Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: Kiel L. Ortega x
- Refine by Access: All Content x
Abstract
This study is the third part of a paper series investigating the polarimetric radar properties of melting hail and application of those properties for operational polarimetric hail detection and determination of its size. The results of theoretical simulations in Part I were used to develop a hail size discrimination algorithm (HSDA) described in Part II. The HSDA uses radar reflectivity Z, differential reflectivity Z DR, and cross-correlation coefficient ρhv along with melting-level height within a fuzzy-logic scheme to distinguish among three hail size classes: small hail (with diameter D < 2.5 cm), large hail (2.5 < D < 5.0 cm), and giant hail (D > 5.0 cm). The HSDA validation is performed using radar data collected by numerous WSR-88D sites and more than 3000 surface hail reports obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE). The original HSDA version was modified in the process of validation, and the modified algorithm demonstrates probability of detection of 0.594, false-alarm ratio of 0.136, and resulting critical success index (CSI) equal to 0.543. The HSDA outperformed the current operational single-polarization hail detection algorithm, which only provides a single hail size estimate per storm and is characterized by CSI equal to 0.324. It is shown that HSDA is particularly sensitive to the quality of Z DR measurements, which might be affected by possible radar miscalibration and anomalously high differential attenuation.
Abstract
This study is the third part of a paper series investigating the polarimetric radar properties of melting hail and application of those properties for operational polarimetric hail detection and determination of its size. The results of theoretical simulations in Part I were used to develop a hail size discrimination algorithm (HSDA) described in Part II. The HSDA uses radar reflectivity Z, differential reflectivity Z DR, and cross-correlation coefficient ρhv along with melting-level height within a fuzzy-logic scheme to distinguish among three hail size classes: small hail (with diameter D < 2.5 cm), large hail (2.5 < D < 5.0 cm), and giant hail (D > 5.0 cm). The HSDA validation is performed using radar data collected by numerous WSR-88D sites and more than 3000 surface hail reports obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE). The original HSDA version was modified in the process of validation, and the modified algorithm demonstrates probability of detection of 0.594, false-alarm ratio of 0.136, and resulting critical success index (CSI) equal to 0.543. The HSDA outperformed the current operational single-polarization hail detection algorithm, which only provides a single hail size estimate per storm and is characterized by CSI equal to 0.324. It is shown that HSDA is particularly sensitive to the quality of Z DR measurements, which might be affected by possible radar miscalibration and anomalously high differential attenuation.
Abstract
The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.
Abstract
The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.
Abstract
The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) dataset blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS dataset uses the WSR-88D archive starting in 1998–2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1 km × 1 km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe-storm-related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5 km × 0.5 km. The initial period of record for the dataset is 1998–2011. The dataset underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall dataset, but which caused large data errors in some cases. This dataset has applications toward radar-based climatologies, postevent analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.
Abstract
The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) dataset blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS dataset uses the WSR-88D archive starting in 1998–2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1 km × 1 km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe-storm-related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5 km × 0.5 km. The initial period of record for the dataset is 1998–2011. The dataset underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall dataset, but which caused large data errors in some cases. This dataset has applications toward radar-based climatologies, postevent analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.
Abstract
The local, linear, least squares derivative (LLSD) approach to radar analysis is a method of quantifying gradients in radar data by fitting a least squares plane to a neighborhood of range bins and finding its slope. When applied to radial velocity fields, for example, LLSD yields part of the azimuthal (rotational) and radial (divergent) components of horizontal shear, which, under certain geometric assumptions, estimate one-half of the two-dimensional vertical vorticity and horizontal divergence equations, respectively. Recent advances in computational capacity as well as increased usage of LLSD products by the meteorological community have motivated an overhaul of the LLSD methodology’s application to radar data. This paper documents the mathematical foundation of the updated LLSD approach, including a complete derivation of its equation set, discussion of its limitations, and considerations for other types of implementation. In addition, updated azimuthal shear calculations are validated against theoretical vorticity using simulated circulations. Applications to nontraditional radar data and new applications to nonvelocity radar data including reflectivity at horizontal polarization, spectrum width, and polarimetric moments are also explored. These LLSD gradient calculations may be leveraged to identify and interrogate a wide variety of severe weather phenomena, either directly by operational forecasters or indirectly as part of future automated algorithms.
Abstract
The local, linear, least squares derivative (LLSD) approach to radar analysis is a method of quantifying gradients in radar data by fitting a least squares plane to a neighborhood of range bins and finding its slope. When applied to radial velocity fields, for example, LLSD yields part of the azimuthal (rotational) and radial (divergent) components of horizontal shear, which, under certain geometric assumptions, estimate one-half of the two-dimensional vertical vorticity and horizontal divergence equations, respectively. Recent advances in computational capacity as well as increased usage of LLSD products by the meteorological community have motivated an overhaul of the LLSD methodology’s application to radar data. This paper documents the mathematical foundation of the updated LLSD approach, including a complete derivation of its equation set, discussion of its limitations, and considerations for other types of implementation. In addition, updated azimuthal shear calculations are validated against theoretical vorticity using simulated circulations. Applications to nontraditional radar data and new applications to nonvelocity radar data including reflectivity at horizontal polarization, spectrum width, and polarimetric moments are also explored. These LLSD gradient calculations may be leveraged to identify and interrogate a wide variety of severe weather phenomena, either directly by operational forecasters or indirectly as part of future automated algorithms.
During the springs and summers of 2006 through 2008, scientists from the National Severe Storms Laboratory and students from the University of Oklahoma have conducted an enhanced severe-storm verification effort. The primary goal for the Severe Hazards Analysis and Verification Experiment (SHAVE) was the remote collection of high spatial and temporal resolution hail, wind (or wind damage), and flash-flooding reports from severe thunderstorms. This dataset has a much higher temporal and spatial resolution than the traditional storm reports collected by the National Weather Service and published in Storm Data (tens of square kilometers and 1–5 min versus thousands of square kilometers and 30–60 min) and also includes reports of nonsevere storms that are not included in Storm Data. The high resolution of the dataset makes it useful for validating high-resolution, gridded warning guidance applications.
SHAVE is unique not only for the type of data collected and the resolution of that data but also for how the data are collected. The daily operations of the project are largely student led and run. To complete the remote, high-resolution verification, the students use Google Earth to display experimental weather data and geographic information databases, such as digital phonebooks. Using these data, the students then make verification phone calls to residences and businesses, throughout the United States, thought to have been affected by a severe thunderstorm. The present article summarizes the data collection facilities and techniques, discusses applications of these data, and shows comparisons of SHAVE reports to reports currently available from Storm Data.
During the springs and summers of 2006 through 2008, scientists from the National Severe Storms Laboratory and students from the University of Oklahoma have conducted an enhanced severe-storm verification effort. The primary goal for the Severe Hazards Analysis and Verification Experiment (SHAVE) was the remote collection of high spatial and temporal resolution hail, wind (or wind damage), and flash-flooding reports from severe thunderstorms. This dataset has a much higher temporal and spatial resolution than the traditional storm reports collected by the National Weather Service and published in Storm Data (tens of square kilometers and 1–5 min versus thousands of square kilometers and 30–60 min) and also includes reports of nonsevere storms that are not included in Storm Data. The high resolution of the dataset makes it useful for validating high-resolution, gridded warning guidance applications.
SHAVE is unique not only for the type of data collected and the resolution of that data but also for how the data are collected. The daily operations of the project are largely student led and run. To complete the remote, high-resolution verification, the students use Google Earth to display experimental weather data and geographic information databases, such as digital phonebooks. Using these data, the students then make verification phone calls to residences and businesses, throughout the United States, thought to have been affected by a severe thunderstorm. The present article summarizes the data collection facilities and techniques, discusses applications of these data, and shows comparisons of SHAVE reports to reports currently available from Storm Data.
Abstract
The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.
Abstract
The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.
Abstract
Forecasters and research meteorologists tested a real-time three-dimensional variational data assimilation (3DVAR) system in the Hazardous Weather Testbed during the springs of 2010–12 to determine its capabilities to assist in the warning process for severe storms. This storm-scale system updates a dynamically consistent three-dimensional wind field every 5 min, with horizontal and average vertical grid spacings of 1 km and 400 m, respectively. The system analyzed the life cycles of 218 supercell thunderstorms on 27 event days during these experiments, producing multiple products such as vertical velocity, vertical vorticity, and updraft helicity. These data are compared to multiradar–multisensor data from the Warning Decision Support System–Integrated Information to document the performance characteristics of the system, such as how vertical vorticity values compare to azimuthal shear fields calculated directly from Doppler radial velocity. Data are stratified by range from the nearest radar, as well as by the number of radars entering into the analysis of a particular storm. The 3DVAR system shows physically realistic trends of updraft speed and vertical vorticity for a majority of cases. Improvements are needed to better estimate the near-surface winds when no radar is nearby and to improve the timeliness of the input data. However, the 3DVAR wind field information provides an integrated look at storm structure that may be of more use to forecasters than traditional radar-based proxies used to infer severe weather potential.
Abstract
Forecasters and research meteorologists tested a real-time three-dimensional variational data assimilation (3DVAR) system in the Hazardous Weather Testbed during the springs of 2010–12 to determine its capabilities to assist in the warning process for severe storms. This storm-scale system updates a dynamically consistent three-dimensional wind field every 5 min, with horizontal and average vertical grid spacings of 1 km and 400 m, respectively. The system analyzed the life cycles of 218 supercell thunderstorms on 27 event days during these experiments, producing multiple products such as vertical velocity, vertical vorticity, and updraft helicity. These data are compared to multiradar–multisensor data from the Warning Decision Support System–Integrated Information to document the performance characteristics of the system, such as how vertical vorticity values compare to azimuthal shear fields calculated directly from Doppler radial velocity. Data are stratified by range from the nearest radar, as well as by the number of radars entering into the analysis of a particular storm. The 3DVAR system shows physically realistic trends of updraft speed and vertical vorticity for a majority of cases. Improvements are needed to better estimate the near-surface winds when no radar is nearby and to improve the timeliness of the input data. However, the 3DVAR wind field information provides an integrated look at storm structure that may be of more use to forecasters than traditional radar-based proxies used to infer severe weather potential.
