The two components of overall hurricane activity that we examine are the seasonal number of hurricanes (H) and the seasonal number of intense (or major) hurricanes (MH). To predict the number of hurricanes we use an ordinaryleastsquares linear regression to estimate the number of tropicalonly hurricanes ([^H]_{T}) to which we add a seasonal average number of baroclinicallyenhanced hurricanes ([`H]_{B}). The model can be expressed as


To predict the number of major hurricanes we use a Poisson regression expressed as

In addition to basinwide activity we predict activity in the Caribbean Sea, the Gulf of Mexico, and along the southeast (Cape Hatteras south to Key West) and entire U.S. coast. We use logistic regression to predict hurricane landfalls along the coast and the presence or absence of major hurricanes in the Gulf, Caribbean and along the entire coast. As with the approach taken for basinwide major hurricane activity, we express the subbasin forecasts in terms of probabilities (Lehmiller et al. 1997).
Logistic regression is a statistical model to predict yes/no events by estimating coefficients for several predictor variables. ``Yes" indicates the occurrence of the event. Here we use a maximum likelihood technique to obtain the coefficients. A logistic regression can be expressed as

Here we describe our predictions for the 2001 North Atlantic hurricane season. Regression coefficients are estimated from data over the period 19502000. These coefficients along with the new predictor values for 2001 are given in Table . The model indicates that there will be 6 hurricanes during the 2001 North Atlantic season. The forecast is based on rounding the regression forecast of the number of tropicalonly hurricanes to 3 and adding to this number the rounded number of baroclinicallyenhanced (BE) hurricanes. The average number of BE hurricanes over the period 19502000 is 2.9. A forecast of intense hurricane activity based on the above regression coefficients is presented in the form of estimated probabilities. The 2001 probabilities are quite different from 2000 probabilities indicating a decreased likelihood of intense hurricane activity during 2001. The expected number of intense hurricanes is 1. More specifically, the Poisson model estimates that there is a 76% chance of observing less than 2 intense hurricanes during 2001.
H_{T}  MH  H_{C}  
i. Predictor Term in Equation  b_{i}  g_{i}  a_{i}  x_{i} 
0. constant  4.315  1.030  1.036   
1. AugNov 00 Gulf of Guinea rainfall  2.681  0.879  2.844  0.50 sd 
2. AugSep 00 West Sahel rainfall    0.455  1.002  0.70 sd 
3. 50mb zonal wind at 10^{°}N fcst for Sep 01    0.022    12 ms^{1} 
4. 30mb zonal wind at 10^{°}N fcst for Sep 01        10 ms^{1} 
5. wind shear at 10^{°}N fcst for Sep 01  0.106  0.033    +2 ms^{1} 
The logistic model for occurrence of a hurricane in the Caribbean indicates a 25% chance of observing at least one hurricane during 2001 in this part of the North Atlantic basin. This is below the longterm average probability (19502000) of nearly 61%. No subjective adjustments are made to these forecasts. The forecast of near or slightly below average activity is based on negative rainfall values. Forecasts will be updated prior to the start of the 2001 hurricane season.
No. of Intense Hurricanes (IH)  0  1  2  3  4  ³ 5 
2000 Forecast Probabilities  0.260  0.350  0.236  0.106  0.036  0.012 
2001 Forecast Probabilities  0.389  0.367  0.173  0.055  0.013  0.003 
Table shows the predicted versus actual values from the 2000 North Atlantic hurricane season based on forecasts issued by our group at Florida State University (FSU). Overall our performance was fair. The December and August models under forecast the observed level of overall activity, particularly the number of hurricanes. The number of major hurricanes was only slightly off the mark. Our June forecast, based on timeseries analysis (Elsner et al. 1998), was considerably more accurate. Subbasin activity was also somewhat different than predicted, though we correctly anticipated that neither the Gulf of Mexico nor the U.S. coast would get hit by a major hurricane during the 2000 season.
Month Issued  
Predictand  December  June  August  Climatology  Actual 
Hurricane  5  7  4  5.9  8 
Major Hurricane  2  2  2.2  3  
H_{C}  34%  61%  Yes  
IH_{C}  47%  48%  Yes  
IH_{G}  39%  46%  No  
H_{SE}  62%  44%  No  
IH_{US}  31%  35%  No 
Table lists the hurricanes of the 2000 North Atlantic hurricane season. There were 3 baroclinically influenced hurricanes; hurricanes Florence and Michael were baroclinicallyinitiated and hurricane Gordon was baroclinically enhanced (Elsner and Kara 1999). In addition, there were 5 tropicalonly hurricanes, of which 3 were major. This is the first year since 1994 that no storm reached the coast at hurricane intensity.
Cat.  Type  Name  Dates  U.S. hurricane? 
IH3  TO  Alberto  August 423  No 
H1  TO  Debby  August 2024  No 
H1  BI  Florence  September 1117  No 
H1  BE  Gordon  September 1518  No 
IH4  TO  Isaac  September 21October 1  No 
H1  TO  Joyce  September 25October 2  No 
IH4  TO  Keith  September 29October 6  No 
H2  BI  Michael  October 1719  No 
Although our group at FSU has been issuing seasonal hurricane forecasts since 1993, the earlier forecasts were issued only for the number of intense hurricanes. Table shows the December forecast probabilities from the Poisson model over the past 8 years. The bold numbers indicate the verification. Forecasts were very good for the 1993, 1997, and 1999 seasons, good for the 1994 and 1995 seasons, fair for the 1998 and 2000 seasons, and poor for the 1996 season.
IH  Climate  1993  1994  1995  1996  1997  1998  1999  2000 
0  15.0  16.8  15.9  4.7  25.9  16.0  35.5  2.8  18.4 
1  28.4  29.9  29.2  14.4  35.0  29.4  36.8  10.1  31.2 
2  27.0  26.7  26.9  22.0  23.6  26.9  19.0  18.0  26.4 
3  17.1  15.9  16.5  22.4  10.7  16.4  6.6  21.4  14.9 
4  8.1  7.1  7.6  17.1  3.6  7.5  1.7  19.0  6.3 
5+  4.4  3.3  3.7  19.4  1.2  3.8  0.4  28.7  2.8 
Acknowledgments: Predictor values were obtained from the Colorado State University (CSU) forecast group. Our research began as an academic exercise to compare statistical schemes used at CSU with different algorithms. We acknowledge the encouragement given to our group by the folks at the Risk Prediction Initiative (RPI) to continue operational seasonal forecasting. Special thanks go to Richard Murnane and Anthony Knap in this regard. Partial support for this work comes from the RPI and from the U.S. National Science Foundation.