IMPACT ON HURRICANE TRACK AND INTENSITY FORECASTS OF GPS DROPWINDSONDE OBSERVATIONS FROM THE SYNOPTIC SURVEILLANCE MISSION INTO HURRICANE MITCH ON 24 OCTOBER, 1998.

Sim D. Aberson

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1. Synoptic situation

Hurricane Mitch developed from an easterly wave that moved off the coast of Africa 10 October, but did not develop into a tropical depression until reaching the western Caribbean Sea on 22 October. The system moved slowly northward, strengthening rapidly into a hurricane by 24 October. Due to the potential threat to Southern Florida if the northward motion continued as suggested by the guidance, a synoptic surveillance mission was tasked for nominal time 25 October 1998 0000 UTC. At that time, Mitch was moving slowly north-northwestward and was located in the west-central Caribbean Sea southwest of Jamaica. Since Mitch was surrounded on all sides by deep-layer anticyclonic gyres except for a weakness to the west, little motion should have been expected (Fig. 1).

2. General Assessment of dropwindsonde impact

A. GFDL model

Figure 2 shows the GFDL forecast tracks for Hurricane Mitch, and Table 1 shows the errors and impact of the synoptic surveillance mission. The results are mainly strongly negative, with improvements only within the first 24 h. The dropwindsonde data initially moved the forecast track to the left, but also slowed the track, allowing for recurvature, whereas the run without the dropwindsonde data continued to move Mitch to the west.

Mitch made landfall 108 h into the forecast on the north coast of Honduras east of La Ceiba, Atlantida. Neither the run with nor the run without the dropwindsonde data forecast landfall.

B. VICBAR

Figure 3 shows the VICBAR forecast tracks for Hurricane Mitch, and Table 2 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data degraded the forecast at all forecast times. The dropwindsonde data pushed the storm further to the east away from the actual track and slowed the track down, accounting for the degradation.

Mitch made landfall 108 h into the forecast on the north coast of Honduras east of La Ceiba, Atlantida. The VBAR forecast with the dropwindsonde data forecast landfall south of Cozumel on the Mexican coast of Yucatan, 517 km away. The forecast without the dropwindsonde data forecast landfall on the Mexican coast of Yucatan near Chetumal, 377 km from the actual lanfall point.

C. GSM

Figure 4 shows the GSM forecast tracks for Hurricane Mitch, and Table 3 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data had a large negative impact on the GSM forecast track at all forecast times except 12 h. The dropwindsonde data pushed the forecast to the left and made the storm move slower than in the forecast without the additional data by 24 h. This allowed the storm to get picked up in the weakness in the subtropical ridge causing the forecast degradation.

Mitch made landfall 108 h into the forecast on the north coast of Honduras east of La Ceiba, Atlantida. Neither the run with nor the run without the dropwindsonde data forecast landfall.

D. Intensity

Table 4 shows the GFDL intensity forecast errors and impact of the synoptic surveillance mission. The dropwindsonde data had a positive impact at all times except 36 and 120 h. Tuleya and Lord (1998) also showed modest improvements to GFDL model intensity forecasts in the HRD synoptic flow cases.

3. Targeting

The atmosphere has long been recognized as a chaotic system (Lorenz 1963), e.g. very small perturbations to initial conditions result in increasingly large differences in the evolution of the atmosphere with time. Since the exact state of the atmosphere can never be measured, all analyses contain errors whose magnitudes can only be estimated. An indeterminate number of initial conditions consistent with the observational data can therefore be used in numerical weather prediction, and single model runs at any synoptic time only give one possible solution to the evolution of the atmosphere. Many operational forecast centers around the world, therefore, now employ ensemble forecasting as a means of quantifying the uncertainty in the evolution of the atmospheric system. Small perturbations from a best "control" state are calculated and added to and subtracted from this control to allow for different integrations starting from theoretically equally likely initial states. These perturbations are designed so as to mimic the fastest growing modes in the model and to create the largest envelope of possibilities in the forecast. Therefore, they generally correspond to locations where large analysis errors will most impact the forecast. Those features corresponding to perturbations which most impact the tracks of tropical cyclones must be found, properly sampled, and thoroughly tested, to prove the efficacy of targeting techniques.

Figure 5 shows the variance of the size of the perturbations in the National Centers for Environmental Prediction (NCEP) global model ensemble forecasting system (Toth and Kalnay 1993). The maximum over the Southwestern Caribbean Sea is associated with Hurricane Mitch. The maximum over Eastern Cuba and Haiti is assocated with the weakness in the subtropical ridge north of Mitch which may have allowed Mitch to move northward rather than westward. The smaller maxima surrounding Bermuda are associated with shortwave troughs revolving through the logwave trough in the Atlantic. The small maximum in the Gulf of Tehuantepec is associated with a weak low pressure area. The large maximum on the western edge of the figure is associated with Hurricane Lester. The dropwindsonde data fully sampled only the second of these maxima, that associated with the weakness in the subtropical ridge (due to the availability of rawinsonde data from Santo Domingo, Grand Cayman, and Kingston).

An additional model run has been performed. The TG run includes the dropwindsonde data taken within and around the weakness in the subtropical ridge (a total of five dropwindsonde observations). Results are shown in Tables 1-4 and Figs. 2-4. The TG run provided better forecasts than the run including all the dropwindsonde data at all times except 12 and 24 h in the GFDL and GSM, and 84 and 96 h in VBAR. The TG run improved the landfall forecast of VBAR, the only model to forecast landfall.

Figure 6 shows the difference in the 850 - 200 hPa averaged winds between the runs in which all the dropwindsonde data and none of the dropwindsonde data are included. The differences nearly encircle Mitch except for a somewhat wide opening to the north. The northernmost dropwindsondes have only slight impact on the initial conditions, and these are all located in or near minima in the ensemble spread. The maximum difference is located just to the east of the hurricane and the maximum spreads to the southwest, just outside the line of actual observations. Since this response may not be physically meaningful, and since it is located in a region of large ensemble spread, these differences are expected to grow, and negatively impact the forecast. Figure 7 shows that, by 24 h into the forecast, the largest difference between the forecasts with and without the dropwindsonde data has amplified and surrounded the hurricane. Since this initial difference may be at least partially spurious, this led to the great degradation of the forecast after 24 h. The forecast may have been improved by flying further from the center to the south and east of the storm. These results seem to confirm that the largest (smallest) ensemble perturbations correspond to amplifying (decaying) modes in the model. The results of the TG model runs confirm that a portion of the dropwindsondes have almost the same impact as all the dropwindsonde data.

4. Conclusion

The dropwindsonde data obtained during the synoptic surveillance mission for Hurricane Mitch at nominal time 25 October 1998 0000 UTC has provided mainly negative results. The MRF ensemble forecasting system suggests, and the model runs confirm, that data around the hurricane itself and in the weakness in the subtropical ridge north of the hurricane have the greatest positive impact on the forecast. However, the southern and eastern quadrants of the hurricane were not fully sampled, and that led to possible spurious spread of the influence of the dropwindsonde data into unstable regions which greatly negatively impacted the forecasts.