The Robert C. Sheets National Hurricane Center Hurricane Probability Program 1320 South Dixie Highway Coral Gables, FL 33146

1. Introduction

Recent census studies have shown a general shift of the U.S. population to the Sun Belt, with a large number of these peo- ple moving to coastal counties. This trend has continued through the decades of the 1960s and the 1970s (Fig. 1). For instance, the coastal county population of Florida was 3 853 244 in 1960,5 414 868 in 1970, and 7 702 337 in 1980, or nearly a doubling of the population in these hurricane- prone areas during the past 20 years! Fortunately, Florida has experienced a considerable decline in hurricane activity during this same period. However, these population concen- trations cause concern about the adequacy of the National Weather Service (NWS) hurricane warning programs for FIG. 1. United States population trends, 1960 to 1970 and 1970 providing sufficient warnings and guidance for the protec- to 1980. tion of lives and property in these highly populated regions. The NWS attempts to provide a minimum of 12 hours of daylight warning for coastal communities to prepare for a hurricane. Community action plans have traditionally been Simply issuing warnings earlier does not seem to be a solu- built around these warnings. A highly coordinated effort in- tion to these problems. Longer warning lead times would re- volving the NWS, state, and local officials is required in this sult in much larger over-warning. For instance, the average warning and action process. This system has worked quite Atlantic area official forecast errors for the well over the past several years to minimize loss of life from period of 1970 to 1979 was 109 n mi (202 km), 244 n mi (452 hurricanes. However, recent studies based upon numerical km), and 377 n mi (699 km), respectively, for 24-, 48- and storm surge model (SLOSH) simulations (Crawford, 1979, 72-hour forecasts (Neumann and Pelissier, 1981a). Instead and Lawrence, 1984) have shown that lead times much of the approximate 50 to 60% over-warning that currently ex- longer than the present 12 hours are required in several ists, over-warning would be as much as 90% at 72 hours lead communities along the coast from Texas to . time. Costs of preparations for a typical warned area for an Hurricanes are rather large weather systems that can si- average-sized hurricane are nearly $50 million; these costs multaneously or nearly simultaneously affect several com- would multiply if longer lead time warnings were issued munities along or near the coastal region of landfall. Evacua- where their extent was based upon average errors for the ex- tion efforts in one community generally affect similar actions tended forecast period. Many residents would soon ignore in adjacent communities since they often share common warnings, waiting until the storm's winds or were affect- roadway systems and places of refuge. Many areas have now ing the area before taking action. In some areas, such courses taken a regional approach to evacuation planning. Results of of action could result in considerable loss of life and the few comprehensive evacuation studies that have been property. completed (many remain to be done) indicate a problem of One further factor to be considered is that the majority of major proportions. Evacuation of only the vulnerable resi- coastal residents do not need to be evacuated. These resi- dents of communities such as the Tampa Bay area, the Fort dents and most businesses generally only need time to close Myers area, the Florida Keys, and Miami and Ft. Lauder- up and "board up." The familiar National Weather Service dale, Florida, as well as Galveston, Texas, and Hilton Head, hurricane watch and warning program is generally adequate South Carolina, require lead times of 20 to 30 hours or more. for these purposes. A major disadvantage of this system, es- Similar conditions are expected for several other coastal pecially for large businesses and industry as well as local gov- communities. Furthermore, these conditions will likely worsen ernments, is that it permits only a qualitative assessment of as coastal populations continue to grow. In addition, many risk. Community officials and private industry decision- businesses and industry often require long lead times to pre- makers would like to be able to assess their risks in a quantita- pare for specified storm conditions. Clearly, the standard tive fashion so that costs of preparation versus potential loss hurricane watch and warning programs do not provide suffi- analyses can be made for determining where, what, and when cient lead times or quantitative information to meet the actions, if any, should be taken. needs of many potential users of these products. With these factors in mind, the National Weather Service decided to continue the familiar hurricane watch and warn- ing program for use by the general public. In addition, a pro- © 1985 American Meteorological Society gram has been initiated which provides quantitative infor-

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Unauthenticated | Downloaded 10/05/21 06:54 PM UTC Bulletin American Meteorological Society 5 mation for assessment of the likelihood that the center of the storm will pass within a prespecified distance of selected coastal cities or island locations. This information is primar- ily intended for government officials and other decision- makers, but is made available through public distribution to assure that as many possible users as practicable will have ac- cess to these data.

2. Hurricane probabilities—background

There are several possible approaches to determine the prob- ability or likelihood that a hurricane will strike a given point FIG. 2. Schematic of overlapping 50% probability ellipses (ap- in time and space. Several methods have been developed proximated by circles) for a hypothetical hurricane forecast to move based upon climatological records (Cry, 1961; Hope and at 10 knots (5 m/sec). Neumann, 1968 and 1971; Simpson and Lawrence, 1971; Jarvinen and Neumann, 1978). Refinements have included were composited relative to the forecast position at the dis- development of statistical prediction models for future crete forecast verification times of 12, 24, 48, and 72 hours. movement of storms in probabilistic form based upon these Crutcher et al. (1981) derived three mode bivariate normal climatological data sets (Hope and Neumann, 1970); other distributions from these data. Simplified versions (i.e., circu- refinements include the addition of current and predicted en- lar distributions assumed) of these distributions were utilized vironmental conditions (Neumann and Pelissier, 1981b). The in the U.S. Navy program (Jarrell, 1981) and have been operational forecaster uses this information for guidance, as adopted for the present time by the NWS program. The pa- well as other analyses of current and expected changes of en- rameters for these discrete time period distributions are used vironmental conditions in preparing forecasts. Error statis- to interpolate and integrate at three-hourly time steps tics indicate that the resultant official forecasts generally through the 72-hour period with overlapping probabilities show improvements over these other methods. One possible removed. Fig. 2 illustrates the 50% probability ellipses (ap- refinement that would be quantitative and still take into ac- proximated by circles) at 12-hour intervals through 72 count the uncertainty in the forecast track, would be to quan- hours, for a hypothetical hurricane forecast to move at 10 tify the official forecast in probabilistic form. knots (5 m 'S_1). The final probabilities for each site of in- S. J. Kimball (1958) developed a technique for estimating terest are arrived at by proportioning the probabilities ob- the probability of hurricane force winds affecting specific lo- tained from the three distributions based upon parameters cations in the western North Pacific Ocean area. A circular, for the specific storm, such as location and the direction and normal distribution of errors was assumed for determining speed of motion. the probability densities. The size of the wind field of interest To determine probabilities for a given site, one must define was placed over the site, with appropriate offsets for asym- what is meant by a storm affecting a location. The NWS has metric distributions, and the probability was then computed arbitrarily defined a "strike" as the center of the storm mov- for determining if the center of the storm would be within or ing through a zone within approximately 50 n mi (93 km) to have passed through the area of interest in a given time pe- the right of or 75 n mi (139 km) to the left of the site of inter- riod. Nomograms were developed for use at the various loca- est. This zone is approximated by a circle of radius 62.5 n mi tions. The pioneering work of Kimball was followed by re- (116 km) whose center is offset from the point of interest by finements by Appleman (1962). Where Kimball used a single 12.5 n mi (23 km) to account for the frequent asymmetry in forecast error field distribution, Appleman used three distri- the storm's wind field. This area would approximately define butions dependent upon storm latitude. He later applied the the region of hurricane force winds for a typical hurricane. same technique to the western North Atlantic Ocean area. (Storms vary in size and intensity, but the definition of a Nearly 20 years later, Jarrell (1978), using essentially the storm "strike" has been fixed, as described above, to simplify same approach as Kimball, but with modern computer me- the communication and interpretation of the storm proba- thods and more refined estimates of forecast error distribu- bilities.) tions, developed an operational program for estimating trop- Fig. 3 provides a conceptual illustration (not the exact ical cyclone "strike probabilities" for selected United States technique used by NWS) of how probabilities may be derived Department of Defense (DoD) sites in the western North Pa- for specific sites based upon the forecast track and histor- cific Ocean area and later for the North Atlantic area (Jarrell, ical forecast error analyses. The circles drawn around the 1981). approximate locations of Houston, Texas; New Orleans, Louisiana; and Panama City, Florida in Fig. 3 represent zones where the center of the storm would have to be within 3. NWS hurricane probabilities—basis or have passed through during the 48-hour forecast period to be considered a "strike." All cases that were in the circle at The NWS probabilities are a quantification or a measure of exactly the 48-hour verification time would represent the in- the "official" forecast track accuracies. To accomplish this stantaneous 48-hour probability. The combination of these quantification, a ten-year sample of "official" forecast errors cases plus those which have already passed through the area

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this is not a factor, since the circles of interest are "marched" through the probability distributions.) To determine the probability that the storm will affect a particular location within the 48-hour time period given the official forecast track, we apply the associated error distribu- tion in the form of probability densities. In a fashion similar to the computer program (with the exception of the time in- tegration and "marching" of the circles through the proba- bility distributions), the probabilities for the sites of interest can now be determined by summing up the areas contained within the circles and shadow zones. For this case, probabili- ties of approximately 4.5, 13, and 8% are obtained, respec- tively, for Houston, New Orleans, and Panama City. Using a similar approach with the same storm 24 hours earlier where the forecast track places the 72-hour forecast position over New Orleans (not illustrated), probabilities of approximately 9, 10, and 9% are obtained, respectively, for Houston, New Orleans and Panama City. For the same storm situation 24 hours after the time shown in Fig. 3, i.e., the 24 hour forecast position now located over New Or- leans, the approximate probabilities are 8, 38, and 10% for Houston, New Orleans, and Panama City, respectively.

FIG. 3. Hypothetical hurricane forecast track with overlayed 48-hour bivariate normal forecast error distribution. Ellipses are 4. Presentation of hurricane probabilities drawn at 10% intervals. Except for inner ellipse and outer ring, each sector represents 0.5% probability. The NWS hurricane probabilities are not stand-alone pro- ducts. They contain information on the likelihood that the during the 48-hour period (forecast too slow) would repre- storm of interest will move over a given area. They do not con- sent the cumulative probability. The "shadow zone" in the tain information on what conditions may be generated by the wake of the circles approximates the areas where storms storm. Predictions of these conditions are contained in the might now be which have previously passed through the cir- NWS advisories and local statements. For this reason, the cle. (Some storms that passed through the circles would have probabilities are attached in tabular form to the advisories curved out of the "shadow zone," but others would have and should be used as supplements to those advisories. The curved into the zone without having passed through the cir- format of the probability tables is shown in Table 1. This cle. For computations in the U.S. Navy and NWS programs, table was attached to Advisory Number 6, issued for Hurri-

TABLE 1. Probability table attached to public, marine, and military advisories for Hurricane Alicia (1983).

Advisory Number 6 Hurricane Alicia probabilities for guidance in hurricane protection planning by government and disaster officials

Chances of center of Alicia passing within 65 miles of listed locations through 7 p.m. CDT Friday 19 August 1983

Chances expressed in percent . . . times CDT

Additional Probabilities 7p.m. Wed. 7 a.m. Thu. 7 p.m. Thu. Total Coastal Through Through Through Through Through Locations 7 p.m. Wed. 7 a.m. Thu. 7 p.m. Thu. 7 p.m. Fri. 7 p.m. Fri.

Panama City, Fla. X" X X 1 1 Pensacola, Fla. X X 1 1 2 Mobile, Ala. X 1 1 2 4 Gulfport, Miss. X 1 1 2 4 Buras, La. 1 X 1 2 4 New Orleans, La. 1 2 2 2 7 New Iberia, La. 7 3 2 1 13 Port Arthur, Texas 26 1 X X 27 Galveston, Texas 46 X X X 46 Port O'Connor, Texas 27 1 X X 28 Corpus Christi, Texas 12 3 1 1 17 Brownsville, Texas 2 4 1 2 9

"X means less than 1%.

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TABLE 2. Continental United States coastal cities for which prob- would include cost versus potential loss analyses well before abilities are displayed in advisories. any hurricane events. Users would then establish their thresh- old values for each time window and the particular actions to East Coast Gulf Coast be taken before the storm event. The threshold values would Marathon, Fla. Key West, Fla. vary depending upon the storm situation. That is, a weak Miami, Fla. Marco Island, Fla. storm moving off the coast would be treated much differently W. Palm Beach, Fla. Ft. Myers, Fla. than a strong storm approaching the coast, even though the Ft. Pierce, Fla. Venice, Fla. probabilities for the two cases might be identical. Actions for Cocoa Beach, Fla. Tampa, Fla. different times and locations would need to be developed for Daytona Beach, Fla. Cedar Key, Fla. Jacksonville, Fla. St. Marks, Fla. the specific site for different storm scenarios. Savannah, Ga. Apalachicola, Fla. This process is generally too complicated and potentially Charleston, S.C. Panama City, Fla. confusing for casual public use. Therefore, the NWS has rec- Myrtle Beach, S.C. Pensacola, Fla. ommended that the media only report or display the Wilmington, N.C. Mobile, Ala. Morehead City, N.C. Gulfport, Miss. numbers from the far right or total column in the table and Cape Hatteras, N.C. Buras, La. also to include values from neighboring coastal locations Norfolk, Va. New Orleans, La. (Table 2 lists all coastal cities for which probabilities are Ocean City, Md. New Iberia, La. issued) for the assessment of relative threats. The general Atlantic City, N.J. Port Arthur, Texas public should then be urged to only use this information in a City, N.Y. Galveston, Texas Montauk Point, N.Y. Freeport, Texas general sense and to take the specific actions recommended Providence, R.I. Port O'Connor, Texas by local officials who not only should have completed de- Nantucket, Mass. Corpus Christi, Texas tailed analyses required for use of the probabilities, but are Hyannis, Mass. Brownsville, Texas taking many other factors into account about the storm and , Mass. Gulf 29N 85W Portland, Me. Gulf 29N 87W their local communities. Bar Harbor, Me. Gulf 28N 89W Table 3 shows the approximate maximum probability Eastport, Me. Gulf 28N 91W values for each forecast period; that is, these are the probabil- Gulf 28N 93W ities that would result if the storm was forecast directly over Gulf 28N 95W Gulf 27N 96W the location of interest. The actual maximum values will vary Gulf 25N 96W slightly depending primarily upon the speed and location of the storm's approach. What these probabilities indicate is that, with the present level of skill for forecasting the motion of hurricanes, many communities and major industries will cane Alicia at 11 p.m. CDTon Tuesday, 16 August 1983. This need to take actions several times when after-the-fact hurri- advisory contained hurricane warnings and expected condi- cane conditions are not experienced in order to be assured of tions for portions of the Texas and Louisiana coasts. taking actions sufficient to protect lives and property for ac- The format of the probability table is designed to enable tual occurrences. users to assess the likelihood that the storm will move over This limitation of forecasting skill is illustrated in Fig. 4. their location of interest within approximately the next 24, This figure and Table 3 show that the longer a decision- 36, 48, and 72 hours. To obtain the desired probability, the maker can wait to take action, the lower his "false alarm" user simply adds the numbers from left to right up through the rate can be. (The use of the term "false alarm" does not mean time of interest. For example, from Table 1, a decision-maker that a threat did not exist, just that it did not materialize for at New Iberia, Louisiana, would find probability values from the site of interest.) For instance, if some community such as the forecast initial time through 7 p.m. Wednesday to be 7%, the Florida Keys needs 30 to 36 hours to evacuate vulnerable 10% through 7 a.m. Thursday, 12% through 7 p.m. Thurs- residents in advance of a potential hurricane situation, they day, or a total of 13% through the end of the forecast period will have to take such actions with a maximum probability of 7 p.m. Friday. By contrast, the likelihood for the storm level of about 20%. This means that over a long period of moving over Galveston, Texas, was 46% by 7 p.m. Wednes- time, they would have to evacuate about four times for every day with little additional probability thereafter, based upon time they needed to because of an actual hurricane occur- the forecast track issued at this time. rence. If that same community could wait until 24 hours be- One point of caution in the use of these tables is that the intermediate columns do not represent the total probability within those time periods. The values listed represent the addi- TABLE 3. Maximum probability values within forecast periods.0 tional probabilities to be added to the columns to their left to obtain the total probability through the given period. Forecast Period Maximum Probability Values

72 hours 10% 48 hours 13-18% 5. Interpretation and use 36 hours 20-25% 24 hours 35-50% 12 hours 60-80% The probability tables have been designed to aid decision- makers who must start taking actions well before the onset of a Maximum values will vary depending upon speed of storm their prespecified critical storm conditions. Ideal usage movement.

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information that tended to reinforce initiation of actions rec- ommended by local officials. A more comprehensive follow- up series of workshops was conducted in 1984 and included local officials and industrial representatives as well as the media. The time and space probability distributions vary consid- erably depending on the forecasted storm track and its rela- tionship to the coastal geography. The greatest differences occur between storms moving perpendicular to the coast and those moving parallel to the coast. Forecasts issued for Hurricanes Frederic (1979) and Belle (1976) are used here to illustrate these two types of cases. (The NWS probability program was not in effect at the time of these storms. How- ever, the probabilities shown were computed based upon the actual forecasts issued for Hurricanes Frederic and Belle.) In addition, forecasts and associated interpretations and per- ceptions for Hurricanes Alicia and Barry from the 1983 hur- FIG. 4. Maximum probability values and minimum "false alarm" rates for actions taken based upon present forecast skills. (Hatched ricane season are used to illustrate lessons learned during the area indicates a range of maximum values that vary depending upon first year of issuance of hurricane track probabilities to the storm location and forecast speed of movement. general public.

a. Perpendicular case—Frederic (1979) fore taking actions, the "false alarm" rate could be reduced to about two of three or perhaps even one of two. (The insert Hurricane Frederic formed from a disturbance that moved in Fig. 4 gives the minimum possible "false alarm" rates in off the coast of Africa in late August 1979. The storm moved terms of the number of times after-the-fact analyses would westward, under the influence of the trade winds, into the indicate such actions were not required for every ten times Caribbean Sea. On 11 September, the storm was located over actions were taken.) southeast Gulf of Mexico and was forecast to move over the Since the hurricane probabilities can be quite small when north Gulf coast in a little over 48 hours. some communities need to start actions, a concern exists as Figs. 5 and 6 show forecasts, along with total probabilities, to whether or not the general public will take actions recom- through 72 hours as the storm approached the northern mended by local officials when low probabilities exist. In an Gulf of Mexico coast. The probabilities slowly increased and effort to minimize potential problems that might arise from narrowed in on the Mississippi through western Florida the initiation of this program, the NWS conducted several Panhandle coasts through the evening of 11 September. media workshops prior to the 1983 hurricane season to sug- Also, the maximum probabilities moved toward earlier fore- gest ways of interpreting, displaying, and communicating cast periods (from right to left in the probability tables—see this information in conjunction with other information being Sheets, 1984). Fig. 5 shows that the maximum probability provided about the storm. In addition, a pilot study was with the storm forecast to be a little over 48 hours from the conducted to determine how people might react to these prob- coast was 16%, with values of 10% or more from Venice, abilities (Baker, 1984). The results of that study were that Florida, westward through New Iberia, Louisiana. The fore- people generally used the probability data as supplemental cast 12 hours later indicated an increase in the forward speed

FIG. 5. Forecast track and associated total probabilities through 72 hours for Hurricane Frederic for a base time of 1200 GMT 11 Sep- FIG. 6. Same as Fig. 5 except for a base time of 0600 GMT 12 Sep- tember 1979. tember 1979.

Unauthenticated | Downloaded 10/05/21 06:54 PM UTC 6 Bulletin American Meteorological Society of the storm with the predicted landfall within about 30 hours, as compared to 48 hours based upon the forecast issued six hours earlier. Probabilities increased markedly. The highest probability was now 25%, with the region of 10% or more narrowing slightly. Six hours later, a continued in- crease in forward motion was forecast (Fig. 6), with the storm now forecast to be within about 24 hours of the coast. Probabilities increased at an even faster rate, with a maxi- mum value of 45%, and the zone of 10% or larger probabili- ties continued to narrow. A summary of general characteristics of the NWS hurri- cane track probabilities for a storm forecast to move on a perpendicular course toward the coast is as follows:

1) While the storm is forecast to be 72 to 48 hours from the coast: a) probabilities at coastal sites are small (generally less than 10 to 15%); b) probabilities are nearly the same over a broad area of the coast; c) shifts of the 48- to 72-hour forecast points of about 100 miles or less generally result in only small changes in the coastal site probabilities; and d) probabilities change slowly during this period (see Fig. 4 for rate of change of maximum values with time). 2) During the last 36 to 12 hours before forecast landfall: a) probabilities increase more rapidly as the time be- FIG. 7. Forecast track and associated total probabilities through fore expected landfall decreases; 72 hours for Hurricane Belle for a base time of 0600 GMT 8 August b) gradations of probabilities increase with the zone of 1976. higher probabilities narrowing; and c) fluctuations in the 24-hour forecast position in terms of direction or speed of about 60 miles or more cause relatively large (10% or more) changes in probabilities near the coastal region of projected landfall. 3) Examination of the distribution of probabilities along the coast reveal the most likely zone for projected land- fall. This relative assessment will show this zone nar- rowing and maximum probabilities increasing as the storm approaches. b. Paralleling case—Belle (1976)

The case for a paralleling storm is illustrated by use of fore- casts issued for Hurricane Belle (1976). Figs. 7, 8, and 9 show a series of forecasts issued as the storm progressed northward nearly paralleling the east coast of the United States. Here, the progression of increasing probabilities is not only toward shorter forecast periods (from right to left in the probability tables—see Sheets, 1984) as the storm comes closer to the coast, but also shifts northward along the coast as the storm moves northward. Also, note that over-water points in Fig. 7 have higher probability values than do adjacent coastal loca- tions. The highest values along the coast do not always reflect the forecast's most likely track of the storm as they do for the perpendicular case! This is true even when an actual landfall is forecast, but at some location farther along the coast at later forecast periods than would be applicable for the nearby paralleling coastal locations. The reason for this FIG. 8. Same as Fig. 7 except for a base time of 1800 GMT 8 August characteristic of the probabilities is that the probability den- 1976.

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FIG. 10. Forecast track and associated total probabilities through 72 hours for Tropical Storm Alicia for a base time of 1800 GMT 15 August 1983.

shows the first forecast track issued for this storm as well as the cumulative probabilities through 72 hours (fourth col- umn in probability table) for selected coastal cities. These probabilities indicated that, based upon the forecast issued at this time, the chances the storm would affect the coast were about the same from the middle Louisiana coast through Brownsville, Texas. Also noted is that the highest probability FIG. 9. Same as Fig. 7 except for a base time of 1800 GMT 9 August for this 72-hour period was not where the storm was forecast 1976. to cross the coast. This resulted from the fact that the prob- ability densities are largest in the shorter time periods and that during these shorter forecast periods (36 to 48 hours) the sities are much larger in the early forecast periods (12 to 36 storm could affect the upper Texas coast as compared to the hours) as compared to the later periods (48 to 72 hours). longer forecast periods (48 to 72 hours) for the lower Texas The probabilities along the coast measure the likelihood coast. that the storm will move over the respective coastal loca- Fig. 11 shows the forecast track and associated probabili- tions. Therefore, an assessment of risk for a specific location ties 18 hours later. Note that the probabilities increased is straightforward. Assessment in a relative sense, i.e., loca- slightly over the middle and upper Texas coast, but changed tion A relative to locations B and C, must take into account little elsewhere. This feature of the probability approach to the time factor. depicting the uncertainty in the forecast track is well worth A summary of some general characteristics of the NWS noting. That is, an individual's perception of risk at Corpus hurricane track probabilities for a storm forecast to move Christi, Texas, would likely change drastically based upon nearly parallel to the coast includes the following: these two forecast tracks. However, the probabilities indicate 1) Highest probabilities along the coast do not always re- that changes of the forecast track of this magnitude at the flect the forecast point of landfall. The time factor must be considered. Relative assessments should include comparisons with over-water points, when given, and with neighboring communities, as well as with the max- imum values likely for each time period; 2) Probabilities will increase and shift toward shorter forecast periods (right to left in the probability tables) as the storm is forecast to move closer to the coast; 3) Probabilities will progressively increase ahead of the storm along the coast and decrease rapidly abreast of and behind the storm; and 4) Probabilities change most rapidly during the period the storm is closest to the site of interest. c. First-year results—perceptions

The National Weather Service formally initiated its hurri- cane probability program with the public issuance of proba- FIG. 11. Same as for Fig. 10 except for a base time of 1200 GMT 16 bilities for Tropical Storm Alicia on 15 August 1983. Fig. 10 August 1983.

Unauthenticated | Downloaded 10/05/21 06:54 PM UTC Bulletin American Meteorological Society 11 longer forecast periods are essentially in the noise level. This perception factor is the major reason that the NWS has resisted issuing exact forecast positions out through these longer forecast periods. There is a tendency to focus upon the forecast track as if it were exact. Furthermore, attention is focused upon the "point" being plotted as representative of where "the storm" is located. This may result in an overstimulation of response at the forecast "point" of landfall and an understimulation at surrounding coastal locations. This is a natural tendency for the media, particularly the voice media, since it is much easier to communicate point information as compared to area concepts. In fact, the storm is much more than a point and, as indicated earlier, storm conditions usually arrive on the coast well in advance of the storm center or "point" crossing the coast. To focus upon the point can be dangerous. For instance, for the first forecast track issued for Alicia, the residents of Galveston might reasonably assume that they were free and clear from possible effects from Alicia. However, they might have started to have second thoughts when the forecast shown in Fig. 11 was issued. Some media and media meteor- ologists apparently stressed in advance of Alicia moving ashore, that "it" (Alicia) would cross the coast near Free- port, Texas at a specific time. This prediction was consistent with track forecasts being issued by the National Hurricane Center, but the emphasis upon the point was inconsistent with advisories that were indicating effects over a broad area. Residents of this area were being urged to take precautionary FIG. 13. Forecast track and associated total probabilities through actions to protect their lives and property from the possible 72 hours for Tropical Storm Barry for a base time of 0000 GMT 24 onslaught of hurricane conditions. In actual fact, the major August 1983. effects of the storm were produced over a broad area, with most of the major damage occurring north of Freeport. However, confusion created by focusing upon the point may Comparisons with Figs. 10 and 11 show how the probable have caused some residents to delay actions and perhaps run zone of storm occurrence has been focused in on with time. unnecessary risks. A feature of the probabilities is that they The second storm for which probabilities were issued was help to spread out this perceived risk in a reasonable fashion. Tropical Storm Barry. Fig. 13 shows the initial forecast track Fig. 12 shows the forecast track and probabilities issued and associated probabilities. For this case, the probabilities for Alicia, approximately 24 hours before landfall. The high- are nearly the same from Miami, Florida, through Cape Hat- est values are shown over the middle and upper Texas coast. teras, . However, a local newspaper in the Cape Kennedy area compared the probability for their loca- tion to that for other locations and headlined a story that the Cape area was the most likely location to be struck by Tropi- cal Storm Barry. Although somewhat correct, this was not the interpretation one would hope for from this forecast. The storm was actually forecast to remain over the water for the next 72 hours. Fortuitously, Barry's slow northward move- ment was blocked by a large high-pressure system that moved off the southeast United States coast, causing the storm to turn toward the west, making the Cape area the most likely region to be affected (Fig. 14). Another point of interest from this figure is that probabilities are quite high and increasing along the west coast of Florida. Barry was a weak storm at this time, but if the storm had been stronger, conditions on the west coast of Florida would have been con- siderably different from those which would have occurred on the east coast. This, again, illustrates why the probabilities cannot be used without considering conditions this particu- lar storm is expected to create at the site of interest. These FIG. 12. Forecast track and associated total probabilities through 72 hours for Hurricane Alicia for a base time of 0000 GMT 17 Au- factors are contained in the NWS advisories and local gust 1983. statements.

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2) The NWS probabilities indicate the likelihood that the storm of interest will move over specific locations within the forecast periods indicated in the probability tables; 3) To determine the probability for a listed community through a given time period, simply add the numbers from left to right through the column that includes the time of interest; 4) If the community of interest is not listed, linearly inter- polate between values given for locations on either side of that community; 5) Users should watch the change of probabilities with time at their location as well as surrounding locations. This permits an assessment as to whether or not the threat is increasing or decreasing and/or shifting to- ward or away from their location as time passes; 6) Use of the probabilities should generally be based on values for the user site, independent of values at other Frc. 14. Same as Fig. 13 except for a base time of 1500 GMT 24 August 1983. locations. That is, when the value for the user location exceeds their predetermined threshold values for the Several important things were learned from the issuance of particular storm situation and action time window, the probabilities during the 1983 season. First, the probabilities user takes action, regardless of values at surrounding were widely accepted and used by decision-makers, although locations; refined usage will require more detailed studies and assess- 7) Because of the high "false alarm" rate for longer fore- ments by these users for their particular situations. Second, cast periods (see Fig. 4), the most costly actions should the probabilities depict the forecast track uncertainties in a be scheduled for as late as practical in the decision- realistic manner; however, educational programs and mate- making process; and rials continue to be needed to assure proper interpretation 8) If a relative assessment is desired, such as for media and usage of these products. Third, some refinements of the purposes, and since the probability tables are compli- system are needed, such as issuance of probabilities of over- cated and not designed for casual use, it is urged that: water points to avoid misinterpretations such as that for a) Only total values (sum through 72 hours) be Barry. Fourth, the issuance of probabilities to the public did displayed; not cause major problems and in fact, based upon a prelimi- b) Values be displayed or read for a broad section of nary report (Baker and Carter, 1984) in the wake of Hurri- the coastline to indicate ranges of values; and cane Alicia, may have actually enhanced some desired actions. c) Comments to the general public be expressed in gen- eral terms. d. Summary—interpretations and use Optimum use of the NWS hurricane track probabilities in a decision-making process requires detailed studies and prepa- 6. Summary and conclusions rations by the potential user. The types of factors to be consid- ered are the vulnerability of the user community for various Coastal population growths and resident and industrial vul- storm scenarios, costs of preventative actions, costs of poten- nerabilities to hurricanes have increased dramatically over tial losses if such actions are not taken, time required to com- portions of the U.S. Gulf of Mexico and Atlantic coasts dur- plete the action before the onset of hurricane or other condi- ing the past two decades. The National Weather Service hur- tions which prevent the actions from being taken, etc. After ricane watch and warning programs, which attempt to min- these cost versus loss analyses are completed, the user then imize over-warning, remain adequate for the majority of the establishes acceptable levels of risk (threshold values of prob- coastal residents, but do not supply adequate lead times for abilities) for each storm scenario and possible preventative many coastal communities. Furthermore, these watches and action. Time windows are established for each action with an warnings are qualitative in nature where a need has been attempt to delay the most costly actions as long as possible to expressed for quantitative assessments of risk. With these reduce the potential "false alarm" rates (see Fig. 4). After factors in mind, the National Weather Service initiated a these studies and resultant plans are completed, the user is program using probabilities to quantitatively assess the un- now ready for the interpretation of the probabilities for use certainties in the forecast tracks of hurricanes. Probability as a tool in the decision-making process. tables were attached to tropical storm and hurricane adviso- A summary of factors to be considered in the interpreta- ries issued during the 1983 season. Results indicated some re- tion and use of the NWS hurricane track probabilities finements were needed and continuing educational programs follows: desired. However, these same results indicate that the pro- 1) The NWS probabilities do not indicate conditions which gram was well-received and timely. may be generated by the storm. This information is Optimum use of this tool in a decision-making process re- contained in NWS advisories and local statements; quires individual users to develop plans based upon their

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own particular needs for various storm scenarios. Such so- Baker, E. J., 1984: Public response to hurricane probability fore- phisticated usage is anticipated for most major communities casts. NO A A Tech. Memo. NWS FCST29, National Weather Ser- and industrial users through integration of information pro- vice, Washington, D.C., 35 pp. vided by this tool into their disaster preparedness plans. It is , and T. M. Carter, 1984: The role of information in public re- sponse to hurricane threats. Proc. AMS Third Tech. Conf. on Me- hoped that casual use by the general public will result in rein- teorology of the Coastal Zone, Miami, Fla, 6 pp. forcement of actions recommended by local officials. This Crawford, K. C., 1979: Hurricane surge potentials over southeast desired response will continue to heavily depend upon the Louisiana as revealed by a storm-surge forecast model: a prelimi- manner in which this and other storm-related information is nary study. Bull. Amer. Meteor. Soc., 60, 422-429. presented by the media. Crutcher, H. L., C. J. Neumann, and J. M. Pelissier, 1981: Tropical With an increased awareness of the uncertainties in the cyclone forecast errors and the multimodal bivariate normal dis- state-of-the-art hurricane forecasts and the vulnerabilities tribution. Mon. Wea. Rev., 109, 978-987. of their local communities to these storms, these probabili- Cry, G. W., 1961: Climatology of 24-hour Atlantic tropical cyclone ties may aid in the perceived credibility of officials. For in- movement. U.S. Wea. Bur. Tech. Memo. NHRP 42, 92 pp. stance, these officials will occasionally need to order major Hope, J. R., and C. J. Neumann, 1968: Probability of tropical cy- clone induced winds at Cape Kennedy. WBTM SOS-1, Wea. Bur. actions in advance of a storm such as evacuations when after- ESSA, U.S. Dept. of Com., Washington, D.C., 67 pp. the-fact analyses indicate that such actions were not needed. , and , 1970: An operational technique for relating the If the probability were 20% when the action was taken, the movement of existing tropical cyclones to past tracks. Mon. Wea. official could state that there was a one in five chance that X Rev., 98, 925-933. number of lives could be lost and that the official was not will- , and , 1971: Digitized Atlantic tropical cyclone tracks. ing to take that chance with these lives. It is hoped that ac- NOAA Tech. Memo. NWS SR-55, 145 pp. tions caused by these "false alarms" would not cause resi- Jarrell, J. D., 1978: Tropical cyclone strike probability forecasting. dents of these communities to become complacent and NAVENVPREDRSCHFAC CR 78-01, NEPRF, Monterey, Calif., ignore future warnings. To obtain the desired results, resi- 47 pp. dents need to be informed of the vulnerabilities of their , 1981: Atlantic hurricane strike probability program communities. In addition, the public would need to under- (STRIKEPA). NAVENPREDRSCHFAC CR 81-04, NEPRF Mon- terey, Calif., 28 pp. stand the limitations on hurricane forecasts which result in Jarvinen, B. J., and C. J. Neumann, 1978: Atlantic tropical cyclone high "false alarm" rates for long lead times. tracks by 5-, 10-, 15-, and 30-day periods. NOAA Tech. Memo. NWS NHC-5, 57 pp. Acknowledgments. The NWS probability program has resulted Kimball, S. J., 1958: Estimating the probability of hurricane force from the efforts of many people. The director of the National Hurri- winds affecting an air base. AWS TR 105-146, AWS (MATS), cane Center, Dr. Neil Frank, NWS headquarters staff members such 20 pp. as Bob Sorey, Richard Coleman, Jim Campbell, Dr. Mike Carter, Lawrence, M. B., 1984: Storm surge model verification statistics. and Richard Wagoner were all instrumental in getting this program initiated and educational programs conducted. The technical devel- Proc. AMS 15th Tech. Conf. on Hur. and Trop. Met., Miami, Fla, opment also had many participants. Special appreciation is due to 2 pp. the Naval Environmental Research and Prediction Facility and Mr. Neumann, C. J., 1983: Current forecasting accuracies and require- Jerry Jarrell for kindly providing the basic program used in the ments for improvements. Federal Coordinator For Meteorological probability computations. Special thanks go to Mr. Charles Neu- Services and Support, Rep. FCM-R2-1982, Apendix C, pp. C1-C33. mann and Drs. Arthur Pike, Preston Leftwich, and Joseph Pelissier , and J. M. Pelissier, 1981a: An analysis of Atlantic tropical cy- for initial testing and evaluation of the probability programs and ad- clone forecast errors, 1970-1979. Mon. Wea. Rev., 109,1248-1266. aptation and alteration of the Navy program for NWS use. , and , 1981b: Models for the prediction of tropical cyclone motion: an operational evaluation. Mon. Wea. Rev., 109,522-538. References Sheets, R. C., 1984: The National Weather Service hurricane proba- bility program. NOAA NWS Tech. Rep. 37, 70 pp. Appleman, H. S., 1962: Estimating the probability of operationally Simpson, R. H., and M. B. Lawrence, 1971: Atlantic hurricane fre- critical wind speeds affecting an air base during passage of a tropi- quencies along U.S. coastlines. NOAA Tech. Memo. NWS SR-58, cal cyclone. AWS TR 164, AWS (MATS), U.S. Air Force, 22 pp. 14 pp. •

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