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- Author or Editor: Jonathan D. Kahl x
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Abstract
Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of “gustiness,” with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement over climatology) in predicting peak gusts out to projections of 72 h. This has important implications for operational wind forecasting because the method can be utilized at any location for which the meteorologically stratified gust factors have been determined. During particularly windy conditions the MSGF model exhibited skill in predicting peak gusts at forecast projections ranging from 6 to 72 h at roughly half of the sites analyzed. Site characteristics and local wind climatologies were shown to exert impacts on gust factor model performance. The MSGF method represents a viable option for the operational prediction of peak wind gusts, although model performance will be sensitive to the quality of the necessary wind speed and direction forecasts.
Abstract
Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of “gustiness,” with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement over climatology) in predicting peak gusts out to projections of 72 h. This has important implications for operational wind forecasting because the method can be utilized at any location for which the meteorologically stratified gust factors have been determined. During particularly windy conditions the MSGF model exhibited skill in predicting peak gusts at forecast projections ranging from 6 to 72 h at roughly half of the sites analyzed. Site characteristics and local wind climatologies were shown to exert impacts on gust factor model performance. The MSGF method represents a viable option for the operational prediction of peak wind gusts, although model performance will be sensitive to the quality of the necessary wind speed and direction forecasts.
Abstract
Meteorological observations conducted during the Cross Appalachian Tracer Experiment (CAPTEX) were utilized to quantify the uncertainty in boundary layer trajectory calculations due to low-resolution meteorological data [the current National Weather Service (NWS) rawinsonde network). Evaluation of several spatial and temporal interpolation techniques against high-resolution measurements revealed mean absolute errors of 2–4 m s−1 in estimation of horizontal wind components.
A trajectory of errors procedure is introduced that allows the quantification of probable errors in transport calculation due to imprecise interpolation. Our results, based on the observed distributions of spatial and temporal interpolation errors during CAPTEX, indicate dust boundary layer trajectories calculated using the current NWS network with 12 h resolution contain a 50% chance of exceeding horizontal displacement errors of 350 km after 72 h travel time. An increase in spatial resolution is shown to improve the accuracy of trajectory calculations more than an increase in temporal resolution. These results are representative of relatively undisturbed flow in the northeastern United States and southern Canada and do not include the possible effects of nonindependent trajectory errors.
Abstract
Meteorological observations conducted during the Cross Appalachian Tracer Experiment (CAPTEX) were utilized to quantify the uncertainty in boundary layer trajectory calculations due to low-resolution meteorological data [the current National Weather Service (NWS) rawinsonde network). Evaluation of several spatial and temporal interpolation techniques against high-resolution measurements revealed mean absolute errors of 2–4 m s−1 in estimation of horizontal wind components.
A trajectory of errors procedure is introduced that allows the quantification of probable errors in transport calculation due to imprecise interpolation. Our results, based on the observed distributions of spatial and temporal interpolation errors during CAPTEX, indicate dust boundary layer trajectories calculated using the current NWS network with 12 h resolution contain a 50% chance of exceeding horizontal displacement errors of 350 km after 72 h travel time. An increase in spatial resolution is shown to improve the accuracy of trajectory calculations more than an increase in temporal resolution. These results are representative of relatively undisturbed flow in the northeastern United States and southern Canada and do not include the possible effects of nonindependent trajectory errors.
Abstract
Routine and supplemental rawinsonde observations collected during the Preliminary Regional Experiment for Storm-Central (PRE-STORM) were analyzed to assess the uncertainty in boundary-layer trajectory calculations due to imprecise interpolation of the horizontal wind field. This study was designed to complement our earlier analysis of rawinsonde data collected during the Cross Appalachian Tracer Experiment (CAPTEX; Kahl and Samson 1986). The present study is representative of widespread convective conditions, while our previous study was representative of fairly persistent, undisturbed flow.
Spatial autocorrelation analysis revealed significant wind field variability on scales less than 100 km. Evaluation of several spatial and temporal interpolation techniques yielded mean absolute errors in estimation of u and v wind components ranging from 3.3–6.5 m −1. Spatial interpolation accuracy improved only slightly when supplemental measurements were included in the interpolation procedure.
Estimates of trajectory errors were obtained using the “trajectory of errors” model of Kahl and Samson (1986). Mean horizontal errors of 493 km were found after 72 h of travel. Contributions of spatial and temporal interpolation to the overall trajectory error were equivalent. Trajectory errors wore 40% greater than those estimated using CAPTEX interpolation statistics.
The presence of predominant mesoscale circulations during PRE-STORM is responsible for the elevated small-scale wind variability as compared to CAPTEX conditions, thus leading to larger interpolation errors and, in turn, larger trajectory errors. Our results suggest that data resolution finer than that considered in this study is necessary to significantly improve trajectory accuracy during meteorological conditions similar to PRE-STORM.
Abstract
Routine and supplemental rawinsonde observations collected during the Preliminary Regional Experiment for Storm-Central (PRE-STORM) were analyzed to assess the uncertainty in boundary-layer trajectory calculations due to imprecise interpolation of the horizontal wind field. This study was designed to complement our earlier analysis of rawinsonde data collected during the Cross Appalachian Tracer Experiment (CAPTEX; Kahl and Samson 1986). The present study is representative of widespread convective conditions, while our previous study was representative of fairly persistent, undisturbed flow.
Spatial autocorrelation analysis revealed significant wind field variability on scales less than 100 km. Evaluation of several spatial and temporal interpolation techniques yielded mean absolute errors in estimation of u and v wind components ranging from 3.3–6.5 m −1. Spatial interpolation accuracy improved only slightly when supplemental measurements were included in the interpolation procedure.
Estimates of trajectory errors were obtained using the “trajectory of errors” model of Kahl and Samson (1986). Mean horizontal errors of 493 km were found after 72 h of travel. Contributions of spatial and temporal interpolation to the overall trajectory error were equivalent. Trajectory errors wore 40% greater than those estimated using CAPTEX interpolation statistics.
The presence of predominant mesoscale circulations during PRE-STORM is responsible for the elevated small-scale wind variability as compared to CAPTEX conditions, thus leading to larger interpolation errors and, in turn, larger trajectory errors. Our results suggest that data resolution finer than that considered in this study is necessary to significantly improve trajectory accuracy during meteorological conditions similar to PRE-STORM.
Abstract
The dependence of wind interpolation accuracy on vertical shear was investigated using routine and supplemental rawinsonde data collected during the Cross Appalachian Tracer Experiment. Spatial interpolation error distributions for horizontal wind components were stratified by the “bulk” shear within the mixed layer. Results indicated that interpolation errors were approximately proportional to vertical wind shear.
Abstract
The dependence of wind interpolation accuracy on vertical shear was investigated using routine and supplemental rawinsonde data collected during the Cross Appalachian Tracer Experiment. Spatial interpolation error distributions for horizontal wind components were stratified by the “bulk” shear within the mixed layer. Results indicated that interpolation errors were approximately proportional to vertical wind shear.
The Atmospheric Science program at the University of Wisconsin—Milwaukee regularly offers the general education course Survey of Meteorology, serving over 400 students each year. This article describes a systematic inquiry into the teaching and learning goals of the course and the adequacy of current methods used to assess student performance. Following a survey of the six faculty members with teaching responsibilities for the course, common student learning goals of meteorological content and the application of meteorological concepts to observations were identified. Student surveys, designed to assess both the extent to which these learning goals were being met as well as the depth of learning, were administered to 241 students during the 2005–06 academic year. Results indicate that 80% of students surveyed met the content learning goal, while the application learning goal was met by only 66% of students. A deeper level of application learning, involving pattern recognition and the separation of concepts into component parts, was achieved by only 45% of the students. A comparison of student survey results with course grade distributions indicates that current grading practices are adequate for assessing the content learning goal but are inadequate for assessing the application learning goal.
The Atmospheric Science program at the University of Wisconsin—Milwaukee regularly offers the general education course Survey of Meteorology, serving over 400 students each year. This article describes a systematic inquiry into the teaching and learning goals of the course and the adequacy of current methods used to assess student performance. Following a survey of the six faculty members with teaching responsibilities for the course, common student learning goals of meteorological content and the application of meteorological concepts to observations were identified. Student surveys, designed to assess both the extent to which these learning goals were being met as well as the depth of learning, were administered to 241 students during the 2005–06 academic year. Results indicate that 80% of students surveyed met the content learning goal, while the application learning goal was met by only 66% of students. A deeper level of application learning, involving pattern recognition and the separation of concepts into component parts, was achieved by only 45% of the students. A comparison of student survey results with course grade distributions indicates that current grading practices are adequate for assessing the content learning goal but are inadequate for assessing the application learning goal.
Abstract
Adapted from the sports concept of scorigami, the weathergami chart is introduced. Weathergami charts depict the frequency of occurrence of the full range of daily maximum and minimum temperature combinations observed at a location. These charts highlight essential features of climate not evident in traditional representations. A variation of the weathergami chart displays transition frequencies, which describe the likelihood of particular day-to-day changes in maximum and minimum temperatures. Likewise, weathergami anomaly charts reveal characteristics of changing climate not evident in standard time series representations. Several examples are provided, with comparisons to climate descriptions found in popular textbooks.
Abstract
Adapted from the sports concept of scorigami, the weathergami chart is introduced. Weathergami charts depict the frequency of occurrence of the full range of daily maximum and minimum temperature combinations observed at a location. These charts highlight essential features of climate not evident in traditional representations. A variation of the weathergami chart displays transition frequencies, which describe the likelihood of particular day-to-day changes in maximum and minimum temperatures. Likewise, weathergami anomaly charts reveal characteristics of changing climate not evident in standard time series representations. Several examples are provided, with comparisons to climate descriptions found in popular textbooks.
Abstract
Gust factors in Milwaukee, Wisconsin, are investigated using Automated Surface Observing System (ASOS) wind measurements from 2007 to 2014. Wind and gust observations reported in the standard hourly ASOS dataset are shown to contain substantial bias caused by sampling and reporting protocols that restrict the reporting of gusts to arbitrarily defined “gusty” periods occurring during small subsets of each hour. The hourly ASOS gust reports are found to be inadequate for describing the gust characteristics of the site and ill suited for the study of gust factors. A gust-factor climatology was established for Milwaukee using the higher-resolution, 1-min version of the ASOS dataset. The mean gust factor is 1.74. Stratified climatologies demonstrate that Milwaukee gust factors vary substantially with meteorological factors, with wind speed and wind direction exerting the strongest controls. A variety of modified gust-factor models were evaluated in which the peak wind gust is estimated by multiplying a gust factor by the observed, rather than forecast, wind speed. Errors thus obtained are entirely attributable to utility of the gust factor in forecasting peak gusts, having eliminated any error associated with the wind speed forecast. Results show that gust-factor models demonstrate skill in estimating peak gusts and improve with the use of meteorologically stratified gust factors.
Abstract
Gust factors in Milwaukee, Wisconsin, are investigated using Automated Surface Observing System (ASOS) wind measurements from 2007 to 2014. Wind and gust observations reported in the standard hourly ASOS dataset are shown to contain substantial bias caused by sampling and reporting protocols that restrict the reporting of gusts to arbitrarily defined “gusty” periods occurring during small subsets of each hour. The hourly ASOS gust reports are found to be inadequate for describing the gust characteristics of the site and ill suited for the study of gust factors. A gust-factor climatology was established for Milwaukee using the higher-resolution, 1-min version of the ASOS dataset. The mean gust factor is 1.74. Stratified climatologies demonstrate that Milwaukee gust factors vary substantially with meteorological factors, with wind speed and wind direction exerting the strongest controls. A variety of modified gust-factor models were evaluated in which the peak wind gust is estimated by multiplying a gust factor by the observed, rather than forecast, wind speed. Errors thus obtained are entirely attributable to utility of the gust factor in forecasting peak gusts, having eliminated any error associated with the wind speed forecast. Results show that gust-factor models demonstrate skill in estimating peak gusts and improve with the use of meteorologically stratified gust factors.
For several years the University of Wisconsin–Milwaukee's Atmospheric Science group has offered the faculty-led study abroad program Mexico: Air Pollution and Ancient Cultures. In this course, open to both atmospheric science majors and nonmajors as well as to students attending other colleges and universities, participating students learn about the corrosive effects of acid deposition on the limestone surfaces of Mesoamerican archaeological sites. The course content includes not only the science aspects of acid rain and environmental corrosion, but also aspects of Mesoamerican history and anthropology, as well as personal reflection on a variety of social science topics via journaling. The academic content is delivered via lectures and laboratories, guided tours of museums and archaeological sites, visits to Mexican universities, and hands-on measurements and analysis. Postprogram surveys indicate that participating students consider the program to be quite valuable in terms of both academic and personal growth.
For several years the University of Wisconsin–Milwaukee's Atmospheric Science group has offered the faculty-led study abroad program Mexico: Air Pollution and Ancient Cultures. In this course, open to both atmospheric science majors and nonmajors as well as to students attending other colleges and universities, participating students learn about the corrosive effects of acid deposition on the limestone surfaces of Mesoamerican archaeological sites. The course content includes not only the science aspects of acid rain and environmental corrosion, but also aspects of Mesoamerican history and anthropology, as well as personal reflection on a variety of social science topics via journaling. The academic content is delivered via lectures and laboratories, guided tours of museums and archaeological sites, visits to Mexican universities, and hands-on measurements and analysis. Postprogram surveys indicate that participating students consider the program to be quite valuable in terms of both academic and personal growth.
ABSTRACT
Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.
ABSTRACT
Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.
