Search Results
You are looking at 1 - 7 of 7 items for :
- Author or Editor: Cameron R. Homeyer x
- Journal of Applied Meteorology and Climatology x
- Refine by Access: All Content x
Abstract
Forecasting tornadogenesis remains a difficult problem in meteorology, especially for short-lived, predominantly nonsupercellular tornadic storms embedded within mesoscale convective systems (MCSs). This study compares populations of tornadic nonsupercellular MCS storm cells with their nontornadic counterparts, focusing on nontornadic storms that have similar radar characteristics to tornadic storms. Comparisons of single-polarization radar variables during storm lifetimes show that median values of low-level, midlevel, and column-maximum azimuthal shear, as well as low-level radial divergence, enable the highest degree of separation between tornadic and nontornadic storms. Focusing on low-level azimuthal shear values, null storms were randomly selected such that the distribution of null low-level azimuthal shear values matched the distribution of tornadic values. After isolating the null cases from the nontornadic population, signatures emerge in single-polarization data that enable discrimination between nontornadic and tornadic storms. In comparison, dual-polarization variables show little deviation between storm types. Tornadic storms both at tornadogenesis and at a 20-min lead time show collocation of the primary storm updraft with enhanced near-surface rotation and convergence, facilitating the nonmesocyclonic tornadogenesis processes.
Abstract
Forecasting tornadogenesis remains a difficult problem in meteorology, especially for short-lived, predominantly nonsupercellular tornadic storms embedded within mesoscale convective systems (MCSs). This study compares populations of tornadic nonsupercellular MCS storm cells with their nontornadic counterparts, focusing on nontornadic storms that have similar radar characteristics to tornadic storms. Comparisons of single-polarization radar variables during storm lifetimes show that median values of low-level, midlevel, and column-maximum azimuthal shear, as well as low-level radial divergence, enable the highest degree of separation between tornadic and nontornadic storms. Focusing on low-level azimuthal shear values, null storms were randomly selected such that the distribution of null low-level azimuthal shear values matched the distribution of tornadic values. After isolating the null cases from the nontornadic population, signatures emerge in single-polarization data that enable discrimination between nontornadic and tornadic storms. In comparison, dual-polarization variables show little deviation between storm types. Tornadic storms both at tornadogenesis and at a 20-min lead time show collocation of the primary storm updraft with enhanced near-surface rotation and convergence, facilitating the nonmesocyclonic tornadogenesis processes.
Abstract
Severe hail days account for the vast majority of severe weather–induced property losses in the United States each year. In the United States, real-time detection of severe storms is largely conducted using ground-based radar observations, mostly using the operational Next Generation Weather Radar network (NEXRAD), which provides three-dimensional information on the physics and dynamics of storms at ~5-min time intervals. Recent NEXRAD upgrades to higher resolution and to dual-polarization capabilities have provided improved hydrometeor discrimination in real time. New geostationary satellite platforms have also led to significant changes in observing capabilities over the United States beginning in 2016, with spatiotemporal resolution that is comparable to that of NEXRAD. Given these recent improvements, a thorough assessment of their ability to identify hailstorms and hail occurrence and to discriminate between hail sizes is needed. This study provides a comprehensive comparative analysis of existing observational radar and satellite products from more than 10 000 storms objectively identified via radar echo-top tracking and nearly 6000 hail reports during 30 recent severe weather days (2013–present). It is found that radar observations provide the most skillful discrimination between severe and nonsevere hailstorms and identification of individual hail occurrence. Single-polarization and dual-polarization radar observations perform similarly at these tasks, with the greatest skill found from combining both single- and dual-polarization metrics. In addition, revisions to the “maximum expected size of hail” (MESH) metric are proposed and are shown to improve spatiotemporal comparisons between reported hail sizes and radar-based estimates for the cases studied.
Abstract
Severe hail days account for the vast majority of severe weather–induced property losses in the United States each year. In the United States, real-time detection of severe storms is largely conducted using ground-based radar observations, mostly using the operational Next Generation Weather Radar network (NEXRAD), which provides three-dimensional information on the physics and dynamics of storms at ~5-min time intervals. Recent NEXRAD upgrades to higher resolution and to dual-polarization capabilities have provided improved hydrometeor discrimination in real time. New geostationary satellite platforms have also led to significant changes in observing capabilities over the United States beginning in 2016, with spatiotemporal resolution that is comparable to that of NEXRAD. Given these recent improvements, a thorough assessment of their ability to identify hailstorms and hail occurrence and to discriminate between hail sizes is needed. This study provides a comprehensive comparative analysis of existing observational radar and satellite products from more than 10 000 storms objectively identified via radar echo-top tracking and nearly 6000 hail reports during 30 recent severe weather days (2013–present). It is found that radar observations provide the most skillful discrimination between severe and nonsevere hailstorms and identification of individual hail occurrence. Single-polarization and dual-polarization radar observations perform similarly at these tasks, with the greatest skill found from combining both single- and dual-polarization metrics. In addition, revisions to the “maximum expected size of hail” (MESH) metric are proposed and are shown to improve spatiotemporal comparisons between reported hail sizes and radar-based estimates for the cases studied.
Abstract
In 2013, all NEXRAD WSR-88D units in the United States were upgraded to dual polarization. Dual polarization allows for the identification of precipitation particle shape, size, orientation, and concentration. In this study, dual-polarization NEXRAD observations from 34 recent events are used to identify the bulk microphysical characteristics of a specific subset of mesoscale convective systems (MCSs), the leading-line trailing-stratiform (LLTS) MCS. NEXRAD observations are used to examine hydrometeor distributions in relative altitude to the 0°C level and as a function of storm life cycle, precipitation source (convective or stratiform), and storm environment. The analysis reveals that graupel particles are the most frequently classified hydrometeor class in a layer extending from the 0°C-level altitude to approximately 5 km above within the convective region. Below the 0°C level, rain is the most frequently classified hydrometeor, with small hail and graupel concentrations present throughout the LLTS system’s life cycle. The stratiform precipitation region contains small graupel concentrations in a shallow layer above the 0°C level, with pristine ice crystals being classified as the most frequently observed hydrometeor at higher altitudes and snow aggregates being classified as the most frequently observed hydrometeor at lower altitudes above the environmental 0°C level. Variations in most unstable convective available potential energy (MUCAPE) have the largest impact on the vertical distribution of hydrometeors, because more-unstable environments are characterized by a greater production of rimed ice.
Abstract
In 2013, all NEXRAD WSR-88D units in the United States were upgraded to dual polarization. Dual polarization allows for the identification of precipitation particle shape, size, orientation, and concentration. In this study, dual-polarization NEXRAD observations from 34 recent events are used to identify the bulk microphysical characteristics of a specific subset of mesoscale convective systems (MCSs), the leading-line trailing-stratiform (LLTS) MCS. NEXRAD observations are used to examine hydrometeor distributions in relative altitude to the 0°C level and as a function of storm life cycle, precipitation source (convective or stratiform), and storm environment. The analysis reveals that graupel particles are the most frequently classified hydrometeor class in a layer extending from the 0°C-level altitude to approximately 5 km above within the convective region. Below the 0°C level, rain is the most frequently classified hydrometeor, with small hail and graupel concentrations present throughout the LLTS system’s life cycle. The stratiform precipitation region contains small graupel concentrations in a shallow layer above the 0°C level, with pristine ice crystals being classified as the most frequently observed hydrometeor at higher altitudes and snow aggregates being classified as the most frequently observed hydrometeor at lower altitudes above the environmental 0°C level. Variations in most unstable convective available potential energy (MUCAPE) have the largest impact on the vertical distribution of hydrometeors, because more-unstable environments are characterized by a greater production of rimed ice.
Abstract
A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.
Abstract
A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.
Abstract
A new method that combines radar reflectivities from individual Next Generation Weather Radars (NEXRAD) into a three-dimensional composite with high horizontal and vertical resolution is used to estimate storm-top altitudes for the continental United States east of the Rocky Mountains. Echo-top altitudes are compared with the altitude of the lapse-rate tropopause calculated from the ERA-Interim reanalysis and radiosondes. To sample the diurnal and annual cycles, tropopause-penetrating convection is analyzed at 3-h intervals throughout 2004. Overshooting convection is most common in the north-central part of the United States (the high plains). There is a pronounced seasonal cycle; the majority of overshooting systems occur during the warm season (March–August). There is also a strong diurnal cycle, with maximum overshooting occurring near 0000 UTC. The overshooting volume decreases rapidly with height above the tropopause. Radiosonde observations are used to evaluate the quality of the reanalysis tropopause altitudes and the dependence of overshooting depth on environmental characteristics. The radar–radiosonde comparison reveals that overshooting is deeper in double-tropopause environments and increases as the stability of the lower stratosphere decreases.
Abstract
A new method that combines radar reflectivities from individual Next Generation Weather Radars (NEXRAD) into a three-dimensional composite with high horizontal and vertical resolution is used to estimate storm-top altitudes for the continental United States east of the Rocky Mountains. Echo-top altitudes are compared with the altitude of the lapse-rate tropopause calculated from the ERA-Interim reanalysis and radiosondes. To sample the diurnal and annual cycles, tropopause-penetrating convection is analyzed at 3-h intervals throughout 2004. Overshooting convection is most common in the north-central part of the United States (the high plains). There is a pronounced seasonal cycle; the majority of overshooting systems occur during the warm season (March–August). There is also a strong diurnal cycle, with maximum overshooting occurring near 0000 UTC. The overshooting volume decreases rapidly with height above the tropopause. Radiosonde observations are used to evaluate the quality of the reanalysis tropopause altitudes and the dependence of overshooting depth on environmental characteristics. The radar–radiosonde comparison reveals that overshooting is deeper in double-tropopause environments and increases as the stability of the lower stratosphere decreases.
Abstract
Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s−1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.
Abstract
Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s−1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.
