RELATIONSHIPS BETWEEN TROPICAL CYCLONE ATTRIBUTES AND PRECIPITATION TOTALS: CONSIDERATIONS OF SCALE

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 22: (2002) Published online in Wiley InterScience ( DOI: /joc.721 RELATIONSHIPS BETWEEN TROPICAL CYCLONE ATTRIBUTES AND PRECIPITATION TOTALS: CONSIDERATIONS OF SCALE CHARLES E. KONRAD II,* MELANIE F. MEAUX and DAVID A. MEAUX Department of Geography, Saunders Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC , USA Received 27 February 2001 Revised 10 August 2001 Accepted 10 August 2001 ABSTRACT In this study, a heavy rain climatology is constructed that relates precipitation totals estimated over a range of spatial scales (i.e. circular regions from 2500 to km 2 ) to tropical cyclone attributes. To carry this out, an automated algorithm is developed that identifies 2 day precipitation totals across the Cooperative Observer Network and estimates the greatest precipitation amounts over each spatial scale for all events from 1950 through to These maximum mean precipitation amounts are related to the tropical cyclone attributes of strength, size, and speed of movement. The resulting relationships are found to vary significantly according to the scale of precipitation. Precipitation amounts over the largest scales are most highly associated with the size of tropical cyclones, and precipitation amounts over the smallest scales are most highly connected with the speed of movement of the tropical cyclones. Copyright 2002 Royal Meteorological Society. KEY WORDS: precipitation; tropical cyclone; eastern United States 1. INTRODUCTION During recent years, tropical cyclones have been associated with numerous flooding events across the world. The heaviest point precipitation total in these events is typically offered as a convenient benchmark for assessing the extremeness of the precipitation (Konrad, 2001). Excessive point precipitation totals, by themselves, provide little information regarding the potential for stream basin flooding. It is the timing and spatial distribution of precipitation within a stream basin that are the key factors in determining whether flooding will be observed (Hirschboeck et al., 2000). For example, the floods with Hurricane Floyd in eastern North Carolina were tied to precipitation point totals of up to 480 mm (i.e. 19 in). The extremeness of the flooding, however, can be related to rivers at or above flood stage immediately prior to Floyd (i.e. from Tropical Storm Dennis 11 days earlier) and the large region in which extreme precipitation was observed during Floyd (e.g. Doppler radar estimated 48 h precipitation totals exceeding 400 mm over a km 2 region). Although flooding is common in areas affected by tropical cyclones, the spatial scales over which the heaviest precipitation typically occurs are not known. Although dynamic convergence and lifting of moist air contribute strongly to the production of the heavy precipitation in a tropical cyclone, it is not clear, from a climatological perspective, what aspects of a tropical cyclone exert the greatest control on precipitation totals over different spatial scales. Slow-moving or quasi-stationary cyclones have been associated with extraordinary point precipitation amounts (NCDC, 1994; Blackwell and Kimball, 2001); e.g. Pfost (2001) has derived an empirical relationship for predicting these amounts based on the speed of movement of the system. Although one would expect the size and the strength * Correspondence to: C. E. Konrad II, Department of Geography, Saunders Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC , USA; konrad@unc.edu Copyright 2002 Royal Meteorological Society

2 238 C. E. KONRAD, M. F. MEAUX AND D. A. MEAUX of tropical cyclones to be positively correlated with the precipitation amounts, the scales over which this precipitation might increase are not known; no published research has addressed these relationships. Other factors that are not directly related to a tropical cyclone can have a strong influence on precipitation amounts. For example, the movement of tropical cyclones or their remnants over orographic features can focus the lifting and, therefore, enhance point precipitation amounts (e.g. Schwarz, 1970). Other studies (e.g. Palmen, 1958; DiMego and Bosart, 1982) have demonstrated the importance of interacting downstream synoptic features (e.g. fronts and upper-level troughs) in contributing to extreme precipitation for a given event. Since the vast majority of the research on the connections between tropical cyclones and precipitation has focused on the case study examination of several events or less (e.g. Marks, 1985; Burpee and Black, 1989), little is known about the climatological relationships between the two. In this study, a large sample of tropical cyclones is investigated to provide climatological links between the heaviest precipitation totals over a range of spatial scales and the strength, size, and speed of movement of tropical cyclones. Also, the precipitation amounts associated with these events are ranked relative to the entire population of precipitation events across the study area in order to determine how frequently and over what spatial scales tropical cyclones produce the heaviest precipitation. 2. METHODOLOGY In the first phase of this work, maximum areal precipitation amounts were estimated for all precipitation events occurring over the eastern USA (Figure 1) between 1950 and Doppler radar precipitation estimates may be used to estimate areal precipitation means over a region, and the accuracy of these estimates is quite high when adjustment factors or gauge corrections are applied (Legates, 2000). Unfortunately, radar-based estimates are only available for recent years, a time period that is not long enough to construct a heavy precipitation climatology. In this study, daily precipitation amounts from the Cooperative Observer Network were used and accessed from the Cooperative Summary of the Day CD ROMs (NCDC, 1997). This network provides the best spatial coverage of gauges over the 47 year study period, although regional differences exist in the density of stations. Most of the 24 h precipitation totals were recorded around 7:00 or 19:00 LST, although primary reporting stations (e.g. NWS) provided measurements near midnight. Unfortunately, no information was available on the recording times for a given station; thus it was not possible to establish a standard 24 h time frame for estimating areal precipitation totals across a given network of stations. Since precipitation events Figure 1. The eastern USA as defined in this study. The dark lines indicate boundaries between each of the four subregions

3 TROPICAL CYCLONE PRECIPITATION 239 vary greatly in their diurnal timing and duration, there is no single time period that is best for capturing a precipitation event. Most extreme events are relatively short lived, with durations of the order of hours as opposed to days (e.g. Giordano and Fritsch, 1991). Since cooperative stations have varying 24 measurement periods, it is problematic to estimate event totals over an area. If a 2 day precipitation total is used, events whose duration is 24 h or less can be captured over a given region, but, in some cases, the precipitation from an adjacent event will add to the calculated event total and produce a positive bias. In this study, 2 day precipitation totals over the network were collected in order to ensure the capture of extreme events with durations of 24 h or less, a time period that is thought to be sufficient for capturing precipitation connected with tropical cyclones. It should be recognized that this approach yielded precipitation overestimates in an unknown number of cases, as short duration events occurring during the day prior to or following the tropical cyclone event are added to the precipitation total. Given the coarse temporal resolution of the gauging data, short duration events often cannot be identified using our approach. However, this is not a serious limitation in this work since tropical cyclone events typically have a longer duration. The 2 day precipitation totals from all stations in the cooperative network within the eastern two-thirds of the USA (Figure 1) were interpolated onto a grid containing a km 2 grid spacing. This effort was carried out daily, thus providing temporally overlapping, 2 day precipitation totals for each day of the 47 year period. Owing to the voluminous amount of daily data, the computationally efficient method of Thiessen polygons was used to interpolate the precipitation amounts. The Thiessen polygon method of extrapolation exhibits some shortcomings, including the high weighting of isolated stations; also, stations situated along the Atlantic and Gulf coastlines are weighted more strongly, as their values are extrapolated beyond the coastline. However, extrapolations were not carried out in pixels beyond a radius of 28 km from any station. Precipitation amounts were not defined in the pixels beyond the extrapolation zone. These pixels were restricted to small regions where the Cooperative Observer Network was relatively sparse (e.g. Figure 1). Additionally, precipitation pixels were not defined over large bodies of water (e.g. Great Lakes, Atlantic Ocean and Gulf of Mexico) beyond a 28 km extrapolation zone from the coastline. The next step in the analysis was to identify regions containing the greatest mean precipitation totals across each of the ten scales. An automated routine was applied to the daily gridded precipitation fields to identify mean precipitation maxima in space and time (e.g. over 2 day periods) for each day of the study period. Daily precipitation regions were defined by determining regional maxima in the mean 2 day precipitation totals over circular areas of ten sizes, or scales, ranging from 2500 to km 2 (Table I). A moving or floating window approach was applied such that the circular areas at each scale were moved across the study domain (i.e. each area centroid moved systematically, pixel by pixel). Precipitation regions were not considered in the sample at a given scale if more than 20% of the pixels were undefined with respect to precipitation amounts. This restriction was imposed in order to prevent the construction of precipitation regions that extended beyond the study region where precipitation data were unavailable. The restriction affected large-scale events (i.e. scale 4 or less) mostly, whose centroids occurred near the borders of the study area. Also, the restriction precluded Table I. Precipitation scales considered in this study Designation Circular area (km 2 ) Radius (km) 1 (large scale) (medium scale) (small scale)

4 240 C. E. KONRAD, M. F. MEAUX AND D. A. MEAUX the identification of large-scale precipitation regions over large peninsular regions of the study area, including Florida and New England. In order to develop an independent sample of precipitation regions at a given scale, the centroids of these regions on a given day had to be at least 1000 km apart; otherwise the region with the lesser mean precipitation amount was not placed in the sample. Given the computational time required for carrying out this procedure (i.e. calculations at pixels over days), the moving windows routine was centred only on the population of pixels registering a 2 day precipitation total of at least 2.54 cm. Precipitation events were formed by linking together daily precipitation regions across the ten spatial scales that occurred within 1000 km of each other and within a 2 day time period (e.g. Figure 2). If two precipitation regions at a given scale overlapped spatially within a 2 day time frame (i.e. centroids less than 1000 km apart), the region with the lesser mean totals was eliminated from consideration. Using this approach, most precipitation events made links to precipitation regions defined over all ten spatial scales of this study. Precipitation events were therefore associated with regions containing the greatest mean precipitation totals over each scale of the study. Events centred in peninsular areas, as defined by the study area (e.g. Florida, New England), did not make links to precipitation regions over the larger scales. The spatial pattern of precipitation in many events is complex, as the shapes of the areas of heaviest precipitation vary across scales (e.g. Figure 2). An examination of several extreme events revealed that precipitation regions were generally elliptical at the larger scales and nearly circular at the smallest scales. The circular pattern over the smallest scales is at least partially the result of the use of Thiessen polygons in the extrapolation of precipitation. The circular precipitation regions defined in this study do not capture elongated or elliptical precipitation regions effectively, and the precipitation amounts in these elongated regions are going to be underestimated. This is especially evident in the large-scale precipitation pattern associated with Figure 2. The 2 day precipitation totals associated with Hurricane Opal. The circles circumscribe regions in which the heaviest mean precipitation totals were identified over each of the ten scales

5 TROPICAL CYCLONE PRECIPITATION 241 Hurricane Opal (e.g. Figure 2), where the circular regions encompass some areas in which little precipitation is observed (e.g. southeastern Georgia). The circular precipitation regions defined in this study, therefore, provide a conservative measure of the mean maximum precipitation, at least over the larger spatial scales. Other shapes, including ellipses and rectangles, could always be generated for a given event to maximize the areal precipitation totals, but, without a common shape, it would be problematic to compare the precipitation totals of different events. In the second phase of this work, all landfalling tropical cyclones in the Atlantic and Gulf coastal regions were tied to precipitation events for the period from 1950 through to The size, strength, and speed of movement of each of these systems were estimated. The tropical cyclone size was defined by the area circumscribed by the outer most closed isobar. Tropical cyclone sizes were calculated manually using all six-hourly (e.g. 00, 06, 12, 18 UTC) NOAA Northern Hemispheric analyses. The distances from the center of the system to the outer most closed isobar were measured in four cardinal directions and converted to kilometres. These distances were then used to estimate the area encompassed by the outer most closed isobar. In some cases, tropical cyclone sizes varied dramatically between the 6 h map times in response to subtle changes in the peripheral pressure field. In order to stabilize the size estimates, averages were calculated over the eight map times (i.e. 48 h time window) surrounding landfall. In some instances in which the cyclone was especially small, no closed isobars were provided on a given analysis. Mean sizes for these cyclones were therefore calculated using only those map times where at least one isobar was analysed around the cyclone. The cyclone strength and speed of movement estimates were derived from measures obtained from the HURDAT data set compiled by the National Hurricane Center (NHC). The dataset provides six-hourly (i.e. 00, 06, 12, 18 UTC) positions (in tenths of a degree latitude and longitude) for all tropical cyclones (i.e. hurricanes, subtropical and tropical storms) in the Atlantic basin. For the first 6 years of the study period (e.g ), only the 00 and 12 UTC positions were recorded by forecasters; therefore, the HURDAT data set contains interpolations of the 06 and 18 UTC positions. Tropical cyclone positions were estimated by forecasters using a variety of data sources, including land and ship observations (1886 to present), radiosonde (1937 to present), aircraft reconnaissance (1944 to present), radar (1955 to present), satellite (1967 to present), and buoy data (1973 to present). The accuracy of the estimates increased through time as more data sources became available, especially after 1944 when aircraft reconnaissance began. The temporal and spatial resolution of the data increased (e.g. land and radar observations were available) as the cyclones approached the US coast, thus allowing for more precise estimates of the cyclone position. According to Jarvinen et al. (1984), the use of multiple data sources required some subjective interpretations in order to provide the best track estimates. For example, these interpretations took into account the small-scale oscillatory (trochoidal) motions or transient deviations that are inherent in a tropical cyclone track. Such oscillations were detected in the cyclone tracks and smoothed slightly (e.g. Figure 4 in Jarvinen et al. (1984)). Cyclone strength was assessed using the maximum sustained wind reported at the map time immediately prior to landfall. The minimum surface pressure may provide a better measure of the tropical cyclone strength, but these measurements were not consistently available in the HURDAT data set before The cyclone speed of movement was estimated by considering the distance travelled during the 6 h period in which the cyclone made landfall. 3. CLIMATOLOGICAL PERSPECTIVES The precipitation associated with the sample of tropical cyclones was ranked within the context of an extreme precipitation climatology of the study area (e.g. see Konrad (2001), for details). This climatology is defined by events that have a regional recurrence interval of 1 year or greater over each of the ten scales for four subregions (Figure 1) of the study area. This recurrence interval is defined by the heaviest 47 precipitation events over the region (i.e. 47 events over a 47 year period). Tropical systems (tropical depressions, tropical storms, and hurricanes) were responsible for a significant number of smaller-scale events in the northeastern and southeastern regions (Figure 3) with 46% of the scale 8, southeastern events connected with tropical systems. Only about one-quarter of the south-central events were associated with tropical systems or their

6 242 C. E. KONRAD, M. F. MEAUX AND D. A. MEAUX Figure 3. The percentage of extreme precipitation events over each of the indicated spatial scales that is associated with tropical systems for each subregion remnants. This small fraction does not necessarily imply that relatively fewer tropical events produce heavy rain there relative to the southeast; rather it indicates that other mechanisms were more likely to produce the heaviest of the precipitation events. Relatively few midwestern events were tied to tropical systems because few systems or their remnants penetrated into the region. Over most scales in the northeastern and southeastern regions, tropical systems were responsible for setting the benchmarks for the heaviest precipitation during the study period (Table II). Hurricane Opal was most extraordinary, as it produced extreme precipitation benchmarks over the entire study area over six of the ten spatial scales investigated in this work. Although tropical systems exert a significant influence on the extreme precipitation climatologies of the nearcoastal portions of the study area, 67% of the tropical cyclones were not associated with extreme precipitation (Figure 4). Landfalling tropical cyclones displayed the strongest connections with extreme precipitation over scales 6 through 9 (i.e to 5000 km 2 ). Interestingly, tropical cyclones were responsible for decreasing percentages of extreme precipitation events over the smallest scales (i.e. scale 10 to the point level, which is Table II. The heaviest precipitation events by scale for each region during the period that are tied to tropical cyclones. The 2 day precipitation totals are given for scales in which the event qualifies as the heaviest. Dashes indicate that the event has a ranking that exceeds 47 Region Name Precipitation amount over each scale (cm) Midwest 9/14/61 NW MO Carla 11.0 Northeast 6/23/72 SE PA Agnes /19/55 NE NC Ione 34.1 Southeast 10/5/95 AL W. GA Opal /6/94 S. GA Alberto /7/50 N. FL Easy 62.2 South-central 8/4/78 CN TX Amelia 66.1

7 TROPICAL CYCLONE PRECIPITATION 243 Figure 4. The percentage of landfalling tropical cyclones that is associated with extreme precipitation over each of the indicated spatial scales not shown in Figure 4). This suggests that tropical cyclones are not as strongly connected with local-scale flooding, but rather have their greatest potential influence on flooding over basin sizes ranging from 5000 to km RELATIONSHIPS BETWEEN PRECIPITATION AMOUNTS AND TROPICAL CYCLONE ATTRIBUTES Between 1950 and 1993, 101 tropical cyclones (e.g. tropical storms and hurricanes) made landfall over the eastern USA. Not all these tropical cyclones could be connected with precipitation events. In nine relatively dry events, other weather features adjacent to the tropical cyclone produced more precipitation, thus preventing the detection of the tropical cyclone precipitation event. Also, some cyclones were not tied to precipitation regions across the larger scales of the study (e.g. only 64 cyclones were connected with precipitation events at scale 3, or km 2 ). This typically occurred because the precipitation region connected with the cyclone was relatively small. Most of the precipitation events examined in this study were centred in the coastal states (Figure 5(a) and (b)). Precipitation regions at scale 3 were concentrated in two swaths: one running northeastward from the Piedmont regions of Georgia and the Carolinas to east-central Pennsylvania, and the other stretching from south-central Texas eastward through central Alabama. The vast majority of the precipitation regions defined over a scale of 2500 km 2 were centred close to the coast. This suggests that the heaviest precipitation over the smallest scales frequently occurs as the cyclone makes landfall. One notable exception is found in South Carolina, where three of the six precipitation regions are found more than 100 km northwest of the coast. Correlations were calculated between the maximum mean precipitation totals at each scale and the three tropical cyclone attributes (Table III). Before calculating correlation coefficients, all of the variables were logarithmically transformed to normalize their distributions. Over the largest scales, mean precipitation amounts were most highly related to the size of the tropical cyclone, with heavier precipitation amounts associated with larger cyclones. The mean precipitation amounts also showed a statistically significant correlation with the strength of the tropical cyclone. The strength of the correlations with size and strength declined over the smaller scales, of the study, especially the tropical cyclone strength. On the other hand, the speed of movement of the cyclones displayed increasing correlation coefficients over the smaller scales, as heavier

8 244 C. E. KONRAD, M. F. MEAUX AND D. A. MEAUX (a) (b) Figure 5. The centres of all precipitation events identified at a scale of (a) km 2, and (b) 2500 km 2 Table III. Correlations between 2 day precipitation totals and three tropical cyclone attributes. All variables have been log transformed. Statistically significant correlations are given in bold Precipitation scale (km 2 ) Strength (wind speed) Speed of movement Size (large scale) (small scale) precipitation amounts were tied to cyclones that moved more slowly. Obviously, the weaker relationships need to be interpreted with some caution. Because of the non-linear nature of the variables, it is useful to examine the relationships identified in greater detail. Table IV provides a cross-quartile tabulation between precipitation at scale 3 (i.e. large scale)

9 TROPICAL CYCLONE PRECIPITATION 245 Table IV. Cross-quartile tabulation of large-scale precipitation totals with tropical cyclone sizes Size Precipitation quartiles Quartiles Range (10 3 km 2 ) and tropical cyclone size.quartile comparisons revealthat 9 out of 16 of the largest (smallest) tropical cyclones in the study were connected with the heaviest (lightest) large-scale precipitation events. These comparisons indicate that the relationship is especially strong along the tails of the distribution, i.e. exceptionally large or small cyclones are especially likely to be connected with exceptionally large or small amounts of precipitation respectively. The strength of the relationship is revealed in Figure 6(a); e.g. 80% of the heaviest precipitation events are connected with tropical cyclones whose size is in the 61st percentile or greater. In contrast, less than 50% of the heaviest precipitation events are connected with tropical cyclones whose strength is in the 61st percentile or greater. Cross-tabulations between precipitation totals at the smallest scales (i.e km 2 ) and tropical cyclone speeds of movement also reveal a strong relationship along the tails of the distribution, as 12 out of 23 of the heaviest precipitation events were tied to cyclones whose speeds were exceptionally slow (see Table V). The relationship is slightly weaker on the low end of the precipitation distribution, where 10 out of 23 of the lightest precipitation events were associated with cyclones that moved exceptionally fast. As shown in Figure 6(b), 50% of the heaviest precipitation events are connected with slow tropical cyclone speeds (i.e. 83rd percentile with respect to slowness). Tropical cyclone size also displayed a significant relationship with precipitation at the smallest scales; e.g. 50% of the heaviest precipitation events were associated with tropical cyclone sizes that are in the 72nd percentile. A majority of the heaviest precipitation events over the largest scales (i.e. 11 out of 16 over a scale of km 2 ) occurred as a tropical cyclone encountered a front (Table VI). Also most of the lightest (a) (b) Figure 6. The percentage of the heaviest precipitation events that are associated with the given attribute magnitude percentiles: (a) km 2 ; (b) 2500 km 2. The solid, dashed, and dotted lines, depict tropical cyclone size, strength, and speed of movement respectively

10 246 C. E. KONRAD, M. F. MEAUX AND D. A. MEAUX Table V. Cross-quartile tabulation of small-scale precipitation totals with tropical cyclone speeds of movement Speed of movement Precipitation quartiles Quartiles Range (km h 1 ) Table VI. Cross-quartile tabulation of large-scale precipitation totals with tropical cyclone connections to a frontal system Precipitation quartiles Front No front precipitation events were not tied to tropical cyclone frontal interactions. These relationships do not suggest a strong connection between fronts and heavy precipitation, however, as only 11 out of 30 of the tropical cyclone frontal situations were tied to exceptionally heavy precipitation. Also, only 12 of the 30 frontal-free, tropical cyclone events were connected with exceptionally light precipitation. 5. DISCUSSION AND CONCLUSIONS Climatological connections were identified in this study between tropical cyclones and precipitation. Tropical cyclones were found to exert a significant influence on the extreme precipitation climatology of the southeastern and northeastern portions of the study area, especially over the smaller scales. In contrast to this, less than one out of three landfalling tropical cyclones was associated with extreme precipitation as defined in this study. Tropical cyclones were most likely to produce extreme precipitation over scales of 5000 to km 2 suggesting that the greatest flooding potential is in drainage basins over these scales. Precipitation amounts over the larger scales (e.g km 2 and greater) of this study were tied most strongly to the size of the tropical cyclone. Precipitation was also significantly related to the tropical cyclone strength. Precipitation amounts over the smaller scales were most strongly correlated with the tropical cyclone speed. The relationships identified were especially strong on the lower and upper ends of the precipitation distribution; in particular, a majority of the heaviest and lightest large-scale events (i.e. those in the fourth quartile) were associated with the largest and smallest cyclones respectively. Also, a majority of the heaviest precipitation events over the smaller scales were tied to the slowest moving tropical cyclones. These relationships suggest that there are thresholds across which modest increases in size or decreases in speed greatly increase the chances of extremely heavy precipitation over a large or small scale respectively. Tropical cyclone size was found to be a better discriminator of the heavy precipitation than the presence of fronts, although nearly 70% of the large-scale (e.g km 2 ) precipitation events were associated with tropical cyclone frontal interactions. This makes it more difficult to clarify climatologically the relative significance of the fronts versus tropical cyclone size in producing excessive large-scale precipitation. There are some interesting connections between large tropical cyclones and fronts that relate to the production of precipitation. Many of the stronger tropical cyclones in this study increased in size as they made

11 TROPICAL CYCLONE PRECIPITATION 247 landfall and moved northward into regions where fronts were commonly located. This usually occurred as the cyclone underwent an extratropical transition (e.g. Palmen, 1958). Such increases in size are common (e.g. Merrill, 1984) as the cyclone transitions into a cold core system and acquires a baroclinic character. Processes connected with the extratropical transition may therefore play some role in increasing the largescale precipitation amounts. Only two of nine large cyclones associated with heavy precipitation in this study were connected with fronts, however. In these cases, extratropical processes did not play a role, and thus the heavy precipitation is likely to be directly related to the size of the system. Another confounding variable in the identification of relationships betweenprecipitation and tropical cyclone attributes is the distance to which the cyclone travels inland. Because the network of rain gauges is situated inland, cyclones travelling farther inland are more likely to produce heavier inland precipitation, especially over the largest scales. The results of this study suggest that large tropical cyclones that move well inland are most likely to produce the greatest large-scale inland flooding. ACKNOWLEDGEMENTS Portions of this work were supported by NSF Grant BCS REFERENCES Blackwell KG, Kimball SK Two slow moving hurricanes produce vastly different rainfall patterns over the Alabama coastal area. In Symposium on Precipitation Extremes: Prediction, Impacts, and Responses, American Meteorological Society; Burpee RW, Black ML Temporal and spatial variations of rainfall near the centers of two tropical cyclones. Monthly Weather Review 117: DiMego GJ, Bosart LF The transformation of tropical storm Agnes into an extratropical cyclone. Part I: The observed fields and vertical motion computations. Monthly Weather Review 110: Giordano LA, Fritsch JM Strong tornadoes and flash-flood producing rainstorms during the warm season in the Mid-Atlantic Region. Weather Forecasting 6: Hirschboeck KK, Ely LL, Maddox RA Hydroclimatology of meteorologic floods. In Inland Flood Hazards, Wohl EE (ed.). Cambridge University Press; Jarvinen BR, Neumann CJ, Davis MA A tropical data tape for the North Atlantic basin, : content, limitations, and uses. NOAA Technical Memorandum, NWS NHC 22. Konrad CE The most extreme precipitation events over the eastern U.S. from : considerations of scale. Journal of Hydrometeorology 2: Legates DR Real-time calibration of radar precipitation estimates. Professional Geographer 52: Marks FD Evolution of the structure of precipitation in Hurricane Allen (1980). Monthly Weather Review 113: Merrill RT A comparison of large and small tropical cyclones. Monthly Weather Review 112: National Climate Data Center (NCDC) weather in the southeast: the February ice storm and the July flooding. US Department of Commerce, National Oceanic and Atmospheric Administration. National Climate Data Center (NCDC) Cooperative Summary of the Day CDs. TD 3200, US Department of Commerce, National Oceanic and Atmospheric Administration. Palmen E Vertical circulation and release of kinetic energy during the development of Hurricane Hazel into an extratropical cyclone. Tellus 10: Pfost RL Basic landfalling tropical cyclone quantitative precipitation forecasting. In Symposium on Precipitation Extremes: Prediction, Impacts, and Responses, American Meteorological Society; Schwarz FK The unprecedented rains associated with the remnants of Hurricane Camille. Monthly Weather Review 98: