IMPACT ON HURRICANE TRACK AND INTENSITY FORECASTS OF GPS DROPWINDSONDE OBSERVATIONS FROM THE SYNOPTIC SURVEILLANCE MISSION INTO TROPICAL STORM ERIKA ON 06 SEPTEMBER, 1997

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

Tropical Storm Erika developed from an easterly wave south of the Cape Verde Islands on 30 August, and moved westward, strengthening slowly. Due to the potential threat to the Virgin Islands and Puerto Rico, a synoptic surveillance mission was tasked for nominal time 06 September 1997 0000 UTC. At that time, Erika was embedded in the westerlies to the south of the subtropical ridge, about 300 km east of Anguilla. A strong trough was located along the U. S. east coast, which could impact the forecast later in the period (Fig. 1).

2. General Assessment of dropwindsonde impact

A. GFDL model

Figure 2 shows the GFDL forecast tracks for Tropical Storm Erika, and Table 1 shows the errors and impact of the synoptic surveillance mission. The results are mixed, with improvements only during the crucial 24 to 36 h range when hurricane watches and warnings are posted. The dropwindsonde data caused the forecast track to be further south and west, closer to the best track, and slightly slower, in turn causing the forecast degradation, than the forecast without the dropwindsonde data. The upper-tropospheric data improved the forecast for all times except for 12, 24, and 120 h.

B. VICBAR

Figure 3 shows the VICBAR forecast tracks for Tropical Storm Erika, and Table 2 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data improved the forecast at all times except 72 h, though the upper-tropospheric data degraded the forecast at all times after 36 h.

C. GSM

Figure 4 shows the GSM forecast tracks for Tropical Storm Erika, and Table 3 shows the errors and impact of the synoptic surveillance mission. The dropwindsonde data degraded the GSM forecast track at all times. The dropwindsonde data pushed the forecast further to the south and west, away from the best track. The upper-tropospheric data also had a negative impact on the forecast track at all times.

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 72 and 96 h. The upper-tropospheric data improved the forecast at these same times. 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 largest perturbations correspond to the trough exiting the U.S. east coast. A small perturbation corresponds to the western side of the circulation of Erika, and another to the east corresponds to the Rossby wave to the southeast of Erika. The only one of these features which was sampled during the synoptic surveillance mission was the trough region off the U.S. east coast.

Two sets of model runs have been performed. The first, the TG run, includes the dropwindsonde data taken within and around the trough off the northeastern U. S. coast (all dropwindsondes north of 32°N). The other, the NT run, includes the complement of the first set. 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 forecast times in the GSM. However, the TG run provides better forecasts than the run including all the dropwindsonde data at 12 h and from 72 to 108 h in the GFDL model, and only at 120 h in VBAR.

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 difference is to the north and northwest of Erika, with other smaller maxima off the southeastern U.S. coast in the trough. The largest difference extends southward and eastward from the dropwindsonde locations toward the circulation of Erika, and this extension of the data impact helps to explain the degradation of the GSM forecast with the dropwindsonde data. Figure 7 shows that, by 24 h into the forecast, the differences between the forecasts with and without the dropwindsonde data in the region of largest initial perturbation has decayed, leaving a maximum difference resulting from the difference in the location of Erika in the different runs. Though the data taken in the trough mostly improved the forecasts, the differences were small due to the slight impact near the tropical storm of these distant data.

4. Conclusion

The dropwindsonde data obtained during the synoptic surveillance mission for Tropical Storm Erika at nominal time 06 September 1997 0000 UTC has provided mixed results. The MRF ensemble forecasting system suggests, that data around the circulation of Erika would have the greatest positive impact on the forecast. However, this area was not sampled during the synoptic surveillance missions. Dropwindsonde data obtained to the north of Erika was spread by the data assimilation to the south and east, pushing the forecasts with the dropwindsondes erroneously toward the south. Forecasts using only the data around the trough off the U. S. east coast, a region of large perturbations in the MRF ensemble forecasting system, provide better forecasts at most times than those including all the dropwindsonde data, though the differences are small due to the remoteness of the data from the storm center.


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)
1231.46.(-48%)35.(-13%)31.( 0%)
2486.63.( 27%)53.( 38%)71.( 17%)
36106.66.( 38%)84.( 21%)85.( 20%)
4894.103.(-10%)136.(-45%)126.(-34%)
72177.222.(-25%)254.(-44%)220.(-24%)
84326.397.(-22%)403.(-24%)389.(-19%)
96578.636.(-10%)648.(-12%)624.( -8%)
1201131.1158.( -2%) 1131.( 0%) 1183.( -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)
1239.31.( 21%)31.( 21%)31.( 21%)
24114.100.( 12%)100.( 12%)107.( 6%)
36123.107.( 13%)114.( 7%)123.( 0%)
48103.84.( 18%)73.( 29%)93.( 10%)
72284.287.( -1%)244.( 14%)293.( -3%)
84531.521.( 2%)477.( 10%)537.( -1%)
96810.766.( 5%)715.( 12%)800.( 1%)
1081044.989.( 5%)919.( 12%)1033.( 1%)
1201302.1264.( 3%)1145.( 12%)1285.( 1%)


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)
1299.138.(-39%)138.(-39%)138.(-39%)
2492.183.(-99%)161.(-75%)146.(-59%)
36156.269.(-72%)246.(-58%)229.(-47%)
48256.373.(-46%)357.(-39%)304.(-19%)
72537.617.(-15%)603.(-12%)547.( -2%)
84744.814.( -9%)747.( 0%)742.( 0%)
96895.959.( -7%)902.( -1%)862.( 4%)
1081023.1072.( -5%)1024.( 0%)1001.( 2%)
1201087.1222.(-12%)1191.(-10%)1115.( -3%)


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 (km)
GFAL Error (kn)
(% Improvement)
GFP3 Error (kn)
(% Improvement)
GFTG Error (kn)
(% Improvement)
121313( 0%)12( 8%)14( -8%)
241417(-21%)17(-21%)13( 7%)
361713( 24%)13( 24%)13( 24%)
4811( 0%)0(100%)3(-200%)
722724( 11%)26( 4%)24( 11%)
842426( -8%)25( -4%)24( 0%)
962421( 13%)22( 8%)21( 13%)
12067(-17%)7(-17%)3( 50%)


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