Edwin S.T. Lai (黎守德)
Hong Kong Observatory
August 2011
TOWARDS PROBABILISTIC
TROPICAL CYCLONE TRACK FORECASTS
1
ON THE MORNING OF 25 AUG 2011
•
•
Severe Tropical Storm Nanmadol tangling with a
tropical depression (to its east) and a weak low
pressure area (to its north), all embedded within
a gigantic gyre over the western North Pacific.
Tracking forecasting is notoriously problematic
in such situations involving multi-vortex
interaction, even more so for the vortex located
at the western end of the gyre, which in this
case is Nanmadol.
2
Satellite image at
8:32 a.m. on 25 Aug 2011
2011年8月25日早上
8時32分的
衛星圖像
Low pressure area
低壓區
Tropical Depression
熱帶低氣壓
Nanmadol
南瑪都
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WHAT DID THE NWP MODELS SAY?
•
•
Numerical Weather Prediction (NWP)
computer models are now the main tools in
track forecasting.
NWP forecasts available at the time (based
on information analysed the previous evening
at around 8 pm) given by state-of-the-art
operational global and regional models
(name of models hidden for the purpose of
this discussion) pointed to a multitude of
possible scenarios, and hence probably a
rather hesitant and erratic track for Nanmadol.
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5
WHAT WOULD A DETERMINISTIC TRACK FORECASTER DO?


He would either adopt a warning track from what he
considers to be the most reliable model (let us say, in
this case, Model 1 for the sake of subsequent
discussion), or formulate a “consensus” track derived
from an “averaging” of available forecast tracks as his
working solution.
“Uncertainties” in the deterministic 3-day forecast track
issued by the Hong Kong Observatory (HKO) are
represented by “bubbles” of pre-determined and everincreasing radius of probable strike areas, based
mostly on past statistics and hence may have very little
physical relevance to the current situation.
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Deterministic 3-day forecast track of Nanmadol
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WHAT WOULD A PROBABILISTIC TRACK FORECASTER DO?


He would look at the full range of possible NWP
track scenarios, and go through a quantitative
assessment of the probabilistic information such as
those generated by the major NWP models in Slide
5 (in particular Model 1 if considered the most
reliable).
He would then formulate his forecasts and
communicate his warning messages not just based
on one main track scenario, but also with due
consideration and sufficient attention given to other
possible scenarios according to the probabilistic
assessment.
8
Cluster analyses, based on
Model 1’s 5-day tracks, identify
five possible track scenarios, from
1 to 5 in decreasing probability.
Note the ensemble mean track
(in grey), similar to Cluster
Mean 2 (yellow-green), is not
the most probable scenario
(which is Cluster Mean 1 in red).
9
On the morning of 25 Aug 2011
Model 1: 5-day Strike Probability Map
based on analyses at 8 pm on 24 Aug 2011
10
5 days later on 30 Aug 2011
Model 1: 5-day Strike Probability Map
based on analyses at 8 pm on 29 Aug 2011
11
WHAT REALLY HAPPENED?


Nanmadol tracked north-northwestward slowly, crossing
Luzon Strait, skirting past southwestern Taiwan, before
landing near Xiamen (Fujian) on 31 Aug. Qualitatively,
Nanmadol’s motion in general followed the most probable
track scenario towards Taiwan, albeit with the whole track
displaced further to the west.
Flipping backward and forward between the two previous
slides (Slides 10 and 11), one can see the actual track (in
black) did not quite fall in line with the axis of maximum
probability (the red and yellow areas). But interestingly, it
somehow stayed within the confines of the probable strike
area (along the 10% dark blue strip at the western
boundary).
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WHY SO MANY TRACK SCENARIOS FROM ONE MODEL?
Ensemble Prediction System (EPS) is an
extension of the NWP models to “mimic” the
“uncertainties” involved.
 “Uncertainties” are artificially and
methodically introduced to an “EPS model”,
typically a coarser version of the parent
operational model (computing resources
being the limiting factor).

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WHY SO MANY TRACK SCENARIOS FROM ONE MODEL?
In accordance with system design, the EPS
model is run as many times (typically
between 10 and 50) as the number of
“uncertainties” to be represented. Results
from each model run constitute an “ensemble
member”.
 For example, on the subject of tropical
cyclone track forecasts, 50 tracks are
generated by 50 ensemble members in
Model 1 for probabilistic assessment.

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IN SEARCH OF GOOD EPS

The key to a “good” EPS is the generation of a
spectrum of “uncertainties” for an adequate
representation of the probable scenarios involved.
For example, given the known variability in a multivortex situation, the representation of “uncertainties”
by Model 5 in Slide 5 may deem to be insufficient.
Even though the predicted track towards Taiwan
turns out to be generally correct in the case of
Nanmadol, its apparently “consistent” forecasts
(rather pleasing in the eyes of the “deterministic”
forecaster) can be, in the context of probabilistic
considerations, extremely misleading.
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IN SEARCH OF GOOD EPS


The EPS model will naturally inherit bias tendencies
or other “weaknesses” from its parent model. For
example, the east-west displacement of tracks
between forecasts by Model 1 and the actual motion
may be due to a systematic bias in the parent
operational model itself.
Compared to its parent model, the coarser EPS
model is in general less capable in representing finer
cyclone structures and hence cyclone intensity. This
may in turn impact on the resultant cyclone motion,
the effect of which is largely undetermined.
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WHAT TO BELIEVE IN EPS?

Pending further development work in EPS,
the use of EPS probabilistic information at
present is likely to be more useful in terms of:
 qualitative
rather than quantitative assessment of
possible strike scenarios; and
 assessing what is very unlikely to happen, rather
than what is most likely to happen.
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WHAT TO BELIEVE IN EPS?
For example, in the case of Nanmadol, we can
probably say on the morning of 25 Aug that it is
more likely to hit Taiwan rather than Japan.
 For forecast and warning strategies in Hong
Kong (and for planning purposes by users):

Nanmadol’s threat can be taken as very minimal
over the 5-day period of 25 – 29 Aug (Slide 10);
 weather likely to be cloudier and more showery on
30 Aug – 3 Sep as a weakened Nanmadol or its
remnant drifts across southern China (Slide 11).

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