Conditioned Strike Probability
in Tropical Cyclone Forecasting
In tropical cyclone (TC) forecasting, strike probability refers to the
likelihood of a location being hit directly by the centre (or the eye) of the
cyclone during the forecast period. If reliably estimated, such information
will greatly facilitate risk managers and emergency response personnel in
their planning and decision-making for the mitigation of consequential
disasters. With the advent of ensemble prediction system (EPS), TC strike
probability can be calculated dynamically (see Appendix A). Typically,
EPS runs in major NWP centres are executed twice daily, i.e. every 12 hours
at 00 and 12 UTC. Fig. 1(a) shows a strike probability map for Typhoon
Songda (0418) over the western North Pacific based on the direct model
output from JMA EPS initialized at 12 UTC on 2 September 2004.
However, how to make the best use of the probabilistic information
provided by the EPS output can sometimes pose quite a challenge. A
cursory look at the strike probability map in Fig. 1(a) suggests that chances
of Songda recurving to the north and tracking westward are more or less the
same. At this point in time (say on the morning of 3 September 2004), risk
managers in Hong Kong and their counterparts in Nagasaki (Japan) or Pusan
(Republic of Korea), facing a tough 50-50 decision, could well opt to sit on
the fence, hoping that the arrival of another day will make things clearer.
But with the next EPS output not due until later in the day, can something
meaningful be done at this stage to steer the decision-makers along the right
track so that the rest of the working day can be utilized to the full for
potentially time-critical preparation?
Under a cooperative research project between HKO and JMA on the
utilization and verification of the EPS TC data (forecast positions and
intensities at 6-hourly intervals for 25 EPS members), a number of
automated algorithms have been developed for the post-processing of such
data. One of the findings that promises great operational benefits is the use
of actual TC observed positions a posteriori for the “conditioning” of strike
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probability, a tactic that could be especially of great value for divergent EPS
track scenarios such as in the case of Songda. For JMA EPS TC data
initialized at 12 UTC, the output is usually available at HKO between 00
and 01 UTC the next day. As such, there are at least three 6-hourly
observed TC positions available at 12, 18, 00 UTC (although more frequent
position fixes are possible, say 3-hourly or even hourly, the potential gain is
expected to level off) for conditioning the strike probability. The method
used is based on track clustering technique and Bayesian learning (Appendix
B). Fig. 1(b) shows the conditioned strike probability for Songda, based on
the same EPS run initialized at 12 UTC on 2 September 2004. As verified
by the actual track (black line in Fig. 1(b)), the conditioned algorithm
successfully enhances the likelihood of Songda going towards higher
latitudes and proportionately lowers its strike probability elsewhere.
If we take time-lagged EPS data from previous runs into
consideration as well, the number of TC observed positions available for
conditioning will naturally increase. Fig. 2 shows the EPS run one day
prior to the example in Fig. 1. The unconditioned strike probability map
indicates that southern China is the more likely destination. Conditioned
on the three observed positions immediately after the model initial time, the
strike probability leans even more towards the westward scenario (Fig. 2(b)).
Repeating the conditioning algorithm again 24 hours later on the same set of
EPS data, but this time with even more latest observed TC positions, the
conditioned strike probability now points more towards the higher latitudes
(Fig. 2(c)), henceforth arriving at the same conclusion as in the example
illustrated in Fig.1.
The performance of conditioned strike probability in quantitative
terms relative to the unconditioned solution is currently under investigation
and evaluation. Theoretically and philosophically, in what ways the
injection of TC observed positions helps to improve the EPS prognoses also
requires further studies. Do they serve to reduce the errors arising from
imperfect model analyses, or do they help to capture the synoptic steering
factors that are not correctly simulated by the numerical process? As a
corollary, are more observed TC positions for injection necessarily better (as
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suggested by the example in Fig. 2), or is it more important to inject the
more critical positions that reflect the deciding synoptic control (in the case
of Songda, such as those specifically on 2 September 2004 which seem to
make the difference between westward and recurving track scenarios)?
As such, while EPS inevitably opens up a whole new dimension in
the forecasting of TCs as well as other severe weather systems, how we can
best enhance and utilize the information in an intelligent and prudent
manner is something that would require further in-depth and systematic
research, something that will involve dedicated development effort in NWP
applications for modellers as well as forecasters, and something that
definitely THORPEX can focus upon as part of its global effort to tackle
such issues for the benefit of disaster mitigation and risk management.
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Appendix A – Calculation of Strike Probability from a TC Ensemble
In an ensemble of TC tracks, strike probability (denoted by Pstrike ) at a point
x is calculated as the ratio between the number of member tracks, N  , falling within
a circular region of radius,  , centering at x and the total number of member tracks,
N total :
Pstrike(x) 
N
.
N total
Following the common practice of major meteorological centres, e.g. ECMWF and
JMA, we take  to be 120 km.
N

x
N total
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Appendix B – Track Clustering Technique and Bayesian Learning
(to be prepared)
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(a)
(b)
Fig.1 Strike probability maps for Typhoon Songda based on JMA EPS run initialized at 12 UTC
on 2 September 2004: (a) unconditioned; and (b) conditioned by three actual TC observed
positions (at 12, 18, 00 UTC) between the EPS run initial time and data availability time
(around 00-01 UTC) at HKO, with actual TC track in black.
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(a)
(c)
(b)
Fig.2 Illustration of the effect of observed TC positions on strike probability distribution, based on the
JMA EPS run initialized at 12 UTC on 1 September 2004, i.e. 24 hours prior to the example in
Fig.1: (a) unconditioned (b) conditioned on the three 6-hourly TC observed positions from EPS
initial time to 00 UTC on 2 September; and (c) conditioned on the seven 6-hourly TC observed
positions from EPS initial time to 00 UTC on 3 September. In (b) and (c), actual TC track is
plotted in black for verification.
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