Click here for N42RF
catalog of sonde drops.
Click here for N42RF zipped post-processed sonde data.
Click here for N43RF catalog of sonde drops.
Click here for N43RF zipped post-processed sonde data.
Click here for N49RF catalog of sonde drops.
Click here for N49RF zipped post-processed sonde data.
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 follow-on missions the next two days. On the third day, Danielle was moving slowly north-northwestward about 400 km northeast of Nassau, embedded within the subtropical ridge (Fig. 1). The strong cold low located about 500 km east of the Lesser Antilles the previous day remains strong, and has moved northwestward. A strong tropical wave, which developed into Earl later on the 31st, was located in the Bay of Campeche.
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 negative, with improvements only at 84 and 96 h. The upper-tropospheric data improved the forecasts at all forecast times except 96 h.
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.
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.
Table 4 shows the GFDL intensity forecast errors and impact of the synoptic surveillance mission. The dropwindsonde data had a negative impact, degrading the forecast at all times. The upper-tropospheric data improved the forecasts at 24, 36, 72, and 84 h. Tuleya and Lord (1997) also showed modest improvements to GFDL model intensity forecasts in the HRD synoptic flow cases.
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 largest perturbation is associated with Hurricane Howard in the North Eastern Pacific Ocean. The area of relatively large perturbation centered over the Yucatan Peninsula is associated with the tropical wave developing into Hurricane Earl. Near Danielle, the largest perturbation exists in the region between Danielle and the subtropical ridge to the northeast. Other large perturbations are in the trough connecting Danielle and the upper-level cold low to the east, in the trough across central Florida, and in the axis of the subtropical ridge extending eastward from the Carolina coastline. Of these, the three areas of large perturbation which were well-sampled were the ones to the northeast of Danielle, the one corresponding to the axis of the subtropical ridge, the trough across northeastern Florida. In the last two cases, regularly available rawinsondes over North America contribute to the sampling of these areas.
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 eastern portion of the perturbation near Danielle was not fully sampled, only those observations within and around the three regions north 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, and Fig. 4. The TG run provided better forecasts than the run including all dropwindsonde data at all times through 72 h in the GFDL, at 24 and 120 h in VBAR, and at 120 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, relatively large impacts around the southern side extend outside the area of dropwindsonde locations, though the maximum differences to the south and northeast of Danielle are 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 nearby 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 converged to form one maximum, and have moved along with Danielle, slowly amplifying. A small maximum impact to the northeast of Danielle corresponds to the initial maximum in the same general location. New small maxima near the northern Bahamas and northeast of Puerto Rico correspond to small differences amplifying rapidly as they reach areas of large perturbation corresponding to the upper-level low northeast of the islands and the large tropical wave which eventually became Hurricane Earl. These results generally confirm that the largest (smallest) ensemble perturbations correspond to amplifying (decaying) modes in the model.
The dropwindsonde data obtained during the synoptic surveillance mission for Hurricane Danielle at nominal time 31 August 1998 0000 UTC has provided mainly positive results throughout the five day forecast of Hurricane Danielle in all the GSM and VBAR, and large negative results in the GFDL. The MRF ensemble forecasting system suggested that data surrounding Danielle and in the subtropical ridge to the north of Danielle, and in the trough across northern Florida 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.
|GFAL Error (km)|
|GFP3 Error (km)|
|GFTG Error (km)|
|72||497.||536.||( -8%)||568.||(-14%)||535.||( -8%)|
|84||449.||376.||( 16%)||387.||( 14%)||377.||( 16%)|
|96||405.||220.||( 46%)||214.||( 47%)||252.||( 38%)|
|VBAL Error (km)|
|VBP3 Error (km)|
|VBTG Error (km)|
|12||22.||22.||( 0%)||22.||( 0%)||22.||( 0%)|
|24||97.||87.||( 10%)||87.||( 10%)||82.||( 15%)|
|36||145.||115.||( 21%)||110.||( 24%)||120.||( 17%)|
|48||235.||211.||( 10%)||203.||( 14%)||227.||( 3%)|
|72||553.||515.||( 7%)||499.||( 10%)||527.||( 5%)|
|84||557.||537.||( 4%)||514.||( 8%)||545.||( 2%)|
|96||519.||543.||( -4%)||513.||( 1%)||551.||( -6%)|
|108||620.||624.||( -1%)||655.||( -6%)||709.||( -14%)|
|120||685.||786.||( -15%)||734.||( -7%)||779.||( -14%)|
|GSAL Error (km)|
|GSP3 Error (km)|
|GSTG Error (km)|
|12||50.||29.||( 42%)||29.||( 42%)||40.||( 20%)|
|24||92.||83.||( 10%)||77.||( 16%)||106.||( -15%)|
|36||123.||58.||( 53%)||66.||( 46%)||78.||( 37%)|
|48||235.||107.||( 54%)||133.||( 43%)||160.||( 32%)|
|72||349.||244.||( 30%)||224.||( 36%)||269.||( 23%)|
|84||472.||268.||( 43%)||242.||( 49%)||308.||( 35%)|
|96||702.||332.||( 53%)||358.||( 49%)||403.||( 43%)|
|108||1042.||596.||( 43%)||612.||( 41%)||673.||( 35%)|
|GFAL Error (kn)|
|GFP3 Error (kn)|
|GFTG Error (kn)|
|12||23||26||( -13%)||26||( -13%)||26||( -13%)|
|24||14||15||( -7%)||16||( -14%)||16||( -14%)|
|36||7||7||( 0%)||8||( -14%)||7||( 0%)|
|84||13||16||( -23%)||18||( -38%)||18||( -38%)|
|96||16||23||( -44%)||23||( -44%)||25||( -56%)|
Return to 1998 assessment page
Return to HRD home page