Search Results
You are looking at 1 - 10 of 13 items for
- Author or Editor: Peter Robinson x
- Refine by Access: All Content x
The National Environmental Data Referral Service consists of a series of records of atmospheric datasets. The nature, characteristics, and name of the holder of the data is indicated, but no actual data are included. Records are maintained in an easily accessible computer-searchable form, so that a rapid assessment of data availability for a particular problem is possible. A description of the type of information available and suggestions for developing an efficient search strategy are presented.
The National Environmental Data Referral Service consists of a series of records of atmospheric datasets. The nature, characteristics, and name of the holder of the data is indicated, but no actual data are included. Records are maintained in an easily accessible computer-searchable form, so that a rapid assessment of data availability for a particular problem is possible. A description of the type of information available and suggestions for developing an efficient search strategy are presented.
Abstract
The impact of various types of weather on aircraft operations for one airline for 3 yr at Atlanta Hartsfield International Airport is investigated. Impacts are expressed as delays defined in terms of the difference between the actual flight time and that projected by the air traffic control system assuming an accurate weather forecast. The impacts of weather events were measured as the difference between these delays in clear conditions and in various types of inclement weather. Fog and thunderstorms create delays in various phases of each flight. The resultant delays at Atlanta alone create costs amounting to over $6 million annually for the airline. More accurate forecasts have the potential to reduce these costs by allowing more accurate flight planning. Decreases in the number and length of delay over time suggest that improvements in forecasts have already had an economic benefit to the airline. Delays associated with three snowstorms were also investigated. Early morning storms, even when forecast relatively late, have a rather small impact since few operations are airborne. Late-day storms, even if forecast early, have a much greater impact, since operations are well under way. Forecasts, in the case of snow events, are most valuable in assisting the airlines in canceling flights and rescheduling them once the storm is past.
Abstract
The impact of various types of weather on aircraft operations for one airline for 3 yr at Atlanta Hartsfield International Airport is investigated. Impacts are expressed as delays defined in terms of the difference between the actual flight time and that projected by the air traffic control system assuming an accurate weather forecast. The impacts of weather events were measured as the difference between these delays in clear conditions and in various types of inclement weather. Fog and thunderstorms create delays in various phases of each flight. The resultant delays at Atlanta alone create costs amounting to over $6 million annually for the airline. More accurate forecasts have the potential to reduce these costs by allowing more accurate flight planning. Decreases in the number and length of delay over time suggest that improvements in forecasts have already had an economic benefit to the airline. Delays associated with three snowstorms were also investigated. Early morning storms, even when forecast relatively late, have a rather small impact since few operations are airborne. Late-day storms, even if forecast early, have a much greater impact, since operations are well under way. Forecasts, in the case of snow events, are most valuable in assisting the airlines in canceling flights and rescheduling them once the storm is past.
Abstract
Models relating system-wide average temperature to total system load were developed for the Virginia Power and Duke Power service areas in the southeastern United States. Daily data for the 1985–91 period were used. The influence of temperature on load was at a minimum around 18°C and increased more rapidly with increasing temperatures than with decreasing ones. The response was sensitive to the day of the week, and models using separate weekdays as well as one using pooled data were created. None adequately accounted for civic holidays or for extreme temperatures. Estimates of average loads over a 3-month period, however, were accurate to within ±3%. The models were used to transform the probability distribution of 3-month average temperatures for each system, derived from the historical record, into load probabilities. These were used with the categorical temperature probabilities given by the National Weather Service long-lead forecasts to estimate the forecast load probabilities. In summer and winter the resultant change in distribution is sufficient to have an impact on the advance fuel purchase decisions of the utilities. Results in spring and fall are more ambiguous.
Abstract
Models relating system-wide average temperature to total system load were developed for the Virginia Power and Duke Power service areas in the southeastern United States. Daily data for the 1985–91 period were used. The influence of temperature on load was at a minimum around 18°C and increased more rapidly with increasing temperatures than with decreasing ones. The response was sensitive to the day of the week, and models using separate weekdays as well as one using pooled data were created. None adequately accounted for civic holidays or for extreme temperatures. Estimates of average loads over a 3-month period, however, were accurate to within ±3%. The models were used to transform the probability distribution of 3-month average temperatures for each system, derived from the historical record, into load probabilities. These were used with the categorical temperature probabilities given by the National Weather Service long-lead forecasts to estimate the forecast load probabilities. In summer and winter the resultant change in distribution is sufficient to have an impact on the advance fuel purchase decisions of the utilities. Results in spring and fall are more ambiguous.
Abstract
Heat waves are a major cause of weather-related deaths. With the current concern for global warming it is reasonable to suppose that they may increase in frequency, severity, duration, or areal extent in the future. However, in the absence of an adequate definition of a heat wave, it is impossible to assess either changes in the past or possible consequences for the future. A set of definitions is proposed here, based on the criteria for heat stress forecasts developed by the National Weather Service (NWS). Watches or warnings are issued when thresholds of daytime high and nighttime low heat index (H i ) values are exceeded for at least two consecutive days. The heat index is a combination of ambient temperature and humidity that approximates the environmental aspect of the thermal regime of a human body, with the NWS thresholds representing a generalized estimate of the onset of physiological stress. These thresholds cannot be applied directly nationwide. In hot and humid regions, physical, social, and cultural adaptations will require that the thresholds be set higher to ensure that only those events perceived as stressful are identified. In other, cooler, areas the NWS criteria may never be reached even though unusually hot events may be perceived as heat waves. Thus, it is likely that a similar number of perceived heat events will occur in all regions, with the thresholds varying regionally. Hourly H i for 178 stations in the coterminous United States was analyzed for the 1951–90 period to determine appropriate threshold criteria. Use of the NWS criteria alone indicated that much of the nation had less than three heat waves per decade, and this value was adopted as the baseline against which to establish suitable thresholds. For all areas, a percentile threshold approach was tested. Using all available data, daytime high and nighttime low thresholds were established separately for each specific percentile. Heat waves were treated as occurring when conditions exceeded both the daytime high and the nighttime low thresholds of the same percentile for two consecutive days. Several thresholds were tested. For much of the South, 1% thresholds produced appropriate values. Consequently, a heat wave was defined as a period of at least 48 h during which neither the overnight low nor the daytime high H i falls below the NWS heat stress thresholds (80° and 105°F, respectively), except at stations for which more than 1% of both the annual high and low H i observations exceed these thresholds, in which case the 1% values are used as the heat wave thresholds. As an extension, “hot spells” were similarly defined, but for events falling between the 1% values and NWS thresholds, with “warm spells” occurring between the 2% and 1% values. Again, stations for which the 1% or 2% H i values exceed the NWS thresholds were given modified definitions. The preliminary investigation of the timing and location of heat waves resulting from these definitions indicated that they correctly identified major epidemiological events. A tentative climatic comparison also suggests that heat waves are becoming less frequent in the southern and more frequent in the midwestern and eastern parts of the nation.
Abstract
Heat waves are a major cause of weather-related deaths. With the current concern for global warming it is reasonable to suppose that they may increase in frequency, severity, duration, or areal extent in the future. However, in the absence of an adequate definition of a heat wave, it is impossible to assess either changes in the past or possible consequences for the future. A set of definitions is proposed here, based on the criteria for heat stress forecasts developed by the National Weather Service (NWS). Watches or warnings are issued when thresholds of daytime high and nighttime low heat index (H i ) values are exceeded for at least two consecutive days. The heat index is a combination of ambient temperature and humidity that approximates the environmental aspect of the thermal regime of a human body, with the NWS thresholds representing a generalized estimate of the onset of physiological stress. These thresholds cannot be applied directly nationwide. In hot and humid regions, physical, social, and cultural adaptations will require that the thresholds be set higher to ensure that only those events perceived as stressful are identified. In other, cooler, areas the NWS criteria may never be reached even though unusually hot events may be perceived as heat waves. Thus, it is likely that a similar number of perceived heat events will occur in all regions, with the thresholds varying regionally. Hourly H i for 178 stations in the coterminous United States was analyzed for the 1951–90 period to determine appropriate threshold criteria. Use of the NWS criteria alone indicated that much of the nation had less than three heat waves per decade, and this value was adopted as the baseline against which to establish suitable thresholds. For all areas, a percentile threshold approach was tested. Using all available data, daytime high and nighttime low thresholds were established separately for each specific percentile. Heat waves were treated as occurring when conditions exceeded both the daytime high and the nighttime low thresholds of the same percentile for two consecutive days. Several thresholds were tested. For much of the South, 1% thresholds produced appropriate values. Consequently, a heat wave was defined as a period of at least 48 h during which neither the overnight low nor the daytime high H i falls below the NWS heat stress thresholds (80° and 105°F, respectively), except at stations for which more than 1% of both the annual high and low H i observations exceed these thresholds, in which case the 1% values are used as the heat wave thresholds. As an extension, “hot spells” were similarly defined, but for events falling between the 1% values and NWS thresholds, with “warm spells” occurring between the 2% and 1% values. Again, stations for which the 1% or 2% H i values exceed the NWS thresholds were given modified definitions. The preliminary investigation of the timing and location of heat waves resulting from these definitions indicated that they correctly identified major epidemiological events. A tentative climatic comparison also suggests that heat waves are becoming less frequent in the southern and more frequent in the midwestern and eastern parts of the nation.
Abstract
A solar energy climatology for North Carolina was developed using radiation data from the six SOLMET stations in the state. The climatology provides information needed to design solar powered space and water heating systems, and evaluate their performance. It specifies the distribution of monthly average daily total radiation on a horizontal surface, relationships between radiation and temperature for an average heating season, and the variability of radiation within a season. The main features of the solar energy climatology can be explained by the synoptic climatology of the area. The absolute values of the SOLMET radiation data are questionable, although they are acceptable for system design purposes. Results are presented graphically, allowing the climatology to be easily related to system performance. Examples of the relationships are given for a typical active space and water heating system, using the F-Chart method to calculate performance.
Abstract
A solar energy climatology for North Carolina was developed using radiation data from the six SOLMET stations in the state. The climatology provides information needed to design solar powered space and water heating systems, and evaluate their performance. It specifies the distribution of monthly average daily total radiation on a horizontal surface, relationships between radiation and temperature for an average heating season, and the variability of radiation within a season. The main features of the solar energy climatology can be explained by the synoptic climatology of the area. The absolute values of the SOLMET radiation data are questionable, although they are acceptable for system design purposes. Results are presented graphically, allowing the climatology to be easily related to system performance. Examples of the relationships are given for a typical active space and water heating system, using the F-Chart method to calculate performance.
Abstract
A method for creating scenarios of time series of monthly mean surface temperature at a specific site is developed. It is postulated that surface temperature can be specified as a linear combination of regional and local temperature components, where the regional component is an areal-average free tropospheric temperature such as may be produced by general circulation model climate simulations, and the local component is a response to purely local surface effects. Three models were tested for the North Carolina area, using weighted least squares linear regression techniques. The first related free tropospheric temperature directly to the site-specific local temperature, while the second added specifically local parameters as independent variables. For the four stations tested, this second model increased the explained variance from about 60% to near 70%. The third model incorporated a local energy and moisture balance process model to specify interactions between local variables, but yielded no significant increase in explanation. Scenarios of time series of future temperatures at a specific site can be obtained by using the statistical regression models and incorporating a random error term based on the regression residuals. For any postulated areas mean tropospheric temperature change this procedure yields a time series that retains the variability characteristics appropriate to the site-specific series.
Abstract
A method for creating scenarios of time series of monthly mean surface temperature at a specific site is developed. It is postulated that surface temperature can be specified as a linear combination of regional and local temperature components, where the regional component is an areal-average free tropospheric temperature such as may be produced by general circulation model climate simulations, and the local component is a response to purely local surface effects. Three models were tested for the North Carolina area, using weighted least squares linear regression techniques. The first related free tropospheric temperature directly to the site-specific local temperature, while the second added specifically local parameters as independent variables. For the four stations tested, this second model increased the explained variance from about 60% to near 70%. The third model incorporated a local energy and moisture balance process model to specify interactions between local variables, but yielded no significant increase in explanation. Scenarios of time series of future temperatures at a specific site can be obtained by using the statistical regression models and incorporating a random error term based on the regression residuals. For any postulated areas mean tropospheric temperature change this procedure yields a time series that retains the variability characteristics appropriate to the site-specific series.
Abstract
During the Taiwan Area Mesoscale Experiment (TAMEX), three Doppler radars complemented enhanced surface and upper-air observations. The focus of the experiment was to better understand the interaction of the terrain with precipitation systems in the production of the important heavy rainfall. The intensive operational period (IOP) number 8 extended from 1400 IST (local standard time) 7 June 1987 until 0800 LST 9 June 1987. During this time, a mesoscale convective system (MCS) formed in the Straits of Taiwan and moved inland. It was interrogated by many observing instruments, including three Doppler radars, over a 6-h period. During this time the front moved through the radar network. The front was shallow and the precipitation widespread, both ahead of and behind the front. The front was only 1.6-km deep over a distance of 100 km.
Using velocity-azimuth display (VAD) data, a portion of the frontogenetic function was computed during the times the front was in the vicinity of the radar. The increase in both convergence and deformation contributed to large values of the frontogenetic function.
Dynamic retrieval was also attempted on the data during the time when the front was most favorably located for analysis. The results are very similar to what has been observed both for tropical squall lines and for midlatitude squall lines.
Abstract
During the Taiwan Area Mesoscale Experiment (TAMEX), three Doppler radars complemented enhanced surface and upper-air observations. The focus of the experiment was to better understand the interaction of the terrain with precipitation systems in the production of the important heavy rainfall. The intensive operational period (IOP) number 8 extended from 1400 IST (local standard time) 7 June 1987 until 0800 LST 9 June 1987. During this time, a mesoscale convective system (MCS) formed in the Straits of Taiwan and moved inland. It was interrogated by many observing instruments, including three Doppler radars, over a 6-h period. During this time the front moved through the radar network. The front was shallow and the precipitation widespread, both ahead of and behind the front. The front was only 1.6-km deep over a distance of 100 km.
Using velocity-azimuth display (VAD) data, a portion of the frontogenetic function was computed during the times the front was in the vicinity of the radar. The increase in both convergence and deformation contributed to large values of the frontogenetic function.
Dynamic retrieval was also attempted on the data during the time when the front was most favorably located for analysis. The results are very similar to what has been observed both for tropical squall lines and for midlatitude squall lines.
Abstract
Starting times of thunderstorms for 450 stations in the conterminuos United States for a 25-year period were analyzed using harmonic analysis techniques. Diurnal variations were expressed as both the time of maximum storm occurrence and the concentration of activity around this time. Distinct seasonal and spatial variations in diurnal activity occur. Analysis of these variations indicates that the country can be divided into nine thunderstorm regions. In the central states the majority of storms occur at night, but storms are frequent at any time. In both the east and the west there is a marked concentration of storms in the afternoon. In the west and northeast winter storms are rare, while along the Pacific Coast summer thunder is uncommon.
Abstract
Starting times of thunderstorms for 450 stations in the conterminuos United States for a 25-year period were analyzed using harmonic analysis techniques. Diurnal variations were expressed as both the time of maximum storm occurrence and the concentration of activity around this time. Distinct seasonal and spatial variations in diurnal activity occur. Analysis of these variations indicates that the country can be divided into nine thunderstorm regions. In the central states the majority of storms occur at night, but storms are frequent at any time. In both the east and the west there is a marked concentration of storms in the afternoon. In the west and northeast winter storms are rare, while along the Pacific Coast summer thunder is uncommon.
Normal temperatures, which are calculated by the National Climatic Data Center for locations across the country, are quality-controlled, smoothed 30-yr-average temperatures. They are used in many facets of media, industry, and meteorology, and a given day's normal maximum and minimum temperatures are often used synonymously with what the observed temperature extremes “should be.” However, allowing some leeway to account for natural daily and seasonal variations can more accurately reflect the ranges of temperature that we can expect on a particular day—a “normal range.” Providing such a range, especially to the public, presents a more accurate perspective on what the temperature “usually” is on any particular day of the year. One way of doing this is presented in this study for several locations across North Carolina. The results yield expected higher variances in the cooler months and seem to well represent the varied weather that locations in North Carolina tend to experience. Day-to-day variations in the normal range are larger than expected, but are retained rather than smoothed. The method is simple and applicable to any location with a complete 30-yr record and with a temperature variance time series that follows a bell curve. The normal-range product has many potential applications.
Normal temperatures, which are calculated by the National Climatic Data Center for locations across the country, are quality-controlled, smoothed 30-yr-average temperatures. They are used in many facets of media, industry, and meteorology, and a given day's normal maximum and minimum temperatures are often used synonymously with what the observed temperature extremes “should be.” However, allowing some leeway to account for natural daily and seasonal variations can more accurately reflect the ranges of temperature that we can expect on a particular day—a “normal range.” Providing such a range, especially to the public, presents a more accurate perspective on what the temperature “usually” is on any particular day of the year. One way of doing this is presented in this study for several locations across North Carolina. The results yield expected higher variances in the cooler months and seem to well represent the varied weather that locations in North Carolina tend to experience. Day-to-day variations in the normal range are larger than expected, but are retained rather than smoothed. The method is simple and applicable to any location with a complete 30-yr record and with a temperature variance time series that follows a bell curve. The normal-range product has many potential applications.
Abstract
Downscaling aims to link the behavior of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high-resolution information at a lower computational and storage cost than numerical simulation alone. This is especially appealing for targeting convective scales, which are at the edge of what is possible to simulate operationally. Since convective-scale weather has a high degree of independence from larger scales, a generative approach is essential. We here propose a statistical method for downscaling moist variables to convective scales using conditional Gaussian random fields, with an application to wet bulb potential temperature (WBPT) data over the United Kingdom. Our model uses an adaptive covariance estimation to capture the variable spatial properties at convective scales. We further propose a method for the validation, which has historically been a challenge for generative models.
Abstract
Downscaling aims to link the behavior of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high-resolution information at a lower computational and storage cost than numerical simulation alone. This is especially appealing for targeting convective scales, which are at the edge of what is possible to simulate operationally. Since convective-scale weather has a high degree of independence from larger scales, a generative approach is essential. We here propose a statistical method for downscaling moist variables to convective scales using conditional Gaussian random fields, with an application to wet bulb potential temperature (WBPT) data over the United Kingdom. Our model uses an adaptive covariance estimation to capture the variable spatial properties at convective scales. We further propose a method for the validation, which has historically been a challenge for generative models.
