1. Edwin S.T. Lai ( 黎守德 ) Hong Kong Observatory August 2011 1

2. 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

3. 3 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 Nanmadol 南瑪都 Tropical Depression 熱帶低氣壓 Low pressure area 低壓區

4. 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. 4

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6.  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 ever- increasing radius of probable strike areas, based mostly on past statistics and hence may have very little physical relevance to the current situation. 6

7. 7 Deterministic 3-day forecast track of Nanmadol

8.  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

9. 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

10. Model 1: 5-day Strike Probability Map based on analyses at 8 pm on 24 Aug 2011 On the morning of 25 Aug 2011 10

11. 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

12.  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). 12

13.  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). 13

14.  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. 14

15.  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 multi- vortex 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. 15

16.  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. 16

17.  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. 17

18.  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). 18