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This paper analyses the 20–21 October 1996 rainstorm along the coastal zone of New England in terms of its synoptic setting and its temporal and spatial patterns, and assesses its recurrence interval. The event was primarily generated by an intense cutoff low aloft, with an associated frontal boundary at the surface, both of which slowly drifted across New England. Storm rainfall totals ranged as high as 488 mm, which fell just short of the all-time greatest storm event ever recorded in New England, but statewide 1-day precipitation records were set in Maine and New Hampshire. Recurrence interval analysis revealed that this event was in gross excess of a 100-yr rainfall event and may be greater than a 400-yr event in this region.
This paper analyses the 20–21 October 1996 rainstorm along the coastal zone of New England in terms of its synoptic setting and its temporal and spatial patterns, and assesses its recurrence interval. The event was primarily generated by an intense cutoff low aloft, with an associated frontal boundary at the surface, both of which slowly drifted across New England. Storm rainfall totals ranged as high as 488 mm, which fell just short of the all-time greatest storm event ever recorded in New England, but statewide 1-day precipitation records were set in Maine and New Hampshire. Recurrence interval analysis revealed that this event was in gross excess of a 100-yr rainfall event and may be greater than a 400-yr event in this region.
Abstract
It has been almost a decade since researchers assessed user preferences in gathering weather information. Maturing channels and increasingly mobile audiences necessitate the need for understanding what channels people use for weather information, what information people want, and how they react to specific content—especially potentially life-saving warnings. Furthermore, geographically compartmentalizing this information will allow communication strategies to be tailored to a more localized audience. As an initiative to this effort, a survey of digitally connected Louisianians found different channel preferences than were found in previous studies. Beyond this study, future research should seek to identify regional preferences since the last broad study on this topic nearly 10 years ago. In the survey, information preferences are collected with Twitter as the focal point, but other channels are included as choices to assess overall user preference. As older channels such as television decline in preference, mobile telephone applications are disrupting previous literature by quickly gaining popularity while studies on their utility remain in short supply. Results show that user channel preferences do not necessarily align with those that best serve weather communication efforts. Facebook, a channel notoriously problematic from a chronology standpoint, is favored by many respondents. On Twitter, there is a disconnect in the type of information respondents report wanting and what type of information generates a response. Interest in warning messages was not coincident with the threat posed by that specific type of weather. The format—wording and construction—of warning messages that generated the most response on Twitter does not align with extensive literature on proper risk communication.
Abstract
It has been almost a decade since researchers assessed user preferences in gathering weather information. Maturing channels and increasingly mobile audiences necessitate the need for understanding what channels people use for weather information, what information people want, and how they react to specific content—especially potentially life-saving warnings. Furthermore, geographically compartmentalizing this information will allow communication strategies to be tailored to a more localized audience. As an initiative to this effort, a survey of digitally connected Louisianians found different channel preferences than were found in previous studies. Beyond this study, future research should seek to identify regional preferences since the last broad study on this topic nearly 10 years ago. In the survey, information preferences are collected with Twitter as the focal point, but other channels are included as choices to assess overall user preference. As older channels such as television decline in preference, mobile telephone applications are disrupting previous literature by quickly gaining popularity while studies on their utility remain in short supply. Results show that user channel preferences do not necessarily align with those that best serve weather communication efforts. Facebook, a channel notoriously problematic from a chronology standpoint, is favored by many respondents. On Twitter, there is a disconnect in the type of information respondents report wanting and what type of information generates a response. Interest in warning messages was not coincident with the threat posed by that specific type of weather. The format—wording and construction—of warning messages that generated the most response on Twitter does not align with extensive literature on proper risk communication.
Abstract
This paper investigates relationships between storm surge heights and tropical cyclone wind speeds at 3-h increments preceding landfall. A unique dataset containing hourly tropical cyclone position and wind speed is used in conjunction with a comprehensive storm surge dataset that provides maximum water levels for 189 surge events along the U.S. Gulf Coast from 1880 to 2011. A landfall/surge classification was developed for analyzing the relationship between surge magnitudes and prelandfall winds. Ten of the landfall/surge event types provided useable data, producing 117 wind–surge events that were incorporated into this study. Statistical analysis indicates that storm surge heights correlate better with prelandfall tropical cyclone winds than with wind speeds at landfall. Wind speeds 18 h before landfall correlated best with surge heights. Raising wind speeds to exponential powers produced the best wind–surge fit. Higher wind–surge correlations were found when testing a more recent sample of data that contained 63 wind–surge events since 1960. The highest correlation for these data was found when wind speeds 18 h before landfall were raised to a power of 2.2, which provided R 2 values that approached 0.70. The R 2 values at landfall for these same data were only 0.44. Such results will be useful to storm surge modelers, coastal scientists, and emergency management personnel, especially when tropical cyclones rapidly strengthen or weaken while approaching the coast.
Abstract
This paper investigates relationships between storm surge heights and tropical cyclone wind speeds at 3-h increments preceding landfall. A unique dataset containing hourly tropical cyclone position and wind speed is used in conjunction with a comprehensive storm surge dataset that provides maximum water levels for 189 surge events along the U.S. Gulf Coast from 1880 to 2011. A landfall/surge classification was developed for analyzing the relationship between surge magnitudes and prelandfall winds. Ten of the landfall/surge event types provided useable data, producing 117 wind–surge events that were incorporated into this study. Statistical analysis indicates that storm surge heights correlate better with prelandfall tropical cyclone winds than with wind speeds at landfall. Wind speeds 18 h before landfall correlated best with surge heights. Raising wind speeds to exponential powers produced the best wind–surge fit. Higher wind–surge correlations were found when testing a more recent sample of data that contained 63 wind–surge events since 1960. The highest correlation for these data was found when wind speeds 18 h before landfall were raised to a power of 2.2, which provided R 2 values that approached 0.70. The R 2 values at landfall for these same data were only 0.44. Such results will be useful to storm surge modelers, coastal scientists, and emergency management personnel, especially when tropical cyclones rapidly strengthen or weaken while approaching the coast.
Abstract
In the past decade, several large tropical cyclones have generated catastrophic storm surges along the U.S. Gulf and Atlantic Coasts. These storms include Hurricanes Katrina, Ike, Isaac, and Sandy. This study uses empirical analysis of tropical cyclone data and maximum storm surge observations to investigate the role of tropical cyclone size in storm surge generation. Storm surge data are provided by the Storm Surge Database (SURGEDAT), a global storm surge database, while a unique tropical cyclone size dataset built from nine different data sources provides the size of the radius of maximum winds (Rmax) and the radii of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Statistical analysis reveals an inverse correlation between storm surge magnitudes and Rmax sizes, while positive correlations exist between storm surge heights and the radius of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Storm surge heights correlate best with the prelandfall radius of 93 km h−1 (50 kt) winds, with a Spearman correlation coefficient value of 0.82, significant at the 99.9% confidence level. Many historical examples support these statistical results. For example, the 1900 Galveston hurricane, the 1935 Labor Day hurricane, and Hurricane Camille all had small Rmax sizes but generated catastrophic surges. Hurricane Katrina provides an example of the importance of large wind fields, as hurricane-force winds extending 167 km [90 nautical miles (n mi)] from the center of circulation enabled this large storm to generate a higher storm surge level than Hurricane Camille along the same stretch of coast, even though Camille’s prelandfall winds were slightly stronger than Katrina’s. These results may be useful to the storm surge modeling community, as well as disaster science and emergency management professionals, who will benefit from better understanding the role of tropical cyclone size for storm surge generation.
Abstract
In the past decade, several large tropical cyclones have generated catastrophic storm surges along the U.S. Gulf and Atlantic Coasts. These storms include Hurricanes Katrina, Ike, Isaac, and Sandy. This study uses empirical analysis of tropical cyclone data and maximum storm surge observations to investigate the role of tropical cyclone size in storm surge generation. Storm surge data are provided by the Storm Surge Database (SURGEDAT), a global storm surge database, while a unique tropical cyclone size dataset built from nine different data sources provides the size of the radius of maximum winds (Rmax) and the radii of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Statistical analysis reveals an inverse correlation between storm surge magnitudes and Rmax sizes, while positive correlations exist between storm surge heights and the radius of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Storm surge heights correlate best with the prelandfall radius of 93 km h−1 (50 kt) winds, with a Spearman correlation coefficient value of 0.82, significant at the 99.9% confidence level. Many historical examples support these statistical results. For example, the 1900 Galveston hurricane, the 1935 Labor Day hurricane, and Hurricane Camille all had small Rmax sizes but generated catastrophic surges. Hurricane Katrina provides an example of the importance of large wind fields, as hurricane-force winds extending 167 km [90 nautical miles (n mi)] from the center of circulation enabled this large storm to generate a higher storm surge level than Hurricane Camille along the same stretch of coast, even though Camille’s prelandfall winds were slightly stronger than Katrina’s. These results may be useful to the storm surge modeling community, as well as disaster science and emergency management professionals, who will benefit from better understanding the role of tropical cyclone size for storm surge generation.
Abstract
This paper examines tropical cyclone (TC) rainfall in the eastern United States from the perspective of documenting accumulated annual water volumes and areas of the precipitation. Volume is a value that merges both rainfall depth and rainfall area into a single metric for each year that can be directly compared between individual years. Area represents the total land area affected by tropical rains. These TC rainfall metrics were then compared to the ENSO and the Atlantic multidecadal oscillation (AMO). Time series of annual TC water volumes show an annual average of 107 km3. The maximum volume was produced in 1985 with 405.8 km3, driven by Hurricanes Bob, Claudette, Danny, Elena, Gloria, Henri, Juan, and Kate as well as by Tropical Storms Henri and Isabel. The lowest TC volume occurred in 1978 with 8.9 km3. ENSO phases did not show any statistical correlation with TC frequency in the eastern United States. However, AMO showed a significant correlation with volume and the number of storms affecting the region. TC rainfall volume and area in the eastern United States showed a strong correlation. However, there are exceptions, whereby 1985 stands out as an exceptional volume year though the area affected is not as impressive. In contrast, 1979 is an example when TCs covered a large area with a corresponding small rainfall volume, in part because of the rapid forward movement of the storms, for example, Hurricanes David and Frederic. Since 1995, TCs have become more numerous, producing larger volumes and affecting larger areas.
Abstract
This paper examines tropical cyclone (TC) rainfall in the eastern United States from the perspective of documenting accumulated annual water volumes and areas of the precipitation. Volume is a value that merges both rainfall depth and rainfall area into a single metric for each year that can be directly compared between individual years. Area represents the total land area affected by tropical rains. These TC rainfall metrics were then compared to the ENSO and the Atlantic multidecadal oscillation (AMO). Time series of annual TC water volumes show an annual average of 107 km3. The maximum volume was produced in 1985 with 405.8 km3, driven by Hurricanes Bob, Claudette, Danny, Elena, Gloria, Henri, Juan, and Kate as well as by Tropical Storms Henri and Isabel. The lowest TC volume occurred in 1978 with 8.9 km3. ENSO phases did not show any statistical correlation with TC frequency in the eastern United States. However, AMO showed a significant correlation with volume and the number of storms affecting the region. TC rainfall volume and area in the eastern United States showed a strong correlation. However, there are exceptions, whereby 1985 stands out as an exceptional volume year though the area affected is not as impressive. In contrast, 1979 is an example when TCs covered a large area with a corresponding small rainfall volume, in part because of the rapid forward movement of the storms, for example, Hurricanes David and Frederic. Since 1995, TCs have become more numerous, producing larger volumes and affecting larger areas.
Abstract
Spatial and temporal trends in temperature and precipitation extremes were investigated for the period 1948–2012 across the southeastern United States using 27 previously defined indices. Results show that regionwide warming in extreme minimum temperatures and cooling in extreme maximum temperatures occurred. The disproportionate changes in extreme daytime and nighttime temperatures are narrowing diurnal temperature ranges for most locations. The intensity and magnitude of extreme precipitation events increased overall, except for more easterly locations, particularly in South Carolina. These indices further show that warming in minimum temperatures has been pronounced most in summer and least in winter. Fall has become significantly wetter, while spring and summer have become drier, on average. Principal component analysis (PCA) was used to characterize a “geography of extremes” based on temperature and precipitation extreme indices. The PCA based on temperature indices revealed two coherent western and eastern subregions that share common modes of variability in extremes. Precipitation indices resulted in a greater number of smaller, spatially coherent groups exhibiting similar modes of variability. This classification regime illustrates important variations in extremes that exist on subregional scales. These findings have relevance for established climate research institutes, local governments, resource managers, and community planners interested in the variability of extreme events throughout the region.
Abstract
Spatial and temporal trends in temperature and precipitation extremes were investigated for the period 1948–2012 across the southeastern United States using 27 previously defined indices. Results show that regionwide warming in extreme minimum temperatures and cooling in extreme maximum temperatures occurred. The disproportionate changes in extreme daytime and nighttime temperatures are narrowing diurnal temperature ranges for most locations. The intensity and magnitude of extreme precipitation events increased overall, except for more easterly locations, particularly in South Carolina. These indices further show that warming in minimum temperatures has been pronounced most in summer and least in winter. Fall has become significantly wetter, while spring and summer have become drier, on average. Principal component analysis (PCA) was used to characterize a “geography of extremes” based on temperature and precipitation extreme indices. The PCA based on temperature indices revealed two coherent western and eastern subregions that share common modes of variability in extremes. Precipitation indices resulted in a greater number of smaller, spatially coherent groups exhibiting similar modes of variability. This classification regime illustrates important variations in extremes that exist on subregional scales. These findings have relevance for established climate research institutes, local governments, resource managers, and community planners interested in the variability of extreme events throughout the region.
Abstract
This study examines the surface wind characteristics of Brazil on the basis of the location of the maximum high pressure center in the South Atlantic basin (SAB), known as the South Atlantic anticyclone (SAA), from three reanalysis datasets for the period of 1980–2014. Linear wind speed trends determined for Brazil are geographically related to surface and macroscale atmospheric conditions found in the SAB. The daily mean position of the SAA exhibited a latitudinal poleward shift for all seasons, and a longitudinal trend was dependent upon extratropical activity found in the SAB. Results also show that wind speed and sea level pressure for northern Brazil are dependent upon the latitudinal position of the SAA. Consequently, surface wind correlations for southern Brazil tend to be related to changes in the longitudinal position of the SAA, which result from transient anticyclones migrating over the SAB. An examination of positive and negative wind anomalies shows that shifts in the position of the SAA are coupled with changes in sea level pressure for northern Brazil and air temperature for southern Brazil. From these findings, a surface wind analysis was performed to demonstrate how the geographical location of the SAA affects wind speed anomalies across Brazil and the SAB. Results from this study can assist in understanding how atmospheric systems change within the SAB so that forthcoming socioeconomic and climate-related causes of wind for the country of Brazil can be known.
Abstract
This study examines the surface wind characteristics of Brazil on the basis of the location of the maximum high pressure center in the South Atlantic basin (SAB), known as the South Atlantic anticyclone (SAA), from three reanalysis datasets for the period of 1980–2014. Linear wind speed trends determined for Brazil are geographically related to surface and macroscale atmospheric conditions found in the SAB. The daily mean position of the SAA exhibited a latitudinal poleward shift for all seasons, and a longitudinal trend was dependent upon extratropical activity found in the SAB. Results also show that wind speed and sea level pressure for northern Brazil are dependent upon the latitudinal position of the SAA. Consequently, surface wind correlations for southern Brazil tend to be related to changes in the longitudinal position of the SAA, which result from transient anticyclones migrating over the SAB. An examination of positive and negative wind anomalies shows that shifts in the position of the SAA are coupled with changes in sea level pressure for northern Brazil and air temperature for southern Brazil. From these findings, a surface wind analysis was performed to demonstrate how the geographical location of the SAA affects wind speed anomalies across Brazil and the SAB. Results from this study can assist in understanding how atmospheric systems change within the SAB so that forthcoming socioeconomic and climate-related causes of wind for the country of Brazil can be known.
Abstract
Time series of extreme meteorological and hydrological events frequently present problems with the use of traditional parametric statistical techniques. These difficulties arise from the frequent use of count data, the presence of zero values, data with nonnormal distributions, and/or truncated data. This paper presents a parametric method to evaluate temporal trends in extreme events that overcomes these problems. The technique includes the testing of the arrival structure of extreme event data for the Poisson distribution, then prepares and tests time series of interarrival times for trend analysis through linear regression. Nor’easters along the east coast of the United States and heavy rainfall events at Covington, Louisiana, are examined.
Abstract
Time series of extreme meteorological and hydrological events frequently present problems with the use of traditional parametric statistical techniques. These difficulties arise from the frequent use of count data, the presence of zero values, data with nonnormal distributions, and/or truncated data. This paper presents a parametric method to evaluate temporal trends in extreme events that overcomes these problems. The technique includes the testing of the arrival structure of extreme event data for the Poisson distribution, then prepares and tests time series of interarrival times for trend analysis through linear regression. Nor’easters along the east coast of the United States and heavy rainfall events at Covington, Louisiana, are examined.
Abstract
Using routinely available hourly surface observations and United States surface analyses for 2001, a method was developed for predicting sea-breeze events. The method is adaptable to any coastal region in the world where surface data are available. Specific prediction guidelines have been developed using Portsmouth, New Hampshire, as the forecast site. Using Portsmouth METARs (translated roughly from the French as aviation routine weather report), 167 days were determined to have conditions favorable for the occurrence of a sea breeze. Each of these 167 days are classified as either sea-breeze, marginal, or non-sea-breeze events. Sea breezes were defined as insolation-driven local onshore winds. Marginal events were weak sea breezes. Non-sea-breeze events were those days on which sufficient insolation was present but failed to produce a sea breeze at Portsmouth. The surface analyses for these 167 days were used to define a set of synoptic classes based on the arrangement of large-scale pressure systems, and meaningful interpretations resulted. For example, sea breezes and marginals account for almost 80% of one class, whereas two other classes produced no sea-breeze events. Standard surface observations were used to calculate the “regional scale” cross-shore potential temperature gradient (δθ/δx) and the cross-shore geostrophic wind component (u G ) for the hour of onset (sea breeze and marginal events) or of peak heating (non-sea-breeze events). Stronger negative δθ/δx values were needed to develop a sea breeze in the presence of stronger positive u G values. The six well-defined synoptic classes were plotted as a function of δθ/δx and u G and occupy specific regions of the resulting diagram.
Abstract
Using routinely available hourly surface observations and United States surface analyses for 2001, a method was developed for predicting sea-breeze events. The method is adaptable to any coastal region in the world where surface data are available. Specific prediction guidelines have been developed using Portsmouth, New Hampshire, as the forecast site. Using Portsmouth METARs (translated roughly from the French as aviation routine weather report), 167 days were determined to have conditions favorable for the occurrence of a sea breeze. Each of these 167 days are classified as either sea-breeze, marginal, or non-sea-breeze events. Sea breezes were defined as insolation-driven local onshore winds. Marginal events were weak sea breezes. Non-sea-breeze events were those days on which sufficient insolation was present but failed to produce a sea breeze at Portsmouth. The surface analyses for these 167 days were used to define a set of synoptic classes based on the arrangement of large-scale pressure systems, and meaningful interpretations resulted. For example, sea breezes and marginals account for almost 80% of one class, whereas two other classes produced no sea-breeze events. Standard surface observations were used to calculate the “regional scale” cross-shore potential temperature gradient (δθ/δx) and the cross-shore geostrophic wind component (u G ) for the hour of onset (sea breeze and marginal events) or of peak heating (non-sea-breeze events). Stronger negative δθ/δx values were needed to develop a sea breeze in the presence of stronger positive u G values. The six well-defined synoptic classes were plotted as a function of δθ/δx and u G and occupy specific regions of the resulting diagram.
Abstract
This research introduces a climatology of hourly precipitation characteristics, investigates trends in precipitation hours (PH) and hourly accumulation, and uses four different time series to determine if precipitation intensity is changing across the southeastern United States from 1960 to 2017. Results indicate hourly intensity significantly increased at 44% (22/50) of the stations, accompanied by an increase in average hourly accumulation at 40% of the sites analyzed (20/50). The average duration of precipitation events decreased at 82% (41/50) of the stations. However, the frequency of 90th percentile hourly events and events above station-specific average hourly totals did not show a broad increase similar to hourly intensity. It seems hourly events are becoming heavier on average, while the duration of the average precipitation event is decreasing. Geographically, heavy hourly events are more frequent along the Gulf Coast and decrease inland. PH significantly decreased across South Carolina, Georgia, and northern Florida, mainly due to significant decreases in winter (DJF) and spring (MAM). Decreases in PH during spring were contained to Georgia and South Carolina and were accompanied by a decrease in accumulation. Decreases in PH during winter were more widespread and did not exhibit a broad decrease in accumulation, suggesting winter precipitation across that portion of the region is becoming more intense.
Abstract
This research introduces a climatology of hourly precipitation characteristics, investigates trends in precipitation hours (PH) and hourly accumulation, and uses four different time series to determine if precipitation intensity is changing across the southeastern United States from 1960 to 2017. Results indicate hourly intensity significantly increased at 44% (22/50) of the stations, accompanied by an increase in average hourly accumulation at 40% of the sites analyzed (20/50). The average duration of precipitation events decreased at 82% (41/50) of the stations. However, the frequency of 90th percentile hourly events and events above station-specific average hourly totals did not show a broad increase similar to hourly intensity. It seems hourly events are becoming heavier on average, while the duration of the average precipitation event is decreasing. Geographically, heavy hourly events are more frequent along the Gulf Coast and decrease inland. PH significantly decreased across South Carolina, Georgia, and northern Florida, mainly due to significant decreases in winter (DJF) and spring (MAM). Decreases in PH during spring were contained to Georgia and South Carolina and were accompanied by a decrease in accumulation. Decreases in PH during winter were more widespread and did not exhibit a broad decrease in accumulation, suggesting winter precipitation across that portion of the region is becoming more intense.