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Abstract
Partial-duration maximum precipitation series from Historical Climatology Network stations are used as a basis for assessing trends in extreme-precipitation recurrence-interval amounts. Two types of time series are analyzed: running series in which the generalized extreme-value (GEV) distribution is fit to separate overlapping 30-yr data series and lengthening series in which more recent years are iteratively added to a base series from the early part of the record. Resampling procedures are used to assess both trend and field significance. Across the United States, nearly two-thirds of the trends in the 2-, 5-, and 10-yr return-period rainfall amounts, as well as the GEV distribution location parameter, are positive. Significant positive trends in these values tend to cluster in the Northeast, western Great Lakes, and Pacific Northwest. Slopes are more pronounced in the 1960–2007 period when compared with the 1950–2007 interval. In the Northeast and western Great Lakes, the 2-yr return-period precipitation amount increases at a rate of approximately 2% per decade, whereas the change in the 100-yr storm amount is between 4% and 9% per decade. These changes result primarily from an increase in the location parameter of the fitted GEV distribution. Collectively, these increases result in a median 20% decrease in the expected recurrence interval, regardless of interval length. Thus, at stations across a large part of the eastern United States and Pacific Northwest, the 50-yr storm based on 1950–79 data can be expected to occur on average once every 40 yr, when data from the 1950–2007 period are considered.
Abstract
Partial-duration maximum precipitation series from Historical Climatology Network stations are used as a basis for assessing trends in extreme-precipitation recurrence-interval amounts. Two types of time series are analyzed: running series in which the generalized extreme-value (GEV) distribution is fit to separate overlapping 30-yr data series and lengthening series in which more recent years are iteratively added to a base series from the early part of the record. Resampling procedures are used to assess both trend and field significance. Across the United States, nearly two-thirds of the trends in the 2-, 5-, and 10-yr return-period rainfall amounts, as well as the GEV distribution location parameter, are positive. Significant positive trends in these values tend to cluster in the Northeast, western Great Lakes, and Pacific Northwest. Slopes are more pronounced in the 1960–2007 period when compared with the 1950–2007 interval. In the Northeast and western Great Lakes, the 2-yr return-period precipitation amount increases at a rate of approximately 2% per decade, whereas the change in the 100-yr storm amount is between 4% and 9% per decade. These changes result primarily from an increase in the location parameter of the fitted GEV distribution. Collectively, these increases result in a median 20% decrease in the expected recurrence interval, regardless of interval length. Thus, at stations across a large part of the eastern United States and Pacific Northwest, the 50-yr storm based on 1950–79 data can be expected to occur on average once every 40 yr, when data from the 1950–2007 period are considered.
Abstract
Climate regions within the northeastern United States are defined using a combination of multivariate statistical techniques. A set of over 100 climatic variables from 641 United States and Canadian Cooperative Observer Network stations form the basis for the classification. Using various numbers of retained principal components, a suite of hierarchical clustering solutions is produced using Ward's method. A single 54-cluster solution is selected based upon the similarity of cluster outcomes using sequentially larger principal component datasets. These clusters form a set of seeds that are used to derive a final nonhierarchical cluster solution.
A novel approach is used in the nonhierarchical cluster analysis to reduce bias introduced by both redundant and irrelevant data. A sequence of cluster solutions is developed in which an additional principal component is considered in each successive solution. Final cluster membership is assigned based on the maximum frequency of cluster membership within this array of solutions. Approximately one-fourth of the climatological stations change cluster membership as a result of this nonhierarchical clustering procedure. These changes result in substantial improvements to the spatial homogeneity of the clusters. Marginal improvements to within- and between-cluster standard deviation are also realized.
Once a final grouping of stations is established, discriminant functions are calculated to distinguish the climatic zones in terms of variables derived from latitude, longitude, and elevation. Cross validation shows that more than 60% of the stations are correctly classified based on the discriminant functions. Since the spatial resolution of the 641 climatological stations is relatively low, a 5-min grided elevation dataset was used in conjunction with the discriminant functions to produce the final climate delineations.
Abstract
Climate regions within the northeastern United States are defined using a combination of multivariate statistical techniques. A set of over 100 climatic variables from 641 United States and Canadian Cooperative Observer Network stations form the basis for the classification. Using various numbers of retained principal components, a suite of hierarchical clustering solutions is produced using Ward's method. A single 54-cluster solution is selected based upon the similarity of cluster outcomes using sequentially larger principal component datasets. These clusters form a set of seeds that are used to derive a final nonhierarchical cluster solution.
A novel approach is used in the nonhierarchical cluster analysis to reduce bias introduced by both redundant and irrelevant data. A sequence of cluster solutions is developed in which an additional principal component is considered in each successive solution. Final cluster membership is assigned based on the maximum frequency of cluster membership within this array of solutions. Approximately one-fourth of the climatological stations change cluster membership as a result of this nonhierarchical clustering procedure. These changes result in substantial improvements to the spatial homogeneity of the clusters. Marginal improvements to within- and between-cluster standard deviation are also realized.
Once a final grouping of stations is established, discriminant functions are calculated to distinguish the climatic zones in terms of variables derived from latitude, longitude, and elevation. Cross validation shows that more than 60% of the stations are correctly classified based on the discriminant functions. Since the spatial resolution of the 641 climatological stations is relatively low, a 5-min grided elevation dataset was used in conjunction with the discriminant functions to produce the final climate delineations.
Abstract
A method to infer the observation time of a station at annual resolution is developed and tested at stations in the United States. The procedure is based on a tendency for the percentiles of the monthly distribution of positive day-to-day maximum temperature changes (i.e., day n + 1 > day n) to exceed the corresponding absolute percentiles of the distribution of negative day-to-day changes at afternoon stations. Similarly absolute percentiles of negative day-to-day minimum temperature change tend to exceed the corresponding positive interdiurnal changes at morning observation sites. Equal percentiles are generally found at stations that use a midnight observation hour. Based on annual and seasonal summations of these monthly percentile differences, discriminant functions are developed that are capable of differentiating between afternoon, morning, and midnight observation schedules.
Across the majority of the United States observation time is correctly classified in over 90% of the station-years tested. Classification success is generally highest for morning and afternoon observations and somewhat lower for midnight observations. Although geographic biases in classification success are not apparent, the procedure’s ability to estimate observation time decreases considerably at stations where the average annual interdiurnal temperature range is less than 1.7°C. In the United States such stations are limited to coastal California, parts of Arizona, and extreme southern portions of Texas and Florida. Application of the procedure to a subset of U.S. climatic normals stations indicates the presence of errors in the corresponding observation time metadata file.
Abstract
A method to infer the observation time of a station at annual resolution is developed and tested at stations in the United States. The procedure is based on a tendency for the percentiles of the monthly distribution of positive day-to-day maximum temperature changes (i.e., day n + 1 > day n) to exceed the corresponding absolute percentiles of the distribution of negative day-to-day changes at afternoon stations. Similarly absolute percentiles of negative day-to-day minimum temperature change tend to exceed the corresponding positive interdiurnal changes at morning observation sites. Equal percentiles are generally found at stations that use a midnight observation hour. Based on annual and seasonal summations of these monthly percentile differences, discriminant functions are developed that are capable of differentiating between afternoon, morning, and midnight observation schedules.
Across the majority of the United States observation time is correctly classified in over 90% of the station-years tested. Classification success is generally highest for morning and afternoon observations and somewhat lower for midnight observations. Although geographic biases in classification success are not apparent, the procedure’s ability to estimate observation time decreases considerably at stations where the average annual interdiurnal temperature range is less than 1.7°C. In the United States such stations are limited to coastal California, parts of Arizona, and extreme southern portions of Texas and Florida. Application of the procedure to a subset of U.S. climatic normals stations indicates the presence of errors in the corresponding observation time metadata file.
Abstract
The quality of hourly wind speed and direction observations from 41 northeastern U.S. first-order weather stations is evaluated with regard to the recognition of individual observations that are either obviously in error or of suspect quality. An automated quality-control routine is developed to screen these individual hourly wind reports obtained from the National Climatic Data Center TD-3280 dataset. Using this routine, observations are assessed based on their internal consistency (i.e., 1-min average winds must not exceed the daily peak gust), temporal variability, and spatial consistency. The evaluation of over 12.5 million hourly observations indicated that the quality of these hourly data is quite good. Fewer than 0.1% of the values, occurring on 2.1% of the days, fail the quality-control checks.
Abstract
The quality of hourly wind speed and direction observations from 41 northeastern U.S. first-order weather stations is evaluated with regard to the recognition of individual observations that are either obviously in error or of suspect quality. An automated quality-control routine is developed to screen these individual hourly wind reports obtained from the National Climatic Data Center TD-3280 dataset. Using this routine, observations are assessed based on their internal consistency (i.e., 1-min average winds must not exceed the daily peak gust), temporal variability, and spatial consistency. The evaluation of over 12.5 million hourly observations indicated that the quality of these hourly data is quite good. Fewer than 0.1% of the values, occurring on 2.1% of the days, fail the quality-control checks.
A procedure to infer time of observation based on day-to-day temperature variations is refined and applied to the 1060-station daily Historical Climatology Network (HCN), creating a set of ersatz observation time metadata. Testing of the observation time inference procedure on the HCN data, as well as a set of U.S. normals stations at which no reported observation time changes occur from 1951 to 1991, indicates that, on average, the correct observation time category is identified in nearly 90% of the station years. Classification success decreases, however, at stations at which average annual interdiurnal temperature range falls below 1.9°C. At these stations, which represent only 4% of the HCN daily station years, the percentage of correctly classified years falls to 78%.
Application of the observation time inference procedure yields a set of annual observation times for stations in the HCN. Primarily, this surrogate dataset provides a means of identifying observation time during years when documented observation times are absent. Such metadata are currently unavailable for approximately one-quarter of the daily HCN station years, limiting their use for analyzing time-dependent climate variations. In addition, the inferred observation times can be used to assess the veracity of the reported observation time data. Although quantifying the accuracy of the HCN observation time metadata is difficult, on average 6% of the station years are misclassified at stations having the highest potential for correct classification. Therefore, overall, these metadata seem reasonably accurate. At individual stations, however, erroneous observation time metadata are identified by the procedure and confirmed using temperature data from adjacent stations.
A procedure to infer time of observation based on day-to-day temperature variations is refined and applied to the 1060-station daily Historical Climatology Network (HCN), creating a set of ersatz observation time metadata. Testing of the observation time inference procedure on the HCN data, as well as a set of U.S. normals stations at which no reported observation time changes occur from 1951 to 1991, indicates that, on average, the correct observation time category is identified in nearly 90% of the station years. Classification success decreases, however, at stations at which average annual interdiurnal temperature range falls below 1.9°C. At these stations, which represent only 4% of the HCN daily station years, the percentage of correctly classified years falls to 78%.
Application of the observation time inference procedure yields a set of annual observation times for stations in the HCN. Primarily, this surrogate dataset provides a means of identifying observation time during years when documented observation times are absent. Such metadata are currently unavailable for approximately one-quarter of the daily HCN station years, limiting their use for analyzing time-dependent climate variations. In addition, the inferred observation times can be used to assess the veracity of the reported observation time data. Although quantifying the accuracy of the HCN observation time metadata is difficult, on average 6% of the station years are misclassified at stations having the highest potential for correct classification. Therefore, overall, these metadata seem reasonably accurate. At individual stations, however, erroneous observation time metadata are identified by the procedure and confirmed using temperature data from adjacent stations.
Abstract
Changes in the annual number of daily maximum and minimum temperature threshold exceedences between 1951 and 1993 are assessed at a network of 22 primarily rural sites in the northeastern United States. After adjusting the annual time series for changes in observation time and eliminating stations at which nonhomogeneous time series were detected, annual time series of the number of days with maximum temperatures ≤35.0°, 32.2° and 29.4° (95°, 90°, and 85°F); maximum temperatures ≤0.0° −6.7°, and −12.2°C (32°, 20°, and 10°F); minimum temperatures ≥23.8°, 21.1°, and 18.3°C (75°, 70°, and 65°F); and minimum temperatures >−12.2°, −15.0°, and −17.8°C (10°, 5°, and 0°F) are statistically analysed.
Overall, a statistically significant trend toward fewer cold minimum temperature threshold execeedences is detected across the region over the period from 1959 to 1993. All stations show a decreasing trend in days with minimum temperatures ≥−15.0°C over this time period, with significant (α = 0.10) trends present at nearly 50% of the sites. Similarly, significant increases in the exceedence of warm minimum temperature thresholds are evident at more stations than can be expected by chance. A significant number of trends toward fewer warm maximum temperature threshold exceedences is also detected over the 1951-1993 time period. However, these trends are most likely due to changes in the attributes of the individual stations. Only three northern stations show significant increases in cold maximum temperature threshold exceedences.
Abstract
Changes in the annual number of daily maximum and minimum temperature threshold exceedences between 1951 and 1993 are assessed at a network of 22 primarily rural sites in the northeastern United States. After adjusting the annual time series for changes in observation time and eliminating stations at which nonhomogeneous time series were detected, annual time series of the number of days with maximum temperatures ≤35.0°, 32.2° and 29.4° (95°, 90°, and 85°F); maximum temperatures ≤0.0° −6.7°, and −12.2°C (32°, 20°, and 10°F); minimum temperatures ≥23.8°, 21.1°, and 18.3°C (75°, 70°, and 65°F); and minimum temperatures >−12.2°, −15.0°, and −17.8°C (10°, 5°, and 0°F) are statistically analysed.
Overall, a statistically significant trend toward fewer cold minimum temperature threshold execeedences is detected across the region over the period from 1959 to 1993. All stations show a decreasing trend in days with minimum temperatures ≥−15.0°C over this time period, with significant (α = 0.10) trends present at nearly 50% of the sites. Similarly, significant increases in the exceedence of warm minimum temperature thresholds are evident at more stations than can be expected by chance. A significant number of trends toward fewer warm maximum temperature threshold exceedences is also detected over the 1951-1993 time period. However, these trends are most likely due to changes in the attributes of the individual stations. Only three northern stations show significant increases in cold maximum temperature threshold exceedences.
From 4 through 10 January 1998 a severe ice storm impacted northern New York and New England. Liquid-equivalent precipitation totals, which fell exclusively as freezing rain, reached as high as 11 cm at some observing sites in northwestern New York. At the limited number of stations in the region that report hourly meteorological observations, the magnitude of the storm was unprecedented since the beginning of digital records in 1948. The duration of the storm exceeded 115 h at Massena, New York, nearly twice the duration of the second-longest event. In terms of the liquid-equivalent precipitation amount that fell as freezing rain, nearly 9 cm was reported at Massena, New York, and 5.7 cm was observed at Burlington, Vermont, the highest amounts on record. Despite these point estimates of storm severity, it is argued that icing events in 1973, 1969, 1956, and 1921 were of comparable magnitude in New York and New England. However, with the exception of the 1921 storm, it does not appear that the spatial extent of these storms was as broad as that of the 1998 ice storm.
The economic impacts associated with the storm were most severe in New York and Maine. Across the region, the greatest icing impacts affected electric and communications utilities, forestry interests, the dairy and maple syrup industries, and property owners. Overall, a conservative estimate of ice storm related damages exceeds $1 billion.
From 4 through 10 January 1998 a severe ice storm impacted northern New York and New England. Liquid-equivalent precipitation totals, which fell exclusively as freezing rain, reached as high as 11 cm at some observing sites in northwestern New York. At the limited number of stations in the region that report hourly meteorological observations, the magnitude of the storm was unprecedented since the beginning of digital records in 1948. The duration of the storm exceeded 115 h at Massena, New York, nearly twice the duration of the second-longest event. In terms of the liquid-equivalent precipitation amount that fell as freezing rain, nearly 9 cm was reported at Massena, New York, and 5.7 cm was observed at Burlington, Vermont, the highest amounts on record. Despite these point estimates of storm severity, it is argued that icing events in 1973, 1969, 1956, and 1921 were of comparable magnitude in New York and New England. However, with the exception of the 1921 storm, it does not appear that the spatial extent of these storms was as broad as that of the 1998 ice storm.
The economic impacts associated with the storm were most severe in New York and Maine. Across the region, the greatest icing impacts affected electric and communications utilities, forestry interests, the dairy and maple syrup industries, and property owners. Overall, a conservative estimate of ice storm related damages exceeds $1 billion.
Abstract
Simulated annual temperature series are used to compare seven homogenization procedures. The two that employ likelihood ratio tests routinely outperform other methods in their ability to identify modest (0.33°C; 0.6 standard deviation anomaly) shifts in the mean. The percentage of imposed shifts that are detected by these methods is similar to that based on tests that rely on a priori metadata information concerning the position of potential shifts. These methods, along with a two-phase regression approach, are also best at identifying and placing multiple shifts within a single time series. Although the regression procedure is better able to detect multiple breaks that are separated by relatively short time intervals, in its published form it suffers from a higher-than-expected Type I error rate. This was also found to be a problem with a metadata-based procedure currently in operational use. The likelihood tests are strongly influenced by the presence of trends in the difference series and short (<20 yr) series length.
The ability of a given procedure to detect a discontinuity is predominately influenced by the magnitude of the discontinuity relative to the standard deviation of the data series being evaluated. Data series length, correlation between the test series and its associated reference series, and test series autocorrelation also influence test performance. These features were not considered in previous homogenization method comparisons.
Discontinuities with magnitudes less than 0.6 times the standard deviation of the time series represent the lower limit for homogenization. Based on the most effective homogenization techniques, 10% of the 1.25 standard deviation discontinuities are likely to remain in climatic data series, unless reference station correlations are exceptional or quality station metadata are available.
Abstract
Simulated annual temperature series are used to compare seven homogenization procedures. The two that employ likelihood ratio tests routinely outperform other methods in their ability to identify modest (0.33°C; 0.6 standard deviation anomaly) shifts in the mean. The percentage of imposed shifts that are detected by these methods is similar to that based on tests that rely on a priori metadata information concerning the position of potential shifts. These methods, along with a two-phase regression approach, are also best at identifying and placing multiple shifts within a single time series. Although the regression procedure is better able to detect multiple breaks that are separated by relatively short time intervals, in its published form it suffers from a higher-than-expected Type I error rate. This was also found to be a problem with a metadata-based procedure currently in operational use. The likelihood tests are strongly influenced by the presence of trends in the difference series and short (<20 yr) series length.
The ability of a given procedure to detect a discontinuity is predominately influenced by the magnitude of the discontinuity relative to the standard deviation of the data series being evaluated. Data series length, correlation between the test series and its associated reference series, and test series autocorrelation also influence test performance. These features were not considered in previous homogenization method comparisons.
Discontinuities with magnitudes less than 0.6 times the standard deviation of the time series represent the lower limit for homogenization. Based on the most effective homogenization techniques, 10% of the 1.25 standard deviation discontinuities are likely to remain in climatic data series, unless reference station correlations are exceptional or quality station metadata are available.
Abstract
Observed and projected increases in the frequency of extreme rainfall complicate the extreme value analyses of precipitation that are used to guide engineering design specifications, because conventional methods assume stationarity. Uncertainty in the magnitude of the trend in future years precludes directly accounting for the trend in these analyses. While previous extreme value analyses have sought to use as long a record as possible, it is shown using stochastically generated time series that this practice exacerbates the potential error introduced by long-term trends. For extreme precipitation series characterized by a trend in the location parameter exceeding approximately 0.005% yr−1, limiting the record length to fewer than 70 years is recommended. The use of longer time periods results in partial-duration series that are significantly different from their stationary counterparts and a greater percentage of rainfall extremes that exceed the 90% confidence interval corresponding to a stationary distribution. The effect is most pronounced on the shortest (i.e., 2 yr) recurrence intervals and generally becomes undetectable for recurrence intervals of more than 25 years. The analyses also indicate that the practice of including stations with records of limited length that end several decades prior to the present should be avoided. Distributions having a stationary location parameter but trended scale parameter do not exhibit this behavior.
Abstract
Observed and projected increases in the frequency of extreme rainfall complicate the extreme value analyses of precipitation that are used to guide engineering design specifications, because conventional methods assume stationarity. Uncertainty in the magnitude of the trend in future years precludes directly accounting for the trend in these analyses. While previous extreme value analyses have sought to use as long a record as possible, it is shown using stochastically generated time series that this practice exacerbates the potential error introduced by long-term trends. For extreme precipitation series characterized by a trend in the location parameter exceeding approximately 0.005% yr−1, limiting the record length to fewer than 70 years is recommended. The use of longer time periods results in partial-duration series that are significantly different from their stationary counterparts and a greater percentage of rainfall extremes that exceed the 90% confidence interval corresponding to a stationary distribution. The effect is most pronounced on the shortest (i.e., 2 yr) recurrence intervals and generally becomes undetectable for recurrence intervals of more than 25 years. The analyses also indicate that the practice of including stations with records of limited length that end several decades prior to the present should be avoided. Distributions having a stationary location parameter but trended scale parameter do not exhibit this behavior.
