesting approach to rainfall prediction comes from the satellite-based eTRaP (Ensemble

Tropical RAinfall Potential) project (Ebert et al. 2010). This is a simple ensemble methodology whose

members are the 6-hourly totals from the single-orbit TRaPs. This ensemble approach allows for the

generation of probabilistic forecasts of rainfall in addition to deterministic rainfall totals similar to

what is currently provided by the TRaP product.

Each eTRaP is made up of forecasts using observations from potentially several microwave sensors-

currently AMSU, TRMM, SSMI and AMSRE-initialized at several observation times, and possibly using

several different track forecasts. The diversity among the ensemble members produces an ensemble

mean forecast that helps to reduce the large (unknown) errors associated with a single-sensor,

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single-track TRaP. The large number of permutations leads to ensembles with many members,

allowing probability forecasts to be issued with acceptable precision and reliability (Fig. 9).

Fig 9: eTRaP Forecast of rainfall exceeding 25mm caused by TC Hubert in March 2010 (left) compared

with verification estimation (right) (NOAA).

2.2.7 Flood Forecasting

Intimately linked with heavy rain occurrence, flood forecasting associated with tropical cyclones and

other tropical systems presents significant problems and flooding is a major socio-economic hazard.

The application of EPS in hydrology over tropical areas has not been as quick in the uptake as in

Europe where over the last ten years Hydrological Ensemble Prediction Systems (HEPS)are

increasingly being used by flood forecasting agencies, based on ECMWF EPS model output. For a

review of HEPS, see Cloke and Pappenberger (2009), who offer a set of studies demonstrating the

potential for improving flood forecasts from an ensemble approach.

Of note to those interested in progressing the use of probabilistic forecasting products in general are

the insights gained from a decade of providing HEPS-based forecasts. Cloke et al. (2009)

recommended that in addition to the need for more research into improving HEPS scientifically,

effort needed to be expended in visualising, verifying, developing user-specific applications for and

communicating the value of probabilistic (flood) forecasts.

2.3.0 ORGANISATIONAL BODIES ADDRESSING ENSEMBLES

2.3.1 THORPEX and TIGGE

Abraham (2006) reported to IWTC-VI on tropical cyclone aspects of THe Observing-system Research

and Predictability EXperiment (THORPEX), citing the program’s intent to accelerate improvement in

forecast accuracy of high-impact weather, of which tropical cyclones are integral, over forecast

periods from one day to two weeks. One facet of this program turns particular attention to the

interpretation and utilisation of ensemble forecasting systems. The THORPEX Grand Global

Interactive Ensemble (TIGGE) project was begun in 2005 to improve ensemble prediction and its use

in mitigating high impact weather (Bougeault et al. 2010). Broadly, TIGGE’s goals are to enhance

international collaboration for developing ensembles, refine multi-model ensemble methods, gain a

better understanding of the origin of elements that contribute to forecast errors, and develop a

prototype Global Interactive Forecast System (GIFS), (TIP 2005).

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Currently, ten NWP centres contribute to TIGGE with a 2-day delay (Table 1). There are three TIGGE

archive centres that have been established to store and redistribute ensemble data for research.

They are:

• China Meteorological Administration (CMA) – [http://wisportal.cma.gov.cn/tigge/]

• National Center for Atmospheric Research (USA) – [http://tigge.ucar.edu]

• European Centre for Medium Range Weather Forecasts (ECMWF) – [http://tigge-

portal.ecmwf.int]

Some preliminary results from TIGGE suggest that multi-model ensembles outperform most

individual ensembles for 500 hPa geopotential height, and appear to outperform all individual

ensembles for 850 hPa temperature and surface level fields such as temperature and rainfall

(Bougeault et al 2010). Ebert et al. (2009a) suggest that there is a strong need for more studies to

evaluate the benefit of multi-model ensembles in predicting high impact weather in accordance with

THORPEX goals.

2.3.2 Global Interactive Forecast System (GIFS)

On implementation, GIFS will take TIGGE to the next level by operating in real-time. Its goal is to

provide 14-day probabilistic forecasts to provide support to forecasting offices and TCWCs in the

mitigation of high impact weather. GIFS will need an effective communication infrastructure to be

successful and is therefore dependent on the implementation of the WMO Information System

(WIS), which is WMO’s strategy to manage data globally into the 21st

century.

Probabilistic tropical cyclone products are being developed as part of GIFS in response to

recommendations made at IWTC-VI and some ensemble products are being made available by

several TIGGE data providers for testing in conjunction with regional forecast pilot projects. During

the THORPEX Pacific Asian Campaign (T-PARC) in 2008-09, several TIGGE centres started to exchange

tropical cyclone track forecasts using a new data format (CXML or Cyclone XML). This formed the

basis for the development of new probabilistic forecast products. It is planned that GIFS (as it

develops) will work closely in association with the Severe Weather Forecast Demonstration Projects

and other regional pilot projects and field experiments to develop new and improved probabilistic

forecast products and evaluate their contribution to more effective warning systems.

2.3.3 North American Ensemble Forecast System (NAEFS)

A similar system to GIFS that is already operational is the North American Ensemble Forecast system

(NAEFS) – an ensemble system combining US and Canadian EPS to produce real-time forecasts over

North America (Toth et al. 2006).

2.3.4 Severe Weather Forecast Demonstration Projects (SWFDP)

WMO have recognised that as NWP and EPS systems improve, many NMHSs, especially those of

developing countries, should receive similar benefits for their meteorological services, especially for

the provision of advisories and warnings of severe weather events with increased lead-times that are

already being realized by other countries. The Severe Weather Forecasting Demonstration Project

(SWFDP) is an approach that facilitates improved access to, training in the interpretation and use of

existing NWP/EPS products by forecasters in developing countries.

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2.3.4.1 Southern Africa

The first SWFDP was implemented in southeast Africa and completed its one-year field phase in

November 2007. It focused on improving weather forecasting and warning services for heavy rain

and strong winds and involved global and regional centres to build the forecasting capacity of the

NMHSs of Botswana, Madagascar, Mozambique, Tanzania, and Zimbabwe, using products provided

by the South African Weather Service. Its success, and it was assessed by WMO as successful

(Coiffier and Chen 2008) was highly dependent on participating global centres: ECMWF, NCEP (USA),

and the Met Office (UK), and the regional centres: RSMC Pretoria (South Africa) and RSMC La

Réunion (France).

Following the success of phase 1 of the project, it has now been extended to cover 16 NMHSs across

southern Africa. The second phase covers an increased range of forecast products with an increased

emphasis on TC cyclone forecasts for the Indian Ocean, including fields from a limited area model

run for RSMC La Réunion.

2.3.4.2 South Pacific

The operational domain of the South Pacific Severe Weather Forecasting Demonstration and

Disaster-reduction Project (SWFDDP) is 150E to 150W, 2N to 25S.The main goals of SWFDDP is to

test the usefulness of NWP products produced by global and regional meteorological centres and to

equip NMHS that do not have access to sophisticated model output with highly effective guidance to

enhance the provision of severe weather forecasting services to their communities.

The original focus of the project was on strong destructive winds and heavy precipitation that could

cause serious flooding, either associated with tropical cyclones or other weather systems. Such a

demonstration project would use a “cascading” approach for forecasting severe weather in three

levels, as follows:

• Global centres to provide a range of NWP products, including probability types;

• Regional centres to interpret information received from Global NWP centres, run limited-area

models to refine products, liaise with the participating NMHSs;

• NMHSs to issue alerts, advisories, severe weather warnings; to liaise with DMCPAs and the

media, and to contribute to the evaluation of the project.

The SWFDDP has been implemented in two phases: a Pilot (limited participation, November 2009 to

November 2010), and a full Demonstration (expanded participation, November 2010 -November

2011). RSMC Wellington is the regional centre for the SWFDDP and issues daily guidance to

participating NMHS based on available guidance, including ensemble products (Fig. 10).

The SWFDDP at the pilot phase has focussed on 3 hazards: heavy rain, strong winds, and damaging

waves. The assessment of damaging waves requires specialized sea-state NWP/EPS guidance

products from global or regional centres. The NMHSs taking part were each invited to provide a list

of up to 10 locations (location name, latitude, longitude, and elevation) for which routine ECMWF

EPSgrams could be generated. Where appropriate (suitable sea points), ECMWF’s “wave EPSgrams”

were also generated. The UK Met Office is also providing EPSgrams constructed from MOGREPS.

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Fig. 10: Example of guidance product issued to participating NMHS in the first phase of the South

Pacific SWFDDP, incorporating available ensemble guidance from major NWP global centres. (WMO)

A training component is an integral part of the project with participating personnel being instructed

on how to interpret the information available on the project website called MetConnect Pacific.

Products that are accessible include a host of NWP EPS products. Other information emphasises the

importance of NMHS forging robust relationships with end-users such as disaster managers and civil

protection authorities.

2.3.4.3 Southeast Asia

The Severe Weather Forecasting Demonstration Project (SWFDP) for Southeast Asia [operational

domain 10°S, 40°N, 80°E and 140°E] is expected to bring benefits in terms of enhancement of

technical capacity in operational forecasting and advancement in weather service delivery to

member countries in the region. The project’s primary focus is on severe weather events and

associated hazards such as flooding, landslides, high waves and swells. Tropical cyclone (both South

China Sea and Bay of Bengal) track, intensity, structure changes and landfall process (wind and gust,

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rainfall and storm surge) are also in scope as is heavy rain triggered by tropical cyclones, SW and NE

monsoon, troughs and ITCZ migration, and orography. A one-year field phase is expected to

commence in May 2011.

The China Meteorological Administration (CMA), Japan Meteorological Agency (JMA) and Korea

Meteorological Agency (KMA) will act as Global Centres, providing NWP guidance material while Viet

Nam takes up the role of Regional Forecast Support. RSMC Tokyo and RSMC New Delhi will offer

tropical cyclone forecasting support, and HKO will provide training and technical support where

appropriate. Cambodia (subject to formal confirmation of interest), Lao PDR, Thailand and Viet Nam

are to participate as National Meteorological Centres.

There is another project currently underway in Southeast Asia associated with the Shanghai World

Expo called the WMO Landfall Typhoon Forecast Demonstration Project (WMO-LTFDP). The project

is being coordinated by the Shanghai Typhoon Institute and is another project with a service focus

2.3.5 Hurricane Forecast Improvement Project (HFIP)

The HFIP is a ten year project designed to provide the basis for NOAA and other agencies to

coordinate hurricane research needed to significantly improve guidance for hurricane track,

intensity, and storm surge forecasts. It also engages and aligns the inter-agency and larger

scientific community efforts towards addressing the challenges posed to improve hurricane

forecasts.

The goals of the HFIP are to improve the accuracy and reliability of hurricane forecasts; to

extend lead time for hurricane forecasts with increased certainty; and to increase confidence in

hurricane forecasts. The HFIP will target major investment in enhanced observational

strategies, improved data assimilation, numerical model systems, and expanded forecast

applications based on the high resolution and ensemble-based NWP systems.

The specific goals of the HFIP are to reduce the average errors of hurricane track and intensity

forecasts by 20% within five years and 50% in ten years with a forecast period out to 7 days.

(Source: http://www.hfip.org/ - Note: Web page is still under development).

2.4.0 VERIFICATION ISSUES FOR TC PROBABILISTIC FORECAST PRODUCTS

The quest to assess the quality of probabilistic forecasts has revealed some fundamental

complexities. Ebert et al (2009b) noted that the assessment of such forecasts will depend on how

that product is used. For example, on a given tropical cyclone warning, emergency managers in a

threat zone may require the prediction of the landfall point more accurately than the absolute

intensity, while numerical modellers may want to assess how realistically their model simulated the

distribution of critical elements. Forecasters may be more interested in knowing where the

limitations of a model lie to apply in future events. Ebert et al. (2009b) suggested that “verification

approaches must be selected or developed to evaluate those aspects and attributes of the forecast

that are important to the particular users in question” and that any verification information must be

communicated in an effective and timely manner, making sure that the evaluation methods were

both understood by and useful to the end-user of the product. They made the clear point that, “If

probabilistic forecasts are difficult for many users to understand, the verification of probabilistic

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forecasts is even harder to comprehend.” This suggested a measure of consultation is necessary

between verifiers and user in order to establish meaningful standards and metrics.

A second issue noted in Ebert et al (2009b) was that, while probabilistic predictions may be more

appropriate than deterministic forecasts for high impact weather events due to their inherent

uncertainty, it was not realistic to verify a probability forecast for any single event. For example, if a

given location was assigned a 20% probability of being affected by a tropical cyclone, and it was

affected –how does that prediction verify? The authors reinforced that “verification of probability

forecasts requires many matched forecasts and observations”- a data set that is so difficult to attain

in many tropical cyclone events.

While researchers and modellers quest for quantitative verification methods, qualitative verification

of probabilistic forecasts can still provide some measure of quality and thus feedback to product

providers. This was exemplified in work done in association with the southern Africa Severe Weather

Forecast Demonstration Project (SWFDP), where anecdotal evidence from forecasters was used with

some effect to gauge product quality and usefulness (Coiffier and Chen 2008).

Spatial verification methods lend themselves towards the effective verification of NWP because they

match forecast and observations on a grid, and can identify systematic errors. Gilleland et al. (2009,

2010) provide a comprehensive review of such methods which, while not considered mainstream at

present, offer viable avenues of pursuit for developing effective and usable verification products.

2.5 FUTURE DIRECTIONS

A tropical cyclone event generally unfolds in a series of forecast decisions from genesis to

dissipation. Tropical cyclones are relatively rare, high-impact events and mostly operate in a data-

sparse area, which creates observational and behavioural uncertainty that compounds to varying

degrees into prediction errors. One approach to manage these errors has been to assign

probabilities to an event occurring by looking back at historical performance to optimise the

forecast. More recently, dynamical techniques have been developed that try to anticipate the range

of uncertainty through an ensemble of multi-member numerical model runs. Both have a future in

meteorological prediction.

2.5.1 Probabilistic forecast products

There is an array of probabilistic tropical cyclone forecasting techniques and methodologies either

currently available or being prototyped in research facilities that give forecasters a view to assessing

properties of tropical cyclones that will supplement the traditional deterministic approach. These

products and techniques are not widely available in all cyclone prone NMHS for a variety of reasons;

including a lack of awareness of their existence by NMHS, communication capacity limitations, or

that the technique has not been configured to operate in a certain area.

2.5.2 Ensemble forecast products

Ensembles are a relatively recent field of research and are not widely available in operations.

Nevertheless there is a range of products that are being developed or run in non-real time. Some

products are being trialled in real-time in some TCWCs and in forecast demonstration projects and

early assessments by users indicate that they will have a place in forecaster decision-making in the

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future (Coiffier and Chen 2008). Once generated at a global centre, ensemble based products can be

transmitted in a file format that is relatively easy to share between countries, including Least

Developed Countries (LDCs) that do not have the same computing capacity. This potentially gives all

NMHS a chance to fast-track benefits from the significant scientific advances that are being made,

and use ensemble products to improve their local tropical cyclone warning system. Such a process

would be a prime example of transforming science directly into tangible socio-economic benefits

through the saving of lives and property.

Equally and because they are such a comparatively recent development, it is important for ensemble

product developers from the major global centres to be in close touch with operational TC

forecasters in order to gain feedback about how ensembles are used and how they might be

improved to maximise their effectiveness.

2.5.3 Verification

There remains much work to do to achieve this. Meaningful (objective) verification of probability

forecasts is still an issue and more understanding is needed in order for users to make the most of

probability products. It is therefore important to bring users on board and for developers to get

feedback from users about the effectiveness of products so that and probabilistic and ensemble

products can continue to evolve in a positive fashion. Therefore subjective verification techniques

stand as a useful way forward in the development process for probabilistic products.

2.5.4 Training

Training is also an important issue here and processes need to be put into place to ensure that

forecasters can take full advantage of probability in forecasting through effective training programs.

Without a satisfactory understanding of these techniques it is possible to misuse probability forecast

products and draw incorrect conclusions. When a tropical cyclone is involved there can be dire

consequences. It behoves WMO, the major global NWP centres, training facilities and individual

NMHS to plan to include probabilistic forecasting into training curricula for future meteorologists, in

addition to dedicated training programs for current operational forecasters as part of any phased

implementation of such products into the forecast process.

2.5.5 Extension into the community

Probability products have been developed to better assist in decision-making and risk assessment,

and in the first instance this guidance has rightly been targeted at meteorological decision-makers in

TCWCs. Some weather-sensitive industry has subscribed to strike probability products to incorporate

into their decision-making process with actions being triggered as certain probability thresholds are

exceeded. However, emergency managers and the general community in most countries have really

only had limited exposure to the concept of uncertainty and how to systematically handle it. The

most effective exposure has been achieved through the introduction of graphical products that

depict “gray” areas in respect of predicted TC centre position, such as the example shown in Fig. 11.

While this has been a useful way to emphasise that a prediction of the future location of a tropical

cyclone is not necessarily a single solution, the full potential of probabilistic forecasts in risk-

assessment is nowhere near to being realised.

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Fig. 11: Example of tropical cyclone threat map available to emergency services and the community

showing past track, predicted 48h track and uncertainty envelope in respect of tropical cyclone

position. Shaded areas on the coast indicate areas under Warning and Watch (Aust. BoM).

Greater exposure of emergency managers to probabilistic products is a logical extension to the

development of such products as it seems the most effective way for any decision-maker to treat

uncertainty. This carries with it the need for NMHS, coordinated through WMO, to plan to move

forward and open dialogue with emergency management agencies on introducing more probability

products to aid in risk assessment. To achieve this, the meteorological community needs to be

ready, ensuring that the probabilistic products being offered will be of genuine benefit to and

accepted by the user.

2.6 REFERENCES

Abraham, J.D., 2006: THORPEX: a focus on tropical cyclone related research, Workshop Topic

Reports (IWTC-VI), TMRP Report Series TMRP No. 72, WMO, 361-372.

Berg, R., J. Rhome and A.A. Taylor, 2010: Storm surge probability forecasts for Hurricane Ike,

29th

AMS Conf. on Hurr. and Trop. Meteor., Tuscon, Ariz.

Bougeault, P., Z. Toth, C. Bishop, Barbara Brown, David Burridge, De Hui Chen, Beth Ebert, Manuel

Fuentes, Tom Hamill, Ken Mylne, Jean Nicolau, Tiziana Paccagnella, Young-Youn Park, David Parsons,

Baudouin Raoult, Doug Schuster, Pedro Silva Dias, Richard Swinbank, Yoshiaki Takeuchi, Warren

Tennant, Laurie Wilson and Steve Worley, 2010: The THORPEX Interactive Grand Global Ensemble

(TIGGE). Bull. Amer. Meteor. Soc., in press.

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Cloke, H., and F. Pappenberger, 2009: Ensemble Flood Forecasting: A Review, J. Hydrol., 375, 613-

626.

Cloke, H., J. Thielen, F. Pappenberger, S. Norbert, G. Balint, C. Edlund, A. Koistinen, C. De Saint-Aubin,

E. Sprokkereef, C. Viel, P. Salamon, and R. Buizza, 2009: Progress in the implementation of

Hydrological Ensemble Prediction Systems (HEPS) in Europe for operational flood forecasting,

ECMWF Newsletter, 121, 20-24

Coiffier, J. and P. Chen, 2008: Severe Weather Forecasting Demonstration Project – Regional

Subproject in RA I – Southeast Africa, Final Report. WMO Secretariat, 27 February 2008.

DeMaria, M., M. Mainelli, L.K. Shay, J.A. Knaff, and J. Kaplan, 2005: Further improvement to the

Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531-543.

DeMaria, M., J.A. Knaff, and C. Sampson, 2007: Evaluation of long-term trends in tropical cyclone

intensity forecasts. Meteor. Atmos. Phys., 97, 19-28.

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Estimating Tropical Cyclone Wind Speed Probabilities. Wea. Forecasting, 24, 1573-1591.

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Hemi. Meteorology and Oceanography, Melbourne, Australia

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Proc. 9th

Intl. Conf. Sthn. Hemi. Meteorology and Oceanography, Melbourne, Australia.

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tropical rainfall potential (eTRaP) forecasts. Wea. Forecasting, accepted.

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134, 2397-2417.

Gilleland, E., D. Ahijevych, B.G. Brown, and E.E. Ebert, 2010: Verifying forecasts spatially. Bull. Amer.

Meteor. Soc, 29pp, in press.

Gilleland, E., D. Ahijevych, B.G. Brown, B. Casati, and E.E. Ebert, 2009: Intercomparison of spatial

forecast verification methods. Wea. Forecasting, 24, 1416-1430.

Harper, B., T. Hardy, L. Mason, R. Fryar, 2009: Developments in storm tide modelling and risk

assessment in the Australian region, Nat. Hazards, 14pp.

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Meteor. Soc., 64, 1050-1056.

Knaff, J.A., C.R. Sampson, and M. DeMaria, 2005: An operational statistical typhoon intensity

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Knaff, J., 2007: Operational guidance and skill in forecasting structure change. Workshop Topic

Reports (IWTC-VI), TMRP Report Series TMRP No. 72, WMO, 160-184.

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Leroy, A., and M.C. Wheeler, 2008: Statistical prediction of weekly tropical cyclone activity in the

southern hemisphere. Mon. Wea. Rev., 136, 3637-3654.

Sampson, C.R., J.L. Franklin, J.A. Knaff, and M. DeMaria, 2008: Experiments with a simple tropical

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methods used at the Joint Typhoon Warning Center, Pt III: forecasts based on a multi-model

consensus approach. Aust. Meteor. Oceanographic J., 58, 19-27.

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tropical cyclone formation, Wea. Forecasting, 24, 456-471.

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WMO/CAS/WWW

SEVENTH WMO INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES Topic 2: Tropical Cyclone probabilistic forecasting and related product development issues & applications for user risk assessment

Possible recommendations

1. That WMO continues to support the work of THORPEX and other related regional tropical

cyclone projects to assist in the goal of developing a comprehensive suite of probabilistic

forecast products targeting tropical cyclone impacts.

2. That WMO builds into its planning agenda the goal of implementation of suitable ensemble

products to all TC-affected NMHS, including plans for necessary training programs and

communication systems.

3. That opportunities be created to allow ensemble product developers to interact directly

with operational TC forecasters to maximise the information flow for improving ensemble

products and usage.

4. That effort be placed into developing meaningful verification methods and protocols so that

any verification statistics published on probabilistic products can be readily interpreted by

users.

5. That WMO and TIGGE place high priority on the development and implementation of the

Global Interactive Forecasting System (GIFS), recognising the significant benefits that this

system will have in enhancing guidance to tropical cyclone forecasters.