IMPACT ON HURRICANE TRACK AND INTENSITY FORECASTS OF GPS DROPWINDSONDE OBSERVATIONS FROM THE SYNOPTIC SURVEILLANCE MISSION INTO HURRICANE DANIELLE ON 29 AUGUST, 1998.

Sim D. Aberson

Hurricane Research Division
Atlantic Oceanographic and Meteorological Laboratories
National Oceanic and Atmospheric Administration

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1. Synoptic situation

Hurricane Danielle developed from an easterly wave midway between the Cape Verde Islands and the Leward Islands on 24 August, 1998. It moved westward, strengthening rapidly into a hurricane the next day. Due to the potential threat to the Carolina coastline, a synoptic surveillance mission was tasked for nominal time 29 August 1998 0000 UTC, with a follow-on mission the next day. At that time, Danielle was moving slowly west-northwestward about 600 km east of Nassau, to the south of the strong subtropical ridge ( Fig. 1). The weak cold low was located over Jamaica the previous day has dissipated, although another about 500 km east of the Lesser Antilles remains strong. The circulation near Cape Breton was a weakening Tropical Storm Bonnie. A strong tropical wave, which developed into Earl in the Gulf of Mexico the next day, was located over the Yucatan peninsula.

2. General Assessment of dropwindsonde impact

A. GFDL model

Figure 2 shows the GFDL forecast tracks for Hurricane Danielle, and Table 1 shows the errors and impact of the synoptic surveillance mission. The results are mainly positive, with large improvements at all times after 24 h. The run with none of the dropwindsonde data is qualitatively better than that with all the dropwindsonde data, but the additional data slowed the motion in the forecast allowing for the forecast improvements. The upper-tropospheric data degraded the forecasts at all forecast times.

B. VICBAR

Figure 3 shows the VICBAR forecast tracks for Hurricane Danielle, and Table 2 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data degraded the forecasts at all times, though the upper-tropospheric data provided a better forecast than the run with all these data.

C. GSM

Figure 4 shows the GSM forecast tracks for Hurricane Danielle, and Table 3 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data improved the GSM forecast track during the first and last days of the forecast. All the forecasts were particularly good in this case. The upper-tropospheric data improved the forecast at all forecast times except 48 h and after 84 h.

D. Intensity

Table 4 shows the GFDL intensity forecast errors and impact of the synoptic surveillance mission. The dropwindsonde data had a mixed impact, improving the forecasts at all times except 24 and 84 h. The upper-tropospheric data improved the forecasts at all times except 96 h. Tuleya and Lord (1997) 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 two largest perturbations are associated with the Tropical Storm Bonnie east of New England and Hurricane Howard in the North Eastern Pacific Ocean. Near Danielle, large perturbations exist in the region between Danielle and the subtropical ridge to the north and between Danielle and the trailing anticyclone behind Danielle. Other areas of large perturbation are in the subtropical ridge axis to the northwest of Danielle, near the wind maximum over southern Florida associated with the tropical wave over Yucatan, and with the wave axis itself. Of these, the only three areas of large perturbation which were well-sampled were the ones to the northwest of the center of Danielle corresponding to the axis of the subtropical ridge, the area between the subtropical ridge and the circulation of Danielle, and the wind maximum over Florida.

Two sets of model runs have been performed. The first, the TG run, includes the dropwindsonde taken in and around well-sampled areas of large ensemble perturbation. Because the southeastern portion of the perturbation near Danielle was not fully sampled, only those observations within and around the three regions northwest of Danielle were included (all the dropwindsondes represented by closed circles in Fig. 5). The other, the NT run, includes the complement of the TG run, with dropwindsondes represented by open circles in Fig. 5. Results are shown in Table 1, Table 2, Table 3, and Table 4 and Fig.2, Fig.3, Fig. 4. The TG run provided better forecasts than the run including all dropwindsonde data at all times in the GFDL, through 36 h in VBAR, and through 96 h in the GSM.

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 largest differences form a ring about 750 km in radius around Danielle. However, much of this impact maximum, from the south to northeast of Danielle, is outside the area of dropwindsonde locations. Only the maximum difference to the northwest of Danielle is fully substantiated by the data. The spread of the impact away from the dropwindsonde locations suggests that the data assimilation has allowed the dropwindsonde data from surrounding areas to influence the initial conditions there. Since the difference maximum to the southeast of Danielle is in an area of large ensemble perturbation, this aliasing may amplify in time and negatively impact the forecasts.

Figure 7 shows that, by 24 h into the forecast, the differences between the forecasts with and without the dropwindsonde data have rotated around and toward Danielle, and amplified. A small maximum impact near Great Inagua, and another about 1000 km southeast of Danielle are residuals from the abovementioned alising problem. These results generally confirm that the largest (smallest) ensemble perturbations correspond to amplifying (decaying) modes in the model, although large impacts were not seen in areas of small ensemble perturbation.

4. Conclusion

The dropwindsonde data obtained during the synoptic surveillance mission for Hurricane Danielle at nominal time 30 August 1998 0000 UTC has provided mainly positive results throughout the five day forecast of Hurricane Danielle in all three models. The MRF ensemble forecasting system suggested that data surrounding Danielle and in the subtropical ridge to the north of Danielle would have the greatest impact on the Danielle forecast. Despite sampling in all quadrants of Danielle, the southeasternmost of these target locations was not completely sampled, and some aliasing in this area may have prevented even larger improvements, as suggested by the results of model runs without dropwindsonde data in this area.

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