IMPACT ON HURRICANE TRACK AND INTENSITY FORECASTS OF GPS DROPWINDSONDE OBSERVATIONS FROM THE SYNOPTIC SURVEILLANCE MISSION INTO HURRICANE BONNIE ON 23 AUGUST, 1998.

Sim D. Aberson

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

4301 Rickenbacker Causeway
Miami, Florida


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1. Synoptic situation

Hurricane Bonnie developed from an easterly wave midway between the Cape Verde Islands and the Leward Islands on 19 August, 1998. It moved westward, strengthening slowly into a major hurricane four days later. Due to the potential threat to the Carolina coastline, a synoptic surveillance mission was tasked for nominal time 24 August 1998 0000 UTC. At this time, Bonnie was nearly stationary in the subtropical ridge about 350 km north of the Turks and Caicos Islands. A broad region of westerlies extended well to the north of the hurricane center north of 35°N. A small cold low was located over the northeastern Gulf of Mexico (Fig. 1).


2. General Assessment of dropwindsonde impact

A. GFDL model

Figure 2 shows the GFDL forecast tracks for Hurricane Bonnie, and Table 1 shows the errors and impact of the synoptic surveillance mission. The results are mainly negative. The dropwindsonde data improved the forecasts only at 12 and 120 h. The upper-tropospheric data improve the forecasts only at 12, 96, and 120 h. None of the runs correctly forecast the landfall of Bonnie near Wilmington three days later.

B. VICBAR

Figure 3 shows the VICBAR forecast tracks for Hurricane Bonnie, and Table 2 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data improved the forecasts at all times. However, even with the dropwindsonde data, the model failed to forecast the landfall of Bonnie on the North Carolina Coast 72 h into the forecast. The upper-tropospheric data had little impact on the forecast.

C. GSM

Figure 4 shows the GSM forecast tracks for Hurricane Bonnie, and Table 3 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data had a positive impact on the GSM forecast track after 12 h until the cyclone tracker lost the center. The upper-tropospheric data had positive at all forecast times. None of the runs correctly forecast the landfall of Bonnie near Wilmington three days later.

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 12, 24, 84, and 96 h. The upper-tropospheric data improved the forecasts at all times except 72, 96, and 120 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) . One large perturbation corresponds to the circulation of Bonnie itself, with smaller perturbation maxima extending through the axis of the subtropical ridge east and west of the vortex center, and another in the cull region just south of the cold low in the northeastern Gulf of Mexico. The maximum in the northwestern Gulf of Mexico was associated with Hurricane Charley which had moved inland. The maxima extending from 28°N 45°W southwestward is associated with a mid-latitude trough and upper low. The large maximum east of the Lesser Antilles was associated with a tropical wave which the MRF tried to intensity (and which eventually became Hurricane Earl in the Gulf of Mexico). Small maxima in the northern Atlantic Ocean were associated with vorticity maxima revolving around the mid-Atlantic trough. The three-plane mission was able to sample the areas of high-pressure to the east and west of Bonnie and the cull region in the Gulf of Mexico. The southern half of the large perturbation associated with the circulation of Bonnie was not sampled due to the logistical difficulty of sampling around both sides of the Greater Antilles.

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 southern portions of the perturbation corresponding to Bonnie was not sampled, only those observations within the two regions of high pressure to the east and west of Bonnie and in the cold low in the northeastern Gulf of Mexico, 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, Table 4 and Figure2, Figure 3, Figure 4. The TG run provided better forecasts than the run indluding all dropwindsonde data at all forecast times in the GFDL. However, the TG run provided a worse forecast than the run including all the dropwindsonde data at all times in VBAR, and all times except 12 h in the GSM.

Because the two P3 aircraft penetrated the center of Hurricane Bonnie, and sampled all four quadrants of the vortex core, another run, the T1 run, was made, including all the dropwindsonde data within the large perturbation pertaining to the storm. Tracks are shown in Figure 8, Figure 9, Figure 10. These forecasts were similar to the AL runs for all three models.

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 are at the center of Bonnie, in Bonnie's outer circulation to the southwest of the center, in the weak extension of the subtropical ridge just off the Carolina coast, and in the vorticity maximum to the south of Nova Scotia. The impact extends almost 1000 km from the nearest data to the northeast of the flight pattern, and to the south of Bonnie, suggesting that the data assimilation has allowed the dropwindsonde data from surrounding areas to influence the initial conditions there. The area to the northeast is mainly in an area of small ensemble perturbation, so this difference is likely to decay. However, the difference to the south of Bonnie is wholly inside a region of large ensemble perturbation, suggesting that this may have a negative impact on the forecast. Figure 7 shows that, by 24 h into the forecast, the differences between the forecasts with and without the dropwindsonde data in the two northern maxima initially located in areas of small ensemble perturbation have decayed, whereas the maxima in the hurricane circulation have amplified. The two maxima in the northeastern Caribbean Sea are due to small differences in the unstable development of the tropical wave moving through the region which corresponded to a large ensemble perturbation. These results seem to confirm that the largest (smallest) ensemble perturbations correspond to amplifying (decaying) modes in the model.


4. Conclusion

The dropwindsonde data obtained during the synoptic surveillance mission for Hurricane Bonnie at nominal time 24 August 1998 0000 UTC has provided mainly positive results except in the GFDL model. The MRF ensemble forecasting system suggested that data surrounding Bonnie, in the areas of high pressure to the east and west of Bonnie, and in the upper-level low in the northeastern Gulf of Mexico would have the greatest impact on the Bonnie forecast. Due to logistical difficulties, only the northern half of the Bonnie circulation was sampled during the synoptic surveillance mission. This led to aliasing of the data into these data-sparse regions, possibly causing the negative impact of the data in the GFDL model.


Table 1
Track forecast errors for the no dropwindsonde GFDL control (GFNO), the all dropwindsonde run (GFAL), the lower-level dropwindsonde run (GFP3), and the run with only targeted observations (GFTG), and the percent improvement of the latter three over the control.
Forecast
time (h)
GFNO
Error (km)
GFAL Error (km)
(% Improvement)
GFP3 Error (km)
(% Improvement)
GFTG Error (km)
(% Improvement)
1259.44.( 25%)46.( 22%)46.( 22%)
2489.89.( 0%)78.( 12%)67.( 25%)
36136.140.( -3%)140.( -3%)118.( 13%)
48187.240.(-28%)240.(-28%)131.( 30%)
72828.905.( -9%)891.( -8%)595.( 28%)
841400.1503.( -7%)1490.( -6%)1139.( 19%)
961867.1966.( -5%)1973.( -6%)1643.( 12%)
1202143.2120.( 1%)2176.( -2%)2043.( 5%)

Table 2
Track forecast errors for the no dropwindsonde VBAR control (VBNO), the all dropwindsonde run
(VBAL), the lower-level dropwindsonde run (VBP3), and the run with only targeted observations (VBTG), and the percent improvement of the latter three over the control.
Forecast
time (h)
VBNO
Error (km)
VBAL Error (km)
(% Improvement)
VBP3 Error (km)
(% Improvement)
VBTG Error (km)
(% Improvement)
12 39. 30.( 23%)30.( 23%)68.(-74%)
24 51. 32.( 37%)32.( 20%)60.(-18%)
36 162. 125.( 23%)125.( 23%)153.( 6%)
48 285. 248.( 13%)248.( 13%)270.( 5%)
72605.537.( 11%)539.( 11%)537.( 11%)
841008. 890.( 12%)882.( 13%)894.( 11%)
961547.1349.( 13%)1349.( 13%)1382.( 11%)
1082211.1952.( 12%)1958.( 11%)2032.( 8%)

Table 3
Track forecast errors for the no dropwindsonde GSM control (GSNO), the all dropwindsonde run (GSAL), the lower-level dropwindsonde run (GSP3), and the run with only targeted observations (GSTG), and the percent improvement of the latter three over the control.
Forecast
time (h)
GSNO
Error (km)
GSAL Error (km)
(% Improvement)
GSP3 Error (km)
(% Improvement)
GSTG Error (km)
(% Improvement)
12 78. 87.(-12%)87.(-12%)78.( 0%)
24 190. 82.( 57%)96.( 49%)134.( 29%)
36 318. 118.( 63%)118.( 63%)243.( 24%)
48 457. 141.( 69%)145.( 68%)335.( 27%)
72 289. 641.
841375.1854.
961836.2212.
1082170.2418.
1202158.2171.

Table 4
Intensity forecast errors for the no dropwindsonde GFDL control (GFNO), the all dropwindsonde run (GFAL), the lower-level dropwindsonde run (GFP3), and the run with only targeted observations (GFTG), and the percent improvement of the latter three over the control.
Forecast
time (h)
GFNO
Error (kn)
GFAL Error (kn)
(% Improvement)
GFP3 Error (kn)
(% Improvement)
GFTG Error (kn)
(% Improvement)
1297( 22%)8( 11%)9( 0%)
2475( 29%)8( -14%)7( 0%)
3647( -75%)8(-100%)2( 50%)
48810( -25%)11( -38%)11(-38%)
721718( -6%)16( 6%)18( -6%)
8468( -33%)8( -33%)6( 0%)
961112( -9%)10( 9%)9( 18%)
12012(-100%)0( 100%)1( 0%)


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